Learning through experience is essential for high-level autonomous driving systems, as it has the potential to enhance driving performance in corner cases. However, current decision and control modules adopt an empirical design paradigm for engineering efficiency, relying heavily on expert rules or real-vehicle data, failing to fully cover and optimize edge scenarios. To address this gap, we propose an enhanced integrated decision and control method that leverages reinforcement learning as the optimal control problem solver, endowing high-level automated vehicles with experience data usage. Specifically, a constrained mixed policy gradient algorithm is developed, which combines and dynamically adjusts the application ratio of experience data and the environmental model during training. This approach achieves fast convergence while maintaining high performance even with inaccurate analytic models. Furthermore, an attention based encoding network is designed to accommodate diverse driving states in urban traffic, integrating an embedding network for feature extraction and a weighting network for feature fusion, realizing order-insensitive encoding and importance differentiation of road users. The trained policy is deployed on a fully functional autonomous vehicle. Experiments at a signalized intersection show that the proposed method can accurately identify critical surrounding obstacles and execute safe, efficient, and intelligent driving behaviors across 32 scenarios.

This article investigates the privacy-preserving fusion filtering problem for vehicle localization subject to unknown inputs. High-accuracy and privacy-preserving localization is essential for the safe and reliable operation of intelligent transportation systems. In practice, vehicle localization is often challenged by measurement bias caused by nonline-of-sight (NLOS) propagation, unknown inputs arising from uncertainties or acceleration/deceleration maneuvers, and risks of signal and location privacy leakage. To address these issues, a privacy-preserving fusion filtering framework is proposed by integrating the reconfigurable intelligent surface (RIS) technique, an unknown-input estimation method, and an output-mask mechanism. A unified measurement model is first developed to represent both line-of-sight (LOS) and NLOS scenarios, and RISs are employed to construct virtual LOS paths to mitigate NLOS effects. An output-mask-based privacy-preserving strategy is then designed to prevent eavesdroppers from inferring vehicle locations or signal characteristics while maintaining the required filtering performance. Based on this model, an unknown-input estimator and a privacy-preserving filter are constructed, and the impacts of NLOS propagation, unknown inputs, and masking on filtering performance are analyzed. The associated gain matrix parameters are obtained by solving the corresponding optimization problems. Furthermore, an RIS-assisted privacy-preserving fusion filtering algorithm is developed to exploit multisource measurements and enhance localization robustness and accuracy. Simulation results demonstrate the effectiveness of the proposed method.

Trajectory planning is crucial for ensuring safety, efficiency, and comfort in autonomous driving, particularly in highway environments, which are critical components of intelligent transportation systems and require vehicles to navigate complex and uncertain traffic conditions. However, related methods are lack of adaptive decision-making capabilities and suffer from high algorithmic complexity. To this end, this paper proposes a novel trajectory planning framework based on deep reinforcement learning and spatio-temporal planning, aiming to improve the adaptability and overall performance of autonomous driving systems in complex traffic environments. First, a data-driven decision-making method based on deep reinforcement learning is developed to establish a stable and human-like decision system. At the planning level, a variable voxel structure is designed to handle different decisions and scenarios, with the spatio-temporal feasible region constructed based on vehicle dynamics model and transportation safety regulations. Trajectory planning is then carried out using piecewise Bézier curves, incorporating various constraints. Finally, a new decision detection module is developed to ensure decision feasibility, while enhancing the self-learning ability of the DRL agent and fully leveraging the performance of the planning layer. Our proposed trajectory planning method is established based on forward decision guidance and backward optimization feedback. Experiments conducted on the highway-env simulator and the CQSkyEyeX real-world dataset show that the proposed framework outperforms the compared reinforcement learning and spatio-temporal planning methods in terms of decision-making efficiency, safety, and human-like planning.

A major obstacle to the rapid maturation and real-world deployment of cooperative connected and automated vehicles (CAVs) is the prohibitive cost and extensive on-road testing mileage required to validate safety in natural traffic, where genuinely high-risk scenarios are exceedingly rare. Although existing scenario-generation methods can generate high-risk scenarios at scale, such scenarios frequently violate real-world physics or driver-behavior patterns, making them implausible and unsuitable for rigorous evaluation. To bridge this critical gap, we propose a plausible high-risk scenario generation method utilizing a bidirectional and autoregressive transformer (BART). Continuous vehicle trajectories from extensive naturalistic datasets are tokenized into a concise behavioral vocabulary, enabling the model to capture latent plausibility structures and realistically reproduce vehicle maneuvers. An iterative risk-feedback mechanism further steers scenario generation toward aggressive yet physically plausible driving conditions, effectively escalating cumulative risk within each simulation and thus yielding more plausible high-risk scenarios. Across cooperative lane-change and merging verification, the proposed BART-driven plausible high-risk generator yields markedly more high-risk and informative test scenarios than the Markov Decision Process (MDP) baseline, an Adaptive Stress Testing with a Deep Q-Network (AST-DQN), and a BART variant without risk-guided decoding, while maintaining a practical balance between scenario plausibility and risk elevation.

Highway interchanges are critical nodes in transportation networks but frequently experience congestion due to complex interactions between ramp diverging and merging flows. While Connected and Automated Vehicles (CAVs) are expected to improve traffic efficiency, the coexistence of Human-Driven Vehicles (HDVs) introduces disturbances that undermine performance. Prior studies have addressed certain cooperative strategies of CAVs in mixed traffic, but they often overlook the interactions between adjacent bottlenecks in interchange scenarios and the heterogeneity of HDV driving styles. To address this gap, this study proposes an Idealization-Oriented Planning (IOP) scheme that enhances interchange traffic efficiency in mixed traffic with high CAV penetration. Enabled by Cloud Control Systems (CCS), an idealized optimum for the overall travel efficiency under the assumption of full CAV penetration is firstly derived as a reference, based on which short-horizon strategies are computed to mitigate disturbances caused by HDV behaviors. Specifically, a DeePC-based Lane-Change Hesitation Guidance mechanism identifies conservative HDVs in advance and adjusts lane-change gaps to prevent excessive deceleration. In parallel, an Aggressive Merging Avoidance mechanism formulates a potential game in which adjacent CAVs cooperate to constrain inefficient HDV cut-ins, yielding safe and system-efficient strategies. A Python-based simulation platform validates the proposed approach, showing that IOP outperforms benchmark methods across various traffic conditions and HDV penetration rates (5%–30%); as an illustrative case, under high-flow conditions with 15% HDV penetration, IOP achieves a delay reduction of up to 53%.

Controlling mixed platoons, which consist of both connected and automated vehicles (CAVs) and human-driven vehicles (HDVs), poses significant challenges due to the uncertain and unknown human driving behaviors. Data-driven control methods offer promising solutions by leveraging available trajectory data, but their performance can be compromised by noise and attacks. To address this issue, this paper proposes a Robust Data-EnablEd Predictive Leading Cruise Control (RDeeP-LCC) framework based on data-driven reachability analysis. The framework over-approximates system dynamics under noise and attack using a matrix zonotope set derived from data, and develops a stabilizing feedback control law. By decoupling the mixed platoon system into nominal and error components, we employ data-driven reachability sets to recursively compute error reachable sets that account for noise and attacks, and obtain tightened safety constraints of the nominal system. This leads to a robust data-driven predictive control framework, solved in a tube-based control manner. Human-in-the-loop experiments demonstrate that theRDeeP-LCCmethod significantly improves robustness against noise and attacks, while enhancing tracking accuracy, control efficiency, energy economy, driving comfort, and driving safety.

4D millimeter-wave radar, as a fundamental sensor for 3D object detection, has gained increasing attention in autonomous driving due to its robustness and additional elevation information. However, the sparsity and noisiness of 4D radar point clouds hinder its broader application. Fusing camera with 4D millimeter-wave radar provides an affordable and robust solution. In this paper, a fast and effective framework HP-C4D for camera and 4D radar fusion is proposed. Three key modules are proposed in HP-C4D. Firstly, we innovatively propose the Height BEVPool to predict the height of each BEV grid with negligible delay increase during the height compression. The predicted height information is incorporated into the final object height prediction. Secondly, Perceptive View Heatmap Guidance (PVHG) is proposed to suppress background noise's generation in image BEV and assist the Height BEVPool for better height prediction. Thirdly, Interactive Guidance Fusion (IGF) is proposed to efficiently fuse image BEV and 4D radar BEV. Extensive experiments on the View-of-Delft (VoD) and TJ4DRadset datasets demonstrate the effectiveness and advance of our proposed method. It is worth highlighting that the 3D mean average precision of our proposed method is 0.71% and 3.85% higher than the latest baseline method LXL (LiDAR-Excluded-Lean) on the VoD and TJ4DRadset datasets, respectively. To the best of our knowledge, HP-C4D is the fastest method for camera and 4D radar fusion in 3D object detection on the VoD dataset, achieving 21.4 FPS on single NVIDIA RTX 3090 GPU. Code will be released at https://github.com/c-yyyy/HP-C4D.

End-to-end autonomous driving offers a stream-lined alternative to the traditional modular pipeline, integrating perception, prediction, and planning within a single framework. While Deep Reinforcement Learning (DRL) has recently gained traction in this domain, existing approaches often overlook the critical connection between feature extraction of DRL and perception. In this paper, we bridge this gap by mapping the DRL feature extraction network directly to the perception phase, en-abling clearer interpretation through semantic segmentation. By leveraging Bird's-Eye- View (BEV) representations, we propose a novel DRL-based end-to-end driving framework that utilizes multi-sensor inputs to construct a unified three-dimensional understanding of the environment. This BEV-based system extracts and translates critical environmental features into high-level abstract states for DRL, facilitating more informed control. Extensive experimental evaluations demonstrate that our approach not only enhances interpretability but also significantly outperforms state-of-the-art methods in autonomous driving control tasks, reducing the collision rate by 20 %.

End-to-end models are emerging as the mainstream in autonomous driving perception. However, the inability to meticulously deconstruct their internal mechanisms results in diminished development efficacy and impedes the establishment of trust. Pioneering in the issue, we present the Independent Functional Module Evaluation for Bird's-EyeView Perception Model (BEV-IFME), a novel framework that juxtaposes the module's feature maps against Ground Truth within a unified semantic Representation Space to quantify their similarity, thereby assessing the training maturity of individual functional modules. The core of the framework lies in the process of feature map encoding and representation aligning, facilitated by our proposed two-stage Alignment AutoEncoder, which ensures the preservation of salient information and the consistency of feature structure. The metric for evaluating the training maturity of functional modules, Similarity Score, demonstrates a robust positive correlation with BEV metrics, with an average correlation coefficient of 0.9387, attesting to the framework's reliability for assessment purposes.

Visual bird’s eye view (BEV) perception, dute to its excellent perceptual capabilities, is progressively replacing costly LiDAR-based perception systems, especially in the realm of urban intelligent driving. However, this type of perception still relies on LiDAR data to construct ground truth databases, a process that is both cumbersome and time-consuming. Additionally, most mass-produced autonomous driving systems are equipped solely with surround camera sensors and lack the LiDAR data necessary for precise annotation. To tackle this challenge, we propose a fine-tuning method for BEV perception network based on visual 2D semantic perception, aimed at enhancing the model’s generalization capabilities in new scene data. Leveraging the maturity of 2D perception technologies, our method utilizes only 2D semantic segmentation labels and monocular depth estimations, thereby significantly reducing the dependence on expensive BEV ground truths and offering strong potential for industrial deployment. Extensive experiments and comparative analyses on the nuScenes and Waymo datasets demonstrate the effectiveness of our method. Specifically, it improves mAP and NDS by 2.51% and 1.93% on nuScenes, and by 1.21% and 0.78% on Waymo, respectively, validating its practical utility and robustness across diverse domains.

Autonomous Intersection Management (AIM) has attracted increasing research attention with the rapid advancement of Connected and Automated Vehicles (CAVs), Vehicle-to-Infrastructure (V2I) communication, and Internet of Things (IoT) technologies. Existing research demonstrates the potential for jointly optimizing signal schemes and vehicle trajectories to improve traffic efficiency at isolated urban intersections, while coordination of signal schemes and CAV trajectories among multiple intersections remains underexplored. Building upon our previous work on joint optimization at an isolated intersection, this paper proposes a bi-level optimization framework for urban corridors under mixed traffic conditions. At the upper level, a corridor arrival-departure model is developed to capture the dynamic relationships between consecutive intersections and to estimate total delay. The lower-level model, adapted from our previous work, takes the cycle lengths, phase orders, and reference green time durations from the upper level as input, and optimizes the final signal schemes and CAV trajectories at each intersection. A heuristic solution algorithm based on block coordinate descent is proposed to solve the upper-level problem. Simulations under varying traffic demands and CAV penetration rates are conducted, and the results demonstrate that the proposed method significantly enhances the traffic efficiency of urban corridors.

The development of connected and automated vehicles (CAVs), vehicle-to-infrastructure (V2I) communication technologies, and Internet of Things (IoT) provides new opportunities for intelligent intersection management. Existing research primarily focuses on optimizing the signal scheme of the intersection based on CAV trajectory data, while overlooking the influence of human-driven vehicles (HDVs) and the interactive relationship between the signal scheme and vehicle trajectories. This article proposes a joint optimization framework for both signal schemes and CAV trajectories at an isolated intersection. A vehicular delay estimation model is developed to predict the travel delay of each vehicle based on its trajectory under a given signal scheme. The estimation model consists of analytical trajectory generation models for both CAVs and HDVs. A heuristic search algorithm is designed to efficiently solve for the optimal signal scheme and CAV trajectories. Simulations are conducted under varying traffic demands and penetration rates, and the results indicate that the proposed method can significantly improve the traffic efficiency of a typical intersection. A sensitivity analysis is performed to investigate the impact of control zone length on the performance of the method.

Optimizing mixed vehicle platoons, which consist of connected and automated vehicles (CAVs) with human-driven vehicles (HDVs), is a critical challenge for intelligent transportation systems. While existing predictive control methods have improved modeling accuracy and control robustness, they are often constrained by their reliance on online optimization, limiting their applicability in real-time scenarios. To address this gap, this paper proposes a Robust Explicit Data-Driven Predictive Control (REDDPC) framework designed to provide robust and real-time control for mixed vehicle platoons. The framework begins by utilizing a deep Koopman operator network to learn the nonlinear dynamics of the system. Using this learned representation, the neural network-based control policy is then optimized through backpropagation, eliminating the need for online optimization. To enhance robustness, a reachability-based safety filter is integrated with the learned control policy to dynamically adjust control inputs, ensuring platoon safety under complex conditions. Simulation and experiment results demonstrate that the proposed method achieves superior tracking performance under noise, disturbance, and attack conditions, while significantly reducing online computational time, making it highly suitable for real-world deployment.

The vehicle platoon control is a promising technology to increase urban transportation efficiency and to make big savings on fuel. However, in practical environments, due to the openness and non-confidentiality of the network, vehicle platoon may be vulnerable to false data injection (FDI) attacks, resulting in malicious tampering of data on the channel. If the abnormal factors brought by FDI attacks on vehicles are ignored, the stable operation and driving safety of the vehicle platoon may not be guaranteed. Considering this situation, an observer-based event-triggered control protocol combined with an anomaly detection algorithm, is proposed. This control protocol has the ability to detect anomalies and does not require real-time updates of state information, which can match scenarios where state information is not measurable. Secondly, the Lyapunov stability theory is applied to provide sufficient conditions for the stability of platoon systems, and the Schur’s complement lemma is applied to reduce the difficulty of solving controller parameters. Finally, the feasibility and superiority of this protocol is confirmed through co-simulation experiments and comparative experiments.

Reinforcement learning (RL) has shown remarkable success in solving complex decision-making and control tasks. However, many model-free RL algorithms experience performance degradation due to inaccurate value estimation, particularly the overestimation of Q-values, which can lead to suboptimal policies. To address this issue, we previously proposed the Distributional Soft Actor-Critic (DSAC or DSACv1), an off-policy RL algorithm that enhances value estimation accuracy by learning a continuous Gaussian value distribution. Despite its effectiveness, DSACv1 faces challenges such as training instability and sensitivity to reward scaling, caused by high variance in critic gradients due to return randomness. In this paper, we introduce three key refinements to DSACv1 to overcome these limitations and further improve Q-value estimation accuracy: expected value substitution, twin value distribution learning, and variance-based critic gradient adjustment. The enhanced algorithm, termed DSAC with Three refinements (DSAC-T or DSACv2), is systematically evaluated across a diverse set of benchmark tasks. Without the need for task-specific hyperparameter tuning, DSAC-T consistently matches or outperforms leading model-free RL algorithms, including SAC, TD3, DDPG, TRPO, and PPO, in all tested environments. Additionally, DSAC-T ensures a stable learning process and maintains robust performance across varying reward scales. Its effectiveness is further demonstrated through real-world application in controlling a wheeled robot, highlighting its potential for deployment in practical robotic tasks.

The autonomous driving system faces challenges in selecting critical targets under dense environments with limited computation resources. Existing rule-based methods struggle with complex scenarios, while learning-based approaches lack interpretability and safety. This paper proposes a hybrid target selection model combining a lightweight long short-term memory (LSTM) based deep learning classifier and rule-based methods. Key input features are identified and processed to enhance training. The LSTM model is validated for accuracy and efficiency against bidirectional LSTM (Bi-LSTM) variations. Compared to the single approach, the hybrid model integrates the LSTM classifier and rule-based methods with a synthesizer, demonstrating improved accuracy, better interpretability, and a potentially higher functional safety level. Integrated into TDA4VM, the hybrid model shows timely and complementary target selection performance in actual urban and highway tests with low computation costs, proving its theoretical value and engineering prospects.

In the context of autonomous driving, acquiring the trajectories of surrounding vehicles in advance by autonomous vehicles is a crucial factor in ensuring high-level road safety. While trajectory prediction methods based on deep learning have achieved promising results, these data-driven models lack interpretability and transparency, making their reliable use a significant challenge. In this paper, firstly, an intention-aware spatial-temporal attention network-based trajectory prediction model is constructed, which considers the coupling of driving intention and the interaction with surrounding vehicles, extracts important feature information of vehicles in both temporal and spatial dimensions. Secondly, a vehicle trajectory prediction method via the integration of data-driven and knowledge-guided is proposed, considering both hard and soft constraints. A hard constraint of vehicle kinematics is incorporated into the intention-aware spatial-temporal attention network prediction model to generate physically feasible predicted trajectories and to make this part of the network structure have a human-understandable physical meaning. In addition, by leveraging knowledge related to traffic rules, an auxiliary loss function based on knowledge constraint penalties is designed as a soft constraint to optimize the training of the model and improve the interpretability of the training process. Finally, the proposed model is experimentally evaluated on the datasets and the prediction results are analyzed in terms of reliability and accuracy. The experimental results demonstrate that knowledge guidance effectively enhance the reliability and interpretability of the prediction, and improve the accuracy of long-term trajectory prediction.

Cooperative lane-change is a pivotal application of connected and automated vehicles (CAVs), enhancing traffic safety and efficiency, especially in congested urban areas and highway on-ramps. However, the widespread implementation of multi-CAV cooperative lane-change is hindered by the lack of efficient verification strategies. This issue is exacerbated by the “curse of dimensionality”, stemming from numerous scenario variables and complex algorithms. To overcome this, we propose an efficient verification strategy that accelerates the process through optimal feature selection. By eliminating variables that have minimal impact on evaluation outcomes yet significantly increase testing complexity, our approach streamlines the verification process, minimizing information loss while maintaining high efficiency. We validated this strategy in various urban and highway scenarios involving multiple CAVs. During the verification process, the dimensionality of scenario variables was reduced, resulting in an exponential decrease in the number of testing scenarios, with information loss limited to no more than 6%. The results demonstrate that our proposed strategy significantly improves the efficiency of verifying cooperative lane-change algorithms in high-dimensional scenario variables, with minimal loss of essential information.

With the development of intelligent vehicle and platoon technology, multi-platoon system will become a new solution to further improve traffic efficiency on highways. However, the existing research seldom consider the interference of human-driven vehicles on multi-platoon stability and the following strategy of multi-platoon leader in mixed traffic. In this paper, a robust distributed model predictive control method for multi-platoon leader in mixed traffic is proposed to reduce the impact of human-driven vehicles on multi-platoon control performance. The following control strategy of multi-platoon leader is proposed firstly, which flexibly determines the following control targets according to the states of leader and HDV to avoid unnecessary frequent acceleration and deceleration. Then, the robust model prediction controller of multi-platoon leader is designed, where the states of sub-platoon leader are added to the objective function in the nominal system optimization problem to reduce the states change of the following vehicles under the influence of HDV from both forward and backward traffic. Furthermore, the auxiliary control law is designed to eliminate the error between the actual states and the nominal states to achieve the suppression of HDV interference. The simulation results show that the multi-platoon leader following control strategy can effectively reduce the speed variation of the multi-platoon to suppress the impact of HDV motion uncertainty on multi-platoon. Moreover, compared with the robust model prediction method of single-platoon leader without considering the state of the rear vehicle, the proposed method can reduce the control errors and improve the stability of multi-platoon.

Bird’s-eye-view (BEV) semantic segmentation is becoming crucial in autonomous driving systems. It realizes ego-vehicle surrounding environment perception by projecting 2D multi-view images into 3D world space. Recently, BEV segmentation has made notable progress, attributed to better view transformation modules, larger image encoders, or more temporal information. However, there are still two issues: 1) a lack of effective understanding and enhancement of BEV space features, particularly in accurately capturing long-distance environmental features and 2) recognizing fine details of target objects. To address these issues, we propose OE-BevSeg, an end-to-end multimodal framework that enhances BEV segmentation performance through global environment-aware perception and local target object enhancement. OE-BevSeg employs an environment-aware BEV compressor. Based on prior knowledge about the main composition of the BEV surrounding environment varying with the increase of distance intervals, long-sequence global modeling is utilized to improve the model’s understanding and perception of the environment. From the perspective of enriching target object information in segmentation results, we introduce the center-informed object enhancement module, using centerness information to supervise and guide the segmentation head, thereby enhancing segmentation performance from a local enhancement perspective. Additionally, we designed a multimodal fusion branch that integrates multi-view RGB image features with radar/LiDAR features, achieving significant performance improvements. Extensive experiments show that, whether in camera-only or multimodal fusion BEV segmentation tasks, our approach achieves state-of-the-art results by a large margin on the nuScenes dataset for vehicle segmentation, demonstrating superior applicability in the field of autonomous driving. Our code will be released at https://github.com/SunJ1025/OE-BevSeghttps://github.com/SunJ1025/OE-BevSeg.

Urban intersections, dense with pedestrian and vehicular traffic and compounded by positioning signal obstructions, are among the most challenging areas in urban traffic systems. Traditional single-vehicle intelligence systems often perform poorly in such environments due to a lack of global scene observations and the inherent uncertainty in predicting other agents’ intentions. Vehicle-to-Everything (V2X) technology, through real-time communication between vehicles (V2V) and vehicles to infrastructure (V2I), offers a robust solution. However, practical applications still face numerous challenges. Spatial registration among vehicle and infrastructure endpoints with different configurations in multi-end sensing systems is crucial for ensuring the accuracy of perception system data. Most existing multi-end spatial registration methods rely on initial extrinsic values provided by positioning systems, but the instability of GNSS signals due to high buildings in urban canyons poses severe challenges to these methods. To address this issue, this paper proposes a novel multi-end spatial registration method that does not require positioning priors to determine initial external parameters and meets real-time requirements. Our method introduces an innovative multi-end perception object association technique that leverages a newOverall Distance(oDist) metric to measure the spatial association between perception objects, subsequently using this metric as the foundation for an optimal transport formulation. By this means, we can extract co-observed targets from object association results for further external parameter computation and optimization. Extensive comparative and ablation experiments conducted on the simulated dataset V2X-Sim and the real dataset DAIR-V2X confirm the effectiveness and efficiency of our method. The code for this method can be accessed at:https://github.com/MassimoQu/v2i-calib.

This study explores the effect of takeover time (TOT) on decision-making for Level-3 autonomous vehicles (L3-AVs). The existing research on L3-AV lacks an in-depth analysis of the mechanisms affecting TOT, ignores the importance of spatial and temporal variations in features for TOT prediction, and also lacks consideration of TOT in downstream trajectory planning tasks. This study proposed an exponential smoothing transformers (ETS) former model for TOT prediction, and then, the spatial-temporal predictive transformer (ST-Preformer) was employed to forecast the trajectories of surrounding vehicles, assess lane availability, and determine lane-changing probabilities. Ultimately, these evaluations contribute to the decision-making process of L3-AVs. The findings showed that the ETSformer was able to explain more than 83% of the characteristics of the TOT distribution in the TOT prediction task, effectively reducing the absolute percentage error by 0.7%, based on which the decision-making framework was able to make safe and comfortable optimal decisions. Decision-making is closely related to driving conditions and the surrounding traffic state, and TOT has a critical impact on the safety and stability of decision-making. A comprehensive understanding the impact of TOT on decision-making can help improve the safety of autonomous driving and provide guidance for improving decision-making techniques.

Training deep reinforcement learning (RL) agents necessitates overcoming the highly unstable nonconvex stochastic optimization inherent in the trial-and-error mechanism. To tackle this challenge, we propose a physics-inspired optimization algorithm called relativistic adaptive gradient descent (RAD), which enhances long-term training stability. By conceptualizing neural network (NN) training as the evolution of a conformal Hamiltonian system, we present a universal framework for transferring long-term stability from conformal symplectic integrators to iterative NN updating rules, where the choice of kinetic energy governs the dynamical properties of resulting optimization algorithms. By utilizing relativistic kinetic energy, RAD incorporates principles from special relativity and limits parameter updates below a finite speed, effectively mitigating abnormal gradient influences. In addition, RAD models NN optimization as the evolution of a multiparticle system where each trainable parameter acts as an independent particle with an individual adaptive learning rate. We prove RAD's sublinear convergence under general nonconvex settings, where smaller gradient variance and larger batch sizes contribute to tighter convergence. Notably, RAD degrades to the well-known adaptive moment estimation (ADAM) algorithm when its speed coefficient is chosen as one and symplectic factor as a small positive value. Experimental results show RAD outperforming nine baseline optimizers with five RL algorithms across twelve environments, including standard benchmarks and challenging scenarios. Notably, RAD achieves up to a 155.1% performance improvement over ADAM in Atari games, showcasing its efficacy in stabilizing and accelerating RL training.

Trajectory prediction and planning remain key challenges for autonomous vehicles (AVs), particularly in complex and dynamic environments. Existing methods, typically based on static safety metrics like Time-to-Collision (TTC), fail to account for the evolving nature of risk in real-world traffic. This paper proposes a novel Safety-Aware Trajectory Prediction and Planning (SA-TP$^{2}$) model, which introduces an adaptive driver risk field to simulate human- like risk perception and decision-making. By dynamically modeling risk as a continuous variable, SA-TP$^{2}$adjusts vehicle trajectories in real-time, accounting for interactions with other agents, road conditions, and environmental uncertainties. The model integrates imitation learning (IL), rule-based strategies, and physics-informed neural networks (PINNs) to ensure safe, efficient, and human-compatible behavior. A Linformer-based architecture and Temporal Hypergraph Convolution Network (THGCN) are introduced to optimize computational efficiency, enabling real-time operation in resource-constrained environments. Experimental results on benchmark datasets including NGSIM, HighD, MoCAD, and NuScenes demonstrate that SA-TP$^{2}$achieves SOTA performance in trajectory prediction. Additionally, extensive closed-loop testing on the NuPlan and CommonRoad platforms further confirms that SA-TP$^{2}$outperforms existing baselines, paving the way for safer navigation of autonomous driving systems.

Multi-lane formation control can significantly enhance the efficiency of traffic systems in multi-lane scenarios. However, existing multi-lane formation control methods are mostly developed for fully Connected and Automated Vehicle (CAV) environments and lack a mechanism for multi-lane formation control in mixed traffic. This paper proposes a CAV formation grouping method and an interaction mechanism of CAVs and Human-driven Vehicles (HDVs), which is suitable for different traffic volumes and penetration rates. Depending on the positional relationship between the CAV formation and HDVs, it flexibly chooses between conventional formation control methods or improved mixed traffic formation control methods. By conducting simulations at various input flow volumes and penetration rates, the applicability of this method to different conditions is verified. The paper also explores the extent to which multi-lane formation control methods can improve traffic efficiency and their relationship with traffic volume and penetration rate.

Intelligent intersection management has been a research hotspot in recent years within the domain of intelligent transportation systems (ITS). Existing studies exhibit a deficiency in explicitly addressing the trajectories of human driven vehicles (HDVs) under mixed traffic environment, and they fall short of harnessing the full potential of connected and automated vehicles (CAVs) and Vehicle-to-Infrastructure (V2I) technologies for the joint optimization of signal timing and phasing and vehicle trajectories. In this study, we propose a cooperative intersection management method designed for mixed traffic environment. Our approach jointly optimizes the green time durations, phase order of traffic signals, and vehicle trajectories. To account for the impact of traffic signals on HDVs, we incorporate it as a virtual leading vehicle within the optimal velocity model (OVM). The comprehensive model is formulated as a nonlinear programming problem and then converted into a mixed integer programming problem using the big-M method. We conduct simulations of the proposed method in various scenarios at different MPRs. The results reveal a significant reduction in average travel time compared to the actuated signal control, highlighting the enhanced efficiency of the intersection achieved through our proposed method.

The multi-vehicle coordinated lane change is one typical application of intelligent connected vehicle(ICV), which must be systematically and thoroughly verified before across-the-board commercial application. Existing evaluation frameworks face challenges in effectively verifying multi-vehicle coordinated lane change algorithm, whose decision-making process is more complex and needs to consider more complex surrounding environments. This complexity introduces the "curse of dimensionality" into the verification process, adversely impacting verification efficiency. To address the aforementioned challenge, an efficient verification strategy with optimal feature subset selection is proposed in this study. Initially, the subset feature is defined by the integrals of position probability density function between host vehicle and surrounding vehicles across various decision-making phases of coordinated lane change algorithm. Following this, the optimal feature subset selection method is presented for verification in different decision-making phases of coordinated lane change algorithm. Subsequently, the verification strategy is delineated. Finally, the optimal feature subset selection verification strategy is implemented within a coordinated lane change scenario. A multi-start search algorithm is employed to explore the feasible domain of the multi-vehicle coordinated lane change algorithm. Verification through simulation is then executed, and its efficiency is compared with a widely used evaluation framework based on Test Matrix. Notably, the proposed strategy demonstrates a minimum efficiency improvement of 85%. The verification results underscore the effectiveness the proposed method in verification of phased multi-vehicle coordinated lane change decision-making algorithm, particularly within high-dimensional and complex environments.

In this article, we present the first experimental results of data-driven predictive control for connected and autonomous vehicles (CAVs) in dissipating traffic waves. In particular, we consider a recent strategy of Data-EnablEd Predictive Leading Cruise Control (DeeP-LCC), which bypasses the need of identifying the driving behaviors of surrounding vehicles and directly relies on measurable traffic data to achieve safe and optimal CAV control in mixed traffic. We present the implementation details ofDeeP-LCC, including data collection, equilibrium estimation, and control execution. Based on a miniature experiment platform, we reproduce the phenomenon of stop-and-go waves in two typical traffic scenarios: 1) open straight-road scenario under external disturbances and 2) closed ring-road scenario with no bottlenecks. Our experiments clearly demonstrate thatDeeP-LCCenables one or a few CAVs to dissipate the traffic waves in both traffic scenarios. These experimental findings validate the great potential ofDeeP-LCCin smoothing practical traffic flow in the presence of noisy data, uncertain low-level vehicle dynamics, and communication and computation delays. The code and videos of our experimental results are available athttps://github.com/soc-ucsd/DeeP-LCC.

Over recent years, the Vehicle-Road-Cloud Integration System (VRCIS) and Intelligent and Connected Vehicles (ICVs) have gained significant attention in the realm of autonomous driving. By sharing data across diverse traffic participants and coordinating with VRCIS, ICVs can achieve enhanced perception accuracy and superior driving decisions, surpassing autonomous vehicles that rely solely on onboard sensors. Existing literature explores VRCIS’ overall architecture, applications, and deployment status. However, there is a lack of a comprehensive review focusing on the overarching architecture of ICV’s perception and its associated technologies, which are fundamental to VRCIS from an information integration perspective. This gap hinders the development of a robust perception framework for VRCIS, including the crucial perception technologies specific to it. This survey seeks to bridge this gap by offering an exhaustive review of the designed VRCIS perception framework and its specific perception technologies. Firstly, an overview of VRCIS’ perception architecture is provided, and the application relationships among various perception technologies are elucidated. Then, single-node, multi-node, and vehicle-road-cloud collaborative perception technologies are explored in sequence. Finally, the survey concludes with a discussion of insights and prospective future directions for VRCIS.

Connected autonomous vehicles (CAVs) cooperate through vehicle to every-thing (V2X) wireless communication to achieve platooning control, which can substantially increase the capacity of roads and reduce the risk of accidents. However, wireless communication systems may encounter abnormal situations in actual traffic environments, such as data packet loss, signal interruption, and communication module damage, which will cause the switching of communication topology among vehicles, thus affecting the performance of platooning system. To address this problem, a self learning-based comfort and energy-efficient platooning control strategy through cloud for CAVs with switching topologies and state estimation is proposed. First, we use weighted data fusion to process the original data. Simultaneously designing a rule to ensure that the acceleration change rate at switching moment remains smooth. Then, we introduce the adaptive Kalman filter (AKF) to get a high-precision state estimation in the abnormal communication scenario and thus to ensure acceptable passenger comfort level. Finally, a double-dueling-deterministic policy gradient ($\rm \mathbf {D^{3}PG}$) algorithm is proposed to achieve the platooning control, and a reliable self-learning platoon strategy was established using iterative learning, prior knowledge and data correction. The numerical results indicate that the proposed method can ensure car-following performance, comfort, and energy efficiency of CAV in abnormal communication scenarios.

The on-ramp merging is one of the typical bottlenecks on highways, and it’s expected to improve vehicle safety and traffic efficiency in this area through multi-vehicle collaboration. Existing research rarely coordinates on-ramp merging utilizing global information in a cyber-physical system, and most of them assume that vehicles in the mainline wouldn’t change lanes for simplification. However, scheduling methods dealing with multi-lane merging areas have been less explored. To address the problem, an optimized scheduling method with dynamic conflict graph is proposed in this study. First, the dynamic conflict graph is established, where vertices define the attributes of vehicle groups and edges describe the relationship among them; the optimization problem is then reconstructed as a graph search problem. Subsequently, a graph decomposition method is presented for the dynamic conflict graph. The feasible domain of vertices’ final states and costs of edges are determined based on optimal control theory, after which the heuristic depth-first search strategy is adopted to find a near-optimal solution. Finally, the dynamic conflict graph is applied in a continuous traffic flow. Simulations are conducted, and the performance is compared with the default algorithm in SUMO. The simulation results reveal that the proposed method reduces the overall travel delay while guaranteeing safety.

In real life, due to the influence of environment and physical equipments, C-V2X wireless communication network connection is prone to be intermittent. When intermittent communication occurs, the vehicle cannot make correct control decisions because it cannot receive the required information from neighboring vehicles, which will lead to the deterioration of the performance of the vehicle platoon. Considering the intermittent information connection of the leading vehicle through C-V2X communication network, a robust control method is proposed to realize vehicle platoon control. The platoon control strategy of a connected vehicle system is designed based on the mode-dependent average dwell time (MDADT) method. The designed strategy has better performance in reducing conservatism and improving flexibility, by comparison with the average dwell time (ADT) method. Moreover, based on the designed switching strategy, the sufficient conditions for the platoon control system to meet the exponential stability and exponential$L_{2}$performance are analyzed, and then, the linear matrix inequalities for solving the controller gain are given. Furthermore, the designed method has an advantage in applying to a variety of communication topologies. Finally, the effectiveness of the proposed robust control method in the case of intermittent communication in leading vehicle’s information transmission is verified by simulation.

As one of the typical applications of cyberphysical system (CPS), autonomous vehicles (AVs) are vulnerable to malicious disturbance from cyberattacks while tracking the desired path. This article focuses on CPS-based path tracking control problem of AVs under cyberattacks. First, the nonlinear state and measurement equations of AVs under cyberattacks are established based on vehicle dynamics model. Second, to improve the robustness of AVs against cyberattacks, sensor redundancy is introduced. A cyberattack detection method is designed by using extended Kalman filter, and the computational complexity of the cyberattack detection method is analyzed. In addition, sensor switching rules are developed to isolate the disturbance of cyberattacks. Then, the control problem of the AVs is formulated based on model predictive control. Input-to-state stability of the control system under cyberattacks is established, and a link between the tolerable attack intensity and the detection thresholds is clearly revealed. Finally, the simulation results demonstrate the effectiveness of the proposed control strategy.

New challenges such as automation, connection, electrification, and sharing (ACES) have brought disruptive changes to vehicles, transportation, and mobility services, which urgently requires an ideal solution for sustainable transportation. This paper introduces the Internet as a paradigm and, for the first time, proposes the Transportation Internet (TI), inspired by the similarity between the Internet and transportation. Referring to the construction ideas of the Internet, this paper establishes the framework of TI, proposes the transportation router based on the transportation switching and routing models, and preliminarily forms a large-scale automatic transportation solution. Following the latest technologies of the Internet, this paper further presents the software-defined transportation (SDT) by separating the control plane and transport plane of the transportation router, which can enhance transportation routing and provide Internet-like capabilities such as centralized intelligent control, terminals plug-and-play, and open application ecology. The evaluation of the prototype system shows promising results. The software-defined signals (SDS) can save 36% energy compared to signal machines, and the software-defined vehicles (SDV) automatic driving can save 24% energy compared to manual driving. Overall, TI brings innovations to sustainable transportation, and provides a framework for a new generation of Intelligent Transportation Systems (ITS).

Various sensors are adopted by autonomous driving systems to perceive objects and surroundings. Thus, the multi-sensor data fusion techniques become essential to combine different sensors’ advantages for better perception performance. However, the current multi-sensor data fusion techniques suffer from the high cost of computation resources, low expansibility for more diverse sensors, and insufficient systematic consideration for modeling. This paper first constructs a detachable and expansible multi-sensor data fusion model based on three main modules: front fusion, global fusion, and synthesizer, where the methods for flexible association gating and virtual targets have been designed. The model can be disassembled and configured for different trim levels of vehicles and is easily expansible for adding more heterogeneous sensors. Next, the presented multi-sensor data fusion model is compared with the cheap Joint Probabilistic Data Association (C-JPDA) method. The comparison shows the superior accuracy of the designed model on false association and effective narrowing of the variance of object detection. Finally, the presented multi-sensor data fusion model is integrated into an embedded system and experimented on urban roads and highways with the engaged Level 3 autonomous driving function. The experiment results indicate that the proposed model has excellent sensor data fusion performance and provides accurate and timely object information in the Level 3 autonomous driving system.

The cooperation between connected and automated vehicles (CAVs) has emerged as a promising way to improve traffic efficiency and safety for ramp merging on highways. Existing research mostly focused on the collaboration of individual CAVs, while the cooperative merging strategy in mixed traffic considering vehicle platoons has been less explored. To address the above problem, this study aims to build connections between sequence scheduling and motion planning in mixed traffic, where individual CAVs, CAVs platoons, and mixed platoons coexist. First, optimal control strategies are presented for vehicles with flexible merging points based on Pontryagin’s minimum principle (PMP), and the final vehicle states in variable conditions are summarized in a phase diagram. Subsequently, the optimal final-state phase diagram is introduced into the passing sequence tree search process, which is designed for different vehicle groups in mixed traffic. Heuristic pruning rules are added to the depth-first search strategy to facilitate finding the optimal solution. Finally, an event-triggered receding horizon optimization algorithm is developed for continuous implementation. The numerical simulations are conducted in multiple traffic volumes, and the simulation results reveal that our proposed algorithm significantly improves the overall traffic efficiency and reduces vehicle-passing delays compared with the traditional FIFO-based cooperation method.

Autonomous vehicle (AV) is expected to reshape the future transportation system, and its decision-making is one of the most critical modules. Many current decision-making modules are designed scenario-by-scenario and thus are not capable of meeting high-level AV requirements for lack of scalability to cope with the diversity of drivers’ demands and the infinity of traffic elements. This paper surveys the decision-making design inspired by driver intelligence and environment reasoning with better scalability for future high-level AVs. It involves three aspects: human factors in driving, environment reasoning, and detailed decision methods. The current state of the art of these three sections is surveyed in this paper. The characteristics of distinguished drivers, their decision mechanism, and the factors influencing decision-making are reviewed first to learn from excellent drivers. Environment reasoning is introduced following as a three-layer structure consisting of restriction, interaction, and attention. The optimization-based decision-making algorithms are then reviewed from the aspect of optimization targets, frameworks, applied scenarios, and limitations. Inspired by the existing research on driver intelligence and environment reasoning, a promising decision-making framework is also introduced for high-level AV design.

This article presents an interacting multiple model (IMM) for short-term prediction and long-term trajectory prediction of an intelligent vehicle. This model is based on vehicle's physics model and maneuver recognition model. The long-term trajectory prediction is challenging due to the dynamical nature of the system and large uncertainties. The vehicle physics model is composed of kinematics and dynamics models, which could guarantee the accuracy of short-term prediction. The maneuver recognition model is realized by means of hidden Markov model, which could guarantee the accuracy of long-term prediction, and an IMM is adopted to guarantee the accuracy of both short-term prediction and long-term prediction. The experiment results of a real vehicle are presented to show the effectiveness of the prediction method.

In mixed traffics, humans are easily confused by the non-humanlike lane change of autonomous vehicles. This may even bring traffic accidents. According to the naturalistic driving analysis results that there exist significant influences of multi-obstacles on lane change behavior, a humanlike lane change decision strategy for scenarios with multi-obstacles is presented. The driver tolerance force is proposed to establish the relationship between the longitudinal social force and lateral lane change behavior by using the driver's speed requirement. Then the visual attenuation coefficient is designed to reflect the influence of social forces generated by different traffic participants. Compared with other methods, the statistical results based on the naturalistic driving data indicate that the proposed strategy has a higher decision accuracy of target lane and start point.

Model-based policy gradient (MBPG) has been employed to seek an approximate solution to the optimal control problem. However, there is coupling between adjacent states due to temporal dependencies, making the training time grow linearly with the time horizon. This paper reshapes the training process of MBPG with the time-splitting technique to establish a time-independent algorithm called Training Through Time-Splitting (T3S). First, copy the coupled variables to obtain two independent variables. Meanwhile, an extra variable together with an equivalence constraint is introduced for problem consistency. Then, the transformed problem divides into subproblems with carefully derived loss functions. Subproblems own decoupled variables and shared policy networks, which means they can be optimized concurrently. Guided by the algorithm design, this paper further proposes an asynchronous parallel training scheme to accelerate training efficiency. Numerical simulation shows that the T3S algorithm outperforms the MBPG algorithm by 83.6% in wall-clock time with a trajectory tracking task.

This special issue of the IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING (T-ASE) focuses on how the state-of-the-art achievements and applications in the general area of artificial intelligence in automation for autonomous unmanned systems applications. As Guest Editors, we are very pleased to present the selected 16 articles, whose topics are specifically related to artificial intelligence real-time object detection, recognition, localization, control optimization, motion planning, formation control, adaptive control, and autonomous decision-making.

Connected and automated vehicles have shown great potential in improving traffic mobility and reducing emissions, especially at unsignalized intersections. Previous research has shown that vehicle passing order is the key influencing factor in improving intersection traffic mobility. In this paper, we propose a graph-based cooperation method to formalize the conflict-free scheduling problem at an unsignalized intersection. Based on graphical analysis, a vehicle’s trajectory conflict relationship is modeled as a conflict directed graph and a coexisting undirected graph. Then, two graph-based methods are proposed to find the vehicle passing order. The first is an improved depth-first spanning tree algorithm, which aims to find the local optimal passing order vehicle by vehicle. The other novel method is a minimum clique cover algorithm, which identifies the global optimal solution. Finally, a distributed control framework and communication topology are presented to realize the conflict-free cooperation of vehicles. Extensive numerical simulations are conducted for various numbers of vehicles and traffic volumes, and the simulation results prove the effectiveness of the proposed algorithms.

Intelligent electric vehicles (IEVs) have attracted more and more attention benefitting from the characteristics of high degree of safety and energy efficiency. This paper proposes an adaptive cruise control framework considering regenerative braking to improve safety and energy efficiency of IEVs during the car-following process. At first, a coupled and nonlinear dynamic model of IEVs system is constructed, which is mainly composed of a powerful battery, an electric motor, a single-speed transmission, and a hydraulic braking system. Then, an adaptive fuzzy sliding mode high-level controller is designed to accurately obtain the desired longitudinal acceleration of IEVs, in which the fuzzy logic is utilized to approximate the switching control item of the sliding mode control for chattering free. And the stability of high-level controller is proven by the Lyapunov theory. In the lower-level controller, traction control and brake control are designed to track the desired acceleration produced by the high-level controller, in addition, a novel regenerative braking strategy is presented to maximize the braking energy recovery. Finally, the simulation results indicate that the proposed control scheme has the excellent performance of longitudinal tracking and braking energy recovery with no loss of safety.

Cooperative formation and control of autonomous vehicles (AVs) promise increased efficiency and safety on public roads. In single-lane mixed traffic consisting of AVs and human-driven vehicles (HDVs), the prevailing platooning of multiple AVs is not the only choice for cooperative formation. In this paper, we investigate how different formations of AVs impact traffic performance from a set-function optimization perspective. We first reveal a stability invariance property and a diminishing improvement property of noncooperative formation when AVs adopt an independently designed Adaptive Cruise Control (ACC) strategy. Then, we focus on the case of cooperative formation where AVs utilize a centralized optimal controller. We further investigate the corresponding optimal formation of multiple AVs using set-function optimization. Two predominant optimal formations,i.e., uniform distribution and platoon formation, emerge from extensive numerical experiments. Interestingly, platooning might have the least potential to improve traffic performance when HDVs have poor string stability behavior. These results suggest more opportunities for cooperative formation of AVs, beyond platooning, in practical mixed traffic flow.

Comprehensive and accurate perception of the real 3D world is the basis of autonomous driving. However, many perceptual methods focus on a single task or object type, and the accuracy of existing multi-task or multi-object methods is difficult to balance against their real-time performance. This paper presents a unified framework for concurrent dynamic multi-object joint perception, which introduces a real-time monocular joint perception network termed MJPNet. In MJPNet relative weightings are automatically learned by a series of developed network branches. By training an end-to-end deep convolutional neural network on a shared feature encoder and many proposed decoding sub-branches, the information of the 2D category and 3D position/pose/size of an object are reconstructed both simultaneously and accurately. Moreover, the effective information among subtasks is transferred by multi-stream learning, guaranteeing the accuracy of each task. Compared to various state-of-the-arts, comprehensive evaluations on the benchmark of challenging image sequences demonstrate the superior performance of our 2D detection and 3D reconstruction of depth, lateral distance, orientation, and heading angle. Moreover, on the KITTI test set, the real-time runtime (up to 15 fps) of MJPNet significantly outran the public state-of-the-art visual detection methods. Accompanying video:https://youtu.be/Z-goToOlI94.

This paper presents a road detection method for autonomous driving based on an end-to-end neural network model. Our method takes advantage of both the characteristics of road boundary and multi-task learning of a deep convolutional network. By reassigning the label and rebalancing the loss of road pixels, we focus on the learning of hard examples on the boundary to refine its performance. Then, a road geometric transformation-based data augmentation method is proposed to enable the network model to be robust under traffic scenes. Based on these two novel methods, a unified architecture consisting of a shared deep residual encoder network and multi-branch decoder sub-networks is integrated. It adopts road scene classification as a supervised learning task to realize road segmentation and scene classification simultaneously. Experiments demonstrate that the proposed method has achieved the highest MaxF value in most road scenes. Both qualitative and quantitative evaluations on the KITTI-Road benchmark demonstrate our superior performance.

Intersection is one of the most accident-prone urban scenarios for autonomous driving wherein making safe and computationally efficient decisions is non-trivial. Current research mainly focuses on the simplified traffic conditions while ignoring the existence of mixed traffic flows, i.e., vehicles, cyclists and pedestrians. For urban roads, different participants lead to a quite dynamic and complex interaction, posing great difficulty to learn an intelligent policy. This paper develops the dynamic permutation state representation in the framework of integrated decision and control (IDC) to handle signalized intersections with mixed traffic flows. Specially, this representation introduces an encoding function and summation operator to construct driving states from environmental observation, capable of dealing with different types and variant number of traffic participants. A constrained optimal control problem is built wherein the objective involves tracking performance and the constraints for different participants, roads and signal lights are designed respectively to assure safety. We solve this problem by gradient-based optimization, wherein the reasonable state will be given by the encoding function and then served as the input of policy and value function. An off-policy training is designed to reuse observations from driving environment and backpropagation through time is utilized to update the policy function and encoding function jointly. Verification result shows that the dynamic permutation state representation can enhance the driving performance of IDC, including comfort, decision compliance and safety with a large margin. The trained driving policy can realize efficient and smooth passing in the complex intersection, guaranteeing driving intelligence and safety simultaneously.

On a highway, the frequency of occurrence of irrelevant features, such as trees, varies a lot in different scenes. A limitation of the deep conventional neural networks used in end-to-end self-driving systems is that if the incoming images contain too much information, it makes it difficult for the network to extract only the subset of features required for decision making. Consequently, while existing end-to-end approaches may perform well in training scenes, they may not work correctly in other scenes. In this study, we developed a novel training method for an auto-encoder that equips it to ignore irrelevant features in input images while simultaneously retaining relevant features. Compared with feature extraction methods in existing end-to-end approaches, the proposed method reduces the labeling costs by only requiring image-level tags. The method was validated by training a convolutional neural network model to process the output of the encoder and produce a steering angle to control the vehicle. The entire end-to-end self-driving approach can ignore the influence of irrelevant features even though there are no such features when training the convolutional neural network.

Cooperative adaptive cruise control (CACC) has the potential to significantly improve road safety, highway throughput and reduce fuel consumption. However, the safety of the intended functionality (SOTIF) of CACC that focuses on unreasonable risks related to performance limitations has not been addressed. Moreover, various unknown uncertainties, disturbances, and controller perturbations present in the road environment make the guarantee of SOTIF for CACC a more challenging problem. This study presents a robust non-fragile fault tolerant control (RNFTC) strategy as a quantitative risk reduction method for ensuring SOTIF of CACC with system uncertainty, multisource disturbances, and controller perturbations. First, an intermediate based robust estimation method is proposed to estimate the performance limitations of perception and actuation, system states, and matched disturbances, simultaneously. Second, a robust non-fragile$\text{H}_{\infty }$FTC method is proposed to accommodate the simultaneous presence of performance limitations and disturbances. Third, theorems for solving optimal estimator and controller gains of the proposed RNFTC are derived in terms of linear matrix inequalities (LMIs). The requirements for CACC system stability, robustness, non-fragile and$\text{H}_{\infty }$performances under the proposed RNFTC are analyzed using Lyapunov theory. Finally, a series of comparative simulations with the CACC system are conducted to demonstrate the effectiveness and superiority of the proposed RNFTC method on ensuring SOTIF of CACC under unknown uncertainties, disturbances, and perturbations.

Connected and autonomous vehicles (CAVs) have great potential to improve road transportation systems. Most existing strategies for CAVs’ longitudinal control focus on downstream traffic conditions, but neglect the impact of CAVs’ behaviors on upstream traffic flow. In this paper, we introduce a notion of Leading Cruise Control (LCC), in which the CAV maintains car-following operations adapting to the states of its preceding vehicles, and also aims to lead the motion of its following vehicles. Specifically, by controlling the CAV, LCC aims to attenuate downstream traffic perturbations and smooth upstream traffic flow actively. We first present the dynamical modeling of LCC, with a focus on three fundamental scenarios: car-following, free-driving, and Connected Cruise Control. Then, the analysis of controllability, observability, and head-to-tail string stability reveals the feasibility and potential of LCC in improving mixed traffic flow performance. Extensive numerical studies validate that the capability of CAVs in dissipating traffic perturbations is further strengthened when incorporating the information of the vehicles behind into the CAVs’ control.

Wireless communication plays a significant role in the control of connected and automated vehicles (CAVs). In particular, poor communication would cause worse vehicle performances, and may even cause safety issues. This paper aims to establish a communication model for vehicular environments and deeply analyze the impact of communication characteristics on CAVs control. Firstly, the three-parameter Burr distribution delay model and the Nakagami distribution packet delivery rate (PDR) model are proposed to describe vehicular wireless networks' characteristics. Then, the platooning control is selected for a case study, and a vehicle platoon control system incorporating the proposed communication model is established. Furthermore, a simulation platform is built based on SUMO and Python, and the impact of communication characteristics on the platoon's performance is studied. The simulation results show that the characteristics presented by the communication model are consistent with those in field tests, and the quantized relationships between communication model parameters and vehicle control performance are also provided.

Connected and automated vehicles (CAVs) have a great potential to improve traffic efficiency in mixed traffic systems, which has been demonstrated by multiple numerical simulations and field experiments. However, some fundamental properties of mixed traffic flow, including controllability and stabilizability, have not been well understood. This paper analyzes the controllability of mixed traffic systems and designs a system-level optimal control strategy. Using the Popov-Belevitch-Hautus (PBH) criterion, we prove for the first time that a ring-road mixed traffic system with one CAV and multiple heterogeneous human-driven vehicles is not completely controllable, but is stabilizable under a very mild condition. Then, we formulate the design of a system-level control strategy for the CAV as a structured optimal control problem, where the CAV’s communication ability is explicitly considered. Finally, we derive an upper bound for reachable traffic velocity via controlling the CAV. Extensive numerical experiments verify the effectiveness of our analytical results and the proposed control strategy. Our results validate the possibility of utilizing CAVs as mobile actuators to smooth traffic flow actively.

Instance segmentation and panoptic segmentation is being paid more and more attention in recent years. In comparison with bounding box based object detection and semantic segmentation, instance segmentation can provide more analytical results at pixel level. Given the insight that pixels belonging to one instance have one or more common attributes of current instance, we bring up an one-stage instance segmentation network named Common Attribute Support Network (CASNet), which realizes instance segmentation by predicting and clustering common attributes. CASNet is designed in the manner of fully convolutional and can implement training and inference from end to end. And CASNet manages predicting the instance without overlaps and holes, which problem exists in most of current instance segmentation algorithms. Furthermore, it can be easily extended to panoptic segmentation through minor modifications with little computation overhead. CASNet builds a bridge between semantic and instance segmentation from finding pixel class ID to obtaining class and instance ID by operations on common attribute. Through experiment for instance and panoptic segmentation, CASNet gets mAP 32.8% and PQ 59.0% on Cityscapes validation dataset by joint training, and mAP 36.3% and PQ 66.1% by separated training mode. For panoptic segmentation, CASNet gets state-of-the-art performance on the Cityscapes validation dataset.

Making long-term trajectory prediction accurately for surrounding vehicles is the crucial prerequisite for intelligent vehicles to accomplish superb decision making and motion planning. In this paper, to achieve high-quality prediction accuracy both in the short and long term, we propose an integrated probabilistic framework with the combination of driver awareness model and Gaussian process model. The former model can obtain high-level semantic information using low-level two-dimensional motion elements. And the latter incorporates the vehicle physical model to reach good prediction performance with strengthened historical input sequence. Furthermore, experiments on the public naturalistic driving dataset in lane-changing scenarios are conducted to verify our novel approach. Compared with another advanced method, the superiorities of our proposed approach are demonstrated with higher estimation and prediction accuracy, as well as more reasonable uncertainty description in terms of the whole prediction process.

The uncertainties arising from the plant model and topologies have been a major challenge in multiagent consensus control. This article presents a distributed robust control method for an uncertain multiagent system with eigenvalue-bounded topologies. The heterogeneity of node dynamics is described as the uncertainties of a linear model with a common certain part. The linear transformation method is adopted to decompose topologically coupled controllers. Then, the linear matrix inequalities (LMIs) technique is used to numerically solve the distributed robust controller problem. It is proved that such a controller is robust stable under the condition that the topology is eigenvalue-bounded. The effectiveness of this method is validated by the simulation of a group of unmanned ground vehicles compared with the LQR controller.

The emergence of autonomous vehicles (AVs) is expected to revolutionize road transportation in the near future. Although large-scale numerical simulations and small-scale experiments have shown promising results, a comprehensive theoretical understanding to smooth traffic flow via AVs is lacking. In this article, from a control-theoretic perspective, we establish analytical results on the controllability, stabilizability, and reachability of a mixed traffic system consisting of human-driven vehicles and AVs in a ring road. We show that the mixed traffic system is not completely controllable, but is stabilizable, indicating that AVs can not only suppress unstable traffic waves but also guide the traffic flow to a higher speed. Accordingly, we establish the maximum traffic speed achievable via controlling AVs. Numerical results show that the traffic speed can be increased by over 6% when there are only 5% AVs. We also design an optimal control strategy for AVs to actively dampen undesirable perturbations. These theoretical findings validate the high potential of AVs to smooth traffic flow.

Lane keeping is a key task in driving, and it plays an important role in staying safe while driving. However, conventional lane-keeping assistance systems (LKASs) have a limited level of automation, while fully autonomous lane-keeping systems have shortcomings regarding reliability. To address these issues, an advanced LKAS with two switchable assistance modes, namely, the lane departure prevention mode and the lane-keeping co-pilot mode, is proposed in this paper. First, the system structure is constructed. Then, the functions and control strategies for the two assistance modes are defined. Next, the controller algorithms are designed using the learning-based model predictive control (LBMPC) method to compensate for modeling errors. Within the framework of LBMPC, an oracle is built to learn the unmodeled dynamics using an extended Kalman filter. Moreover, optimization problems are formulated to achieve the control objectives regarding driving safety and driver acceptance. Finally, driver-in-the-loop experiments carried out on a driving simulator prove that both assistance modes are effective. Furthermore, the lane-keeping co-pilot mode can help reduce the driving burden in the sense of avoiding frequent steering correction.

Pedestrian orientation is essential for trajectory prediction, intention and behavior analysis. Existing methods using low-level features result in imperfect performance. Aiming at continuous pedestrian orientation estimation from a single frame, we propose a novel approach based on high-level semantic features extracted from human keypoints locations. The proposed method first predicts human keypoints locations by pose estimation algorithm, based on human pose, body motion limitations and body parts occlusion. High-level semantic features are then extracted from keypoints locations. Finally, this method applies a soft classifier and interpolation to produce continuous results. Experiments conducted on public datasets show that the proposed method outperforms the state-of-the-art by a large margin, demonstrating the critical role of high-level semantic feature representation for the task of continuous pedestrian orientation estimation.

This paper is concerned with coordinated formation assignment and control of multiple connected vehicles in highway scenario. A dynamical interlaced layered formation generation method is introduced to provide safe distance among vehicles and efficiency for coordinated lane changing and formation switching simultaneously in real time. To assign vehicles to the generated formation, the optimal problem is modeled by introducing the function of numbers of changed lanes for all the vehicles with respect to the kinematic constraints, and on-board local controllers for the vehicles are developed. Simulation result indicates that, comparing to existing methods for lane assignment in multiple traffic scenarios, the method provided by this paper could increase the traffic efficiency by utilizing maximum road capacity while decreasing travel time for all the vehicles.

Connected and automated vehicles (CAVs) have a great potential to actively influence traffic systems. This has been demonstrated by large-scale numerical simulations and small-scale real experiments, whereas a comprehensive theoretical analysis is still lacking. In this paper, we focus on mixed traffic systems with one single CAV and heterogeneous human-driven vehicles, and present rigorous controllability analysis and optimal controller synthesis. Using the PBH controllability criterion, we reveal controllability properties of a linearized mixed traffic system in a ring road. It is proved that the mixed traffic flow can be stabilized by one single CAV under a very mild condition. We formulate the problem of designing CAV control strategies under a pre-specified communication topology as structured optimal controller synthesis. This formulation considers a system-level performance index that allows the CAV to actively dampen undesired perturbations in traffic flow. Numerical experiments verify the effectiveness of our results.

We present an experimental study comparing various Recurrent Neural Network architectures for the task of Vulnerable Road User (VRU) motion trajectory prediction in the intelligent vehicle domain. Making use of temporal motion cues and visual appearance features, we design multi-cue RNN-based architectures with dedicated optimization process to predict future moving trajectories from historical consecutive frames. Experiments are performed on image sequences recorded from on-board a moving vehicle and public tracking datasets. In particular, the Tsinghua-Daimler Cyclist Benchmark (TDCB) has been augmented with additional annotations (vari-ous VRU types) to support the evaluation of object tracking approaches and trajectory prediction methods. This newly introduced dataset is termed TDCB-Track. We demonstrate the effectiveness of the proposed RNN architectures on the public MOT16 dataset and the TDCB-Track dataset. We show that the proposed approaches outperform simpler baseline methods and stay ahead with the state-of-the-art.

Only a few existing works exploit multiple modalities of data for road detection task in the context of autonomous driving. In this work, a deep learning based approach is developed to fuse LIDAR point cloud and camera image features over a bird's eye view representation. A two-stream fully-convolutional network is designed as encoder to extract general features of two types of data. Instead of limiting the fusion processing at a single stage or to a predefined extent, we propose a multi-stage residual fusion strategy to merge the feature maps in a residual learning fashion, and integrate the information at different network depth. Experiments conduct on KITTI road benchmark show that our proposed method has a significant improvement over single modality methods and other fusion approaches. And it is also among the top-performing algorithms.

Vehicle-road cooperative perception system can greatly improve the perception ability of intelligent vehicles by making use of perception information from road side units (RSU). This paper focuses on the target positioning of static camera for vehicle-road cooperation. A low-cost camera calibration method is proposed to complete the accurate mapping between the image plane and the 3D world space. Precise location of interested targets are achieved by an efficient tracking strategy. Real test scenarios show that our algorithm can effectively locate vehicles, pedestrians, non-motor vehicles and other targets with high accuracy. Our algorithm won the Monocular Static Camera Positioning and Ranging Competition for Autonomous Driving championship in 2019.

Signalized intersections play an important role in transportation efficiency and vehicle fuel economy in urban areas. This paper proposes a cooperative method of traffic signal control and vehicle speed optimization for connected automated vehicles, which optimizes the traffic signal timing and vehicles' speed trajectories at the same time. The method consists of two levels, i.e., roadside traffic signal optimization and onboard vehicle speed control. The former calculates the optimal traffic signal timing and vehicles' arrival time to minimize the total travel time of all vehicles; the latter optimizes the engine power and brake force to minimize the fuel consumption of individual vehicles. The enumeration method and the pseudospectral method are applied in roadside and onboard optimization, respectively. Simulation studies are conducted to compare the proposed method with benchmark methods. The results show significant improvement of transportation efficiency and fuel economy by the cooperation method.

Research on collision avoidance has received significant attention in academia, but few attempts have been made regarding the impact of the real driver. Focusing on the hybrid vehicle controlled by a real driver, this paper proposes a combined yaw moment and steering torque control method based on driving behaviors during the emergency steering evasion (ESE). First, a CarSim vehicle model and a vision-based driving simulator are built to acquire and analyze ESE driving behaviors, and the trigger condition of the assistance is defined. Second, a preview distance adaptive driver steering model is created and followed by the calculation of the desired value of the steering angle and yaw rate from the planned collision avoidance path. Third, an ESE assistance controller is designed on the basis of the electric power steering (EPS) torque assistance law during normal driving and driving behaviors for the steering evasion lane change. Controllers used for this paper include a yaw rate tracking controller and a steering torque assistance fuzzy controller. The actuators are EPS, an electric stability program, and two hub motors in the rear axle. The preview distance is optimized by the particle swarm optimization method and the driving behavior-based ESE controller is tested with the driving simulator. The result shows that the optimized preview distance is credible, and the path tracking accuracy and the vehicle stability are improved with the help of the proposed ESE assistance controller by giving full consideration to driving behaviors in ESE.

This paper presents an object classification method for vision and light detection and ranging (LIDAR) fusion of autonomous vehicles in the environment. This method is based on convolutional neural network (CNN) and image upsampling theory. By creating a point cloud of LIDAR data upsampling and converting into pixel-level depth information, depth information is connected with Red Green Blue data and fed into a deep CNN. The proposed method can obtain informative feature representation for object classification in autonomous vehicle environment using the integrated vision and LIDAR data. This method is also adopted to guarantee both object classification accuracy and minimal loss. Experimental results are presented and show the effectiveness and efficiency of object classification strategies.

This paper considers the robustness analysis and distributed 'I-1∞(H-infinity) controller synthesis for a platoon of connected vehicles with undirected topologies. We first formulate a unified model to describe the collective behavior of homogeneous platoons with external disturbances using graph theory. By exploiting the spectral decomposition of a symmetric matrix, the collective dynamics of a platoon is equivalently decomposed into a set of subsystems sharing the same size with one single vehicle. Then, we provide an explicit scaling trend of robustness measure γ-gain, and introduce a scalable multistep procedure to synthesize a distributed 'I-1∞controller for large-scale platoons. It is shown that communication topology, especially the leader's information, exerts great influence on both robustness performance and controller synthesis. Furthermore, an intuitive optimization problem is formulated to optimize an undirected topology for a platoon system, and the upper and lower bounds of the objective are explicitly analyzed, which hints us that coordination of multiple mini-platoons is one reasonable architecture to control large-scale platoons. Numerical simulations are conducted to illustrate our findings.

This paper deals with the trajectory following control problem of a class of autonomous vehicles with parametric uncertainties, external disturbances, and over-actuated features. A novel adaptive hierarchical control framework is proposed to supervise the lateral motion of autonomous four-wheel independent drive electric vehicles. First, an adaptive sliding mode high-level control law with the linear matrix inequality-based switching surface is designed to produce a vector of front steering angle and external yaw moment, in which the uncertain term and the switching control gain are adaptively regulated by the fuzzy logic technique, to further moderate the chattering phenomenon, an adaptive boundary layer is introduced. Second, a pseudo-inverse low-level control allocation algorithm is presented to optimally allocate the external yaw moment via coordinating and reconstructing the tire longitudinal forces. Finally, numerical simulation and experimental results demonstrate that the proposed adaptive control approach has outstanding tracking performance.

The platooning of automated vehicles has the potential to significantly benefit road traffic. This paper presents a distributed$\text{H}_{\mathrm {\infty }}$control method for multi-vehicle systems with identical dynamic controllers and rigid formation geometry. After compensating for the powertrain nonlinearity, the node dynamics in a platoon is mathematically described by a multiplicative uncertainty model. The platoon control system is then decomposed into an uncertain part and a diagonal nominal system through linear transformation and eigenvalue decomposition of the information-exchange-topology matrix. Robust stability, string stability, and distance tracking performance of the designed platoons are analyzed theoretically under the decoupled$\text{H}_{\mathrm {\infty }}$framework. A comparative simulation with non-robust controllers is used to demonstrate the effectiveness of this method.

Existing traffic signal optimization and vehicle speed optimization at signalized intersections cannot work together for the lack of proper cooperation methods. We propose the V2I (vehicle to infrastructure) based cooperation between traffic signal and approaching vehicles which optimizes the traffic signal and vehicles' speed trajectories simultaneously. The cooperation consists of roadside traffic signal optimization and onboard speed control, of which the former calculates the optimal traffic signal timing and vehicles' arriving time to minimize trip time and the latter optimizes the vehicle engine power and brake force to minimize the fuel consumption in the whole trip. A simulation study is conducted to compare the proposed cooperation method and the actuated signal control method. The simulation results show significant improvement of transportation efficiency and vehicle fuel economy by using the cooperation method.

Extensive research interest has been focused on protecting vulnerable road users in recent years, particularly pedestrians and cyclists, due to their attributes of vulnerability. However, comparatively little effort has been spent on detecting pedestrian and cyclist together, particularly when it concerns quantitative performance analysis on large datasets. In this paper, we present a unified framework for concurrent pedestrian and cyclist detection, which includes a novel detection proposal method (termed UB-MPR) to output a set of object candidates, a discriminative deep model based on Fast R-CNN for classification and localization, and a specific postprocessing step to further improve detection performance. Experiments are performed on a new pedestrian and cyclist dataset containing 30 490 annotated pedestrian and 26 771 cyclist instances in over 50 000 images, recorded from a moving vehicle in the urban traffic of Beijing. Experimental results indicate that the proposed method outperforms other state-of-the-art methods significantly.

This paper presents two fuel-prioritized feedback controllers, which are called the estimated minimum principle (EMP) and kinetic energy conversion (KEC), to realize eco-cruising on varying slopes for vehicles with conventional powertrains. The former is derived from the minimum principle with an estimated Hamiltonian, and the latter is designed based on the equivalent conversion between the kinetic-energy change of vehicle body and the fuel consumption of the engine. They are implemented with analytical control laws and rely on current road slope information only without look-ahead prediction. This feature results in a very light computing load, with the average computing time of each step less than one millisecond. Their fuel-saving performances are quantitatively studied and compared with a model predictive control and a constant speed control. As an expansion, the control rule for avoiding rear-end collision is also designed by using a safety-guaranteed car-following model to constrain the high-risk behaviors.

This paper presents an energy-efficient and terrain-information-and-preceding-vehicle-information-incorporated energy management strategy for fully electric vehicles (FEVs) equipped with in-wheel motors. Saving driving energy with terrain preview and preceding vehicle movement prediction are crucial to prolong the driving distance for an FEV. Unlike conducting energy optimization under the assumption that the preceding vehicle movements are already known in most studies, the front vehicle movements are predicted during each control cycle based on the vehicle-to-vehicle communication, and the FEV vehicle velocity and motor torque distribution are optimized by a nonlinear model predictive controller to reduce energy consumption. The energy-saving objective is achieved by including, in the cost function, the motor energy consumption in each control cycle, while the safety objective is accomplished by keeping a suitable relative distance from the preceding vehicle. Since the nonlinear vehicle longitudinal model is applied, the gridding initial torque plane is utilized in each time step to search for the global minimum. Simulation results show that this method has a better energy-saving performance than the control method without using the preceding vehicle movement information, and the algorithm proposed here has a wide applicability under various driving conditions.

Significant progress has been achieved over the past decade on vision-based pedestrian detection; this has led to active pedestrian safety systems being deployed in most mid- to high-range cars on the market. Comparatively little effort has been spent on vision-based cyclist detection, especially when it concerns quantitative performance analysis on large datasets. We present a large-scale experimental study on cyclist detection where we examine the currently most promising object detection methods; we consider Aggregated Channel Features, Deformable Part Models and Region-based Convolutional Neural Networks. We also introduce a new method called Stereo-Proposal based Fast R-CNN (SP-FRCN) to detect cyclists based on stereo proposals and Fast R-CNN (FRCN) framework. Experiments are performed on a dataset containing 22161 annotated cyclist instances in over 30000 images, recorded from a moving vehicle in the urban traffic of Beijing. Results indicate that all the three solution families can reach top performance around 0.89 average precision on the easy case, but the performance drops gradually with the difficulty increasing. The dataset including rich annotations, stereo images and evaluation scripts (termed “Tsinghua-Daimler Cyclist Benchmark”) is made public to the scientific community, to serve as a common point of reference for future research.

The ability to perform long-range pedestrian detection is essential for autonomous vehicles. However, for 3-D LIDAR, an object's point cloud becomes sparse when it is away, directly affecting its detection as a result. In this paper, a novel density enhancement method is proposed to improve the quality of a sparse point cloud. The input of the method is an object's raw point cloud; first, a high-quality local coordinate system of the point cloud is built using a new evaluation metric, and then radial basis function (RBF)-based interpolation is performed based on the local coordinate system. Finally, a resampling algorithm is used to generate a new point cloud that not only meets a density requirement but also fits the object's geometric shape. Novel features of our method are its evaluation metric of a local coordinate system and method to choose a good shape parameter and kernel in RBF-based interpolation step. The effectiveness of this method is demonstrated using naturalistic data and three experiments.

In addition to decentralized controllers, the information flow among vehicles can significantly affect the dynamics of a platoon. This paper studies the influence of information flow topology on the internal stability and scalability of homogeneous vehicular platoons moving in a rigid formation. A linearized vehicle longitudinal dynamic model is derived using the exact feedback linearization technique, which accommodates the inertial delay of powertrain dynamics. Directed graphs are adopted to describe different types of allowable information flow interconnecting vehicles, including both radar-based sensors and vehicle-to-vehicle (V2V) communications. Under linear feedback controllers, a unified internal stability theorem is proved by using the algebraic graph theory and Routh-Hurwitz stability criterion. The theorem explicitly establishes the stabilizing thresholds of linear controller gains for platoons, under a large class of different information flow topologies. Using matrix eigenvalue analysis, the scalability is investigated for platoons under two typical information flow topologies, i.e., 1) the stability margin of platoon decays to zero as 0(1/N2) for bidirectional topology; and 2) the stability margin is always bounded and independent of the platoon size for bidirectional-leader topology. Numerical simulations are used to illustrate the results.

The platooning of autonomous ground vehicles has potential to largely benefit the road traffic, including enhancing highway safety, improving traffic utility and reducing fuel consumption. The main goal of platoon control is to ensure all the vehicles in the same group to move at consensual speed while maintaining desired spaces between adjacent vehicles. This paper presents an overview of vehicular platoon control techniques from networked control perspective, which naturally decomposes a platoon into four interrelated components, i.e., 1) node dynamics (ND), 2) information flow topology (IFT), 3) distributed controller (DC) and, 4) geometry formation (GF). Under the four-component framework, existing literature are categorized and analyzed according to their technical features. Three main performance metrics, i.e. string stability, stability margin and coherence behavior, are also discussed.

To address the problem caused by a conventional adaptive cruise control (ACC) system, which hinders drivers from changing lanes, in this study we propose a novel coordinated ACC system with a lane-change assistance function, which enables dual-target tracking, safe lane change, and longitudinal ride comfort. We first analyze lane-change risk by calculating minimum safety spacing between the host vehicle and surrounding vehicles and then develop a coordinated control algorithm using model predictive control theory. Tracking performance is designed on the basis of tracking errors of the host car and two leading vehicles, safety performance is realized by considering the safe distance between the host car and surrounding vehicles, and ride comfort performance is realized by limiting the vehicle's longitudinal acceleration. Driver-in-the-loop tests performed on a driving simulator confirm that the proposed ACC system can overcome the disadvantages of conventional ACC and achieves multiobjective coordination in the lane-change process.

The recent progress of advanced vehicle control systems presents a great opportunity for the application of model predictive control (MPC) in the automotive industry. However, high computational complexity inherently associated with the receding horizon optimization must be addressed to achieve real-time implementation. This paper presents a generic scale reduction framework to reduce the online computational burden of MPC controllers. A lower dimensional MPC algorithm is formulated by combining an existing “move blocking ” strategy with a “constraint-set compression” strategy, which is proposed to further reduce the problem scale by partially relaxing inequality constraints in the prediction horizon. The closed-loop stability is guaranteed by adding terminal zero-state constraint. The tradeoff between control optimality and computational intensity is achieved by proper design of the blocking and compression matrices. The fast algorithm has been applied on intelligent vehicular longitudinal automation, implemented as a fuel economy-oriented adaptive cruise controller and experimentally evaluated by a series of real-time simulations and field tests. These results indicate that the proposed method significantly improves the computational speed while maintaining satisfactory control optimality without sacrificing the desired performance.

Adaptive cruise control (ACC) of hybrid electric vehicles (HEVs) has been traditionally developed without an efficient integration with active safety and energy management systems of hybrid power-trains, mainly for facilitating its implementation. This, however, leads to a compromise in the fuel economy of HEVs, since the predictive driving information provided by ACC is not exploited by the energy management system. In order to enhance the energy efficiency and control system integration, a novel ACC system for intelligent HEVs (i-HEV ACC) is developed in this study. The controller is proposed within the framework of nonlinear model predictive control, and a position-based nonlinear longitudinal intervehicle dynamics model is developed. A coordinated optimal control problem for both the tracking safety and the fuel consumption is formulated subject to the constraints on stable tracking. A multistep offline dynamic programming optimization and an online lookup table are used to implement the real-time control algorithm. Experiments are further conducted, which demonstrate that the proposed i-HEV ACC achieves enhanced performance and cooperation in traffic safety, fuel efficiency, and ride comfort.

A driver intention recognition method designed for lateral driving assistance systems is proposed based on comprehensive lane-change environment assessment. A new symbol, Comprehensive Decision Index is designed using fuzzy method to assess the influence of surrounding traffic environment on drivers' lane-change decisions. Meanwhile, Hidden Markov Model is applied to recognize driver intention. In the model structure, multiple observation variables are used and the elements' density functions in observation matrix are given the form of Gaussian 3-component mixture model. Finally, data of lane changes performed on driving simulator are used to testify the performance of the proposed method. The results show that the Comprehensive Decision Index is able to make an effective assessment on the environment's influence on drivers' lane-change decisions and the algorithm using it as one of the observation signals can both guarantee the accuracy of recognition results and improve the real-time performance.

A prototype of a longitudinal driving-assistance system, which is adaptive to driver behavior, is developed. Its functions include adaptive cruise control and forward collision warning/avoidance. The research data came from driver car-following tests in real traffic environments. Based on the data analysis, a driver model imitating the driver's operation is established to generate the desired throttle depression and braking pressure. Algorithms for collision warning and automatic braking activation are designed based on the driver's pedal deflection timing during approach (gap closing). A self-learning algorithm for driver characteristics is proposed based on the recursive least-square method with a forgetting factor. Using this algorithm, the parameters of the driver model can be identified from the data in the manual operation phase, and the identification result is applied during the automatic control phase in real time. A test bed with an electronic throttle and an electrohydraulic brake actuator is developed for system validation. The experimental results show that the self-learning algorithm is effective and that the system can, to some extent, adapt to individual characteristics.

An adaptive cruise control system of electric vehicles is proposed, considering both fuel economy and tracking safety with model predictive control theory. Firstly, the mathematical relationship between fuel cost and longitudinal acceleration is analyzed through a simulation model. Secondly the 2-norm number is adopted to indicate the integrated cost function, which integrates economy performance and tracking performance together. Finally the proposed optimization problem is solved by model predictive control theory, and a contrast controller is built with linear quadratic algorithm. Both simulation and real vehicle test results show that the MPC controller can reduce fuel cost by above 5% than LQ controller in the range of safe tracking, and it successfully coordinates fuel economy and tracking safety.

The concept of an intelligent environment-friendly vehicle (i-EFV) is proposed in this paper. It integrates three components, i.e., clean-energy powertrain, electrified chassis, and intelligent information interaction devices. By employing such technologies as structure sharing, data fusion, and control coordination, more comprehensive performances are achievable, in terms of traffic safety, fuel efficiency, and environmental protection. Based on its definition and configuration, some key technologies, including design for resource effectiveness, driving environment identification, and coordinated control, are studied. As a basic application, a platform of an intelligent hybrid electric vehicle (i-HEV), which incorporates a hybrid powertrain with adaptive cruise control, has been designed and implemented. Both simulation and experimental results demonstrated that the i-EFV performed better than a conventional vehicle.

A target selection method based on multi features fusion is proposed to improve the accuracy of target vehicle selection. The parameters consisting of the longitudinal distance, lateral distance, relative speed between objects and the host vehicle, the in-lane probability of objects are regarded as the features of individual vehicles. Firstly, some pre-processes of features data are carried out including Distance Compensation Factor (DCF) correcting and Kalman filtering, which are used to correct the in-lane probability data provided by lidar, track and predict the relative distance and speed of objects to lower the missing rate of vehicle detection respectively. Furthermore a two-layer BP neural network is designed to train the sample data and obtain the importance weight of feature variables; the training output is finally utilized as the index for target recognition. The selection method utilizes the valid information collected by sensors through the fusion of multi vehicle features. Experiments show that the vehicle detection results can be improved and the target selection and tracking accurately can be fulfilled through the proposed method. Even under cut-in conditions, the target can also be switched to the cut-in vehicle in time.