The application of object detection in industrial transportation has witnessed substantial advancements, yielding significant enhancements in both safety and efficiency. While Transformer-based detectors have demonstrated remarkable success in object detection for autonomous driving, their quadratic computational complexity and limited small-object perception capabilities remain significant challenges. To address these limitations, we propose lightweight visual Mamba small object detection (LVMSOD) method, a novel selective state space model designed for small object detection task. The proposed framework employs a multi-level cascade of dual-layer nested Mamba modules to comprehensively capture contextual information across different scales, coupled with a hierarchical feature fusion strategy to enhance multi-scale feature integration for improved small object detection. The LVMSOD framework incorporates two key components to enhance feature representation: Visual Mamba Detection (VDM) block that preserves fine-grained image details, and Lightweight Gated Multi-Layer Perceptron (LGMLP) designed to model local feature dependencies efficiently. Furthermore, we propose an optimized feature extraction mechanism employing depthwise and pointwise convolutions with distribution factors, significantly reducing computational overhead while maintaining detection accuracy. Comprehensive evaluations on benchmark datasets demonstrate the effectiveness of our approach, achieving [email protected] scores of 93.3% on KITTI and 45.3% on VisDrone while maintaining superior computational efficiency. These results highlight the potential of LVMSOD for efficient and accurate small object detection.

Autonomous on-ramp merging requires a safe and efficient decision and planning framework to navigate dynamic and complex traffic scenarios. Reinforcement Learning (RL) offers adaptability in such environments but struggles to ensure safety in unseen situations, whereas Model Predictive Control (MPC) can enforce safety constraints but relies on accurate models. To leverage the strengths of both, we propose a hierarchical Safety-Augmented RL and MPC integration approach, i.e., SARMI, for decision and planning in the on-ramp merging scenario. The high-level decision-making layer employs a discrete Soft Actor-Critic (SAC-Discrete) algorithm enhanced with the Augmented Lagrangian method to generate actions. MPC generates reference trajectories based on RL actions and feeds predicted states back to RL for reward and cost design, ensuring consistency between RL and MPC. Our key innovations include: i) incorporating a Gaussian-based risk field model into the cost design of constrained RL, which quantifies collision risk based on MPC-predicted states, enabling agents to make proactive decisions; ii) an Augmented Lagrangian SAC-Discrete method with barrier-like quadratic penalties to promote compliance with safety constraints and alleviate oscillations in dual gradient descent; iii) theoretical analysis proving the equivalence between the optimal solutions of the primal and dual problems in Augmented Lagrangian SAC-Discrete and iv) a dual safety mechanism combining action masking (to filter invalid actions) and action shielding (to replace unsafe actions in RL), enhancing safety during the exploration and execution stages, respectively. Experiments demonstrate the superiority of our method over the baseline SAC-Discrete, showing improved safety and efficiency.

Four-wheel independent steering (4WIS) vehicles have attracted increasing attention for their superior maneuverability. Human drivers typically choose to cross or drive over low-profile obstacles (e.g., plastic bags) to efficiently navigate through narrow spaces, while existing planners neglect obstacle attributes, leading to suboptimal efficiency or planning failures. To address this issue, we propose a novel multimodal trajectory planning framework that employs a neural network for scene perception, integrates 4WIS hybrid A* search to generate a warm start, and formulates an optimal control problem (OCP) for trajectory optimization. Specifically, a multimodal perception network fusing visual information and vehicle states is employed to capture semantic and contextual scene information, enabling the planner to adapt the strategy according to scene complexity (hard or easy planning task). For hard tasks, guided points are introduced to decompose complex tasks into local subtasks, improving search efficiency. The multiple steering modes of 4WIS vehicles—Ackermann, diagonal, and zero-turn—are also incorporated as kinematically feasible motion primitives. Moreover, a hierarchical obstacle handling strategy, which categorizes obstacles as “non-traversable”, “crossable”, and “drive-over”, is incorporated into the node expansion process, explicitly linking obstacle attributes to planning actions to enable efficient decisionmaking. Furthermore, to address dynamic obstacles with motion uncertainty, we introduce a probabilistic risk field model, constructing risk-aware driving corridors that serve as linear collision constraints in the OCP. Experimental results demonstrate the proposed framework’s effectiveness in generating safe, efficient, and smooth trajectories for 4WIS vehicles, especially in constrained environments.

LiDAR-based multi-task perception for autonomous driving requires efficient integration of spatial context and cross-task information, yet existing methods often suffer from restricted receptive fields and suboptimal task interaction. This paper presents a novel multi-task framework that goes beyond sparsity constraints, leveraging receptive field expansion and cross-task fusion to enhance 3D object detection and semantic segmentation. We introduce the Spatial Density-Invariant Multi-scale Integrator (SDIMI), which adaptively fuses multi-resolution contextual features using density-agnostic strategies to expand the receptive field while preserving feature sparsity. Additionally, the Synergistic Instance-Driven Multitask Fusion (SIDMF) module dynamically aligns instance-level features between segmentation and detection, enabling efficient high-level feature propagation via bounding box mapping masks. Experiments on NuScenes and Waymo Open Dataset demonstrate state-of-the-art performance: our method achieves 72.0% NuScenes Detection Score (NDS) and 84.4% mIOU on NuScenes, and 79.8% mAPH-L2 and 72.3% mIOU on Waymo.

In this study, a novel learning-based model predictive control (LMPC) framework that uses a Lipschitz interpolation (LI)-based nonparametric estimation method is developed for the optimal control of a partially unknown Lipschitz discrete system subject to noise and constraints. This work focuses on the design and theoretical analysis of an online model learning method and an event-triggered constraint handling strategy. First, a sampled data set construction mechanism for an LI-based prediction function is developed to ensure a bounded estimation deviation with low computational complexity. Second, a relaxed barrier function is introduced as a safe control strategy to address the hard state constraint. To overcome the contradiction between the satisfaction of state constraints and the efficient online solving of the optimal control problem, a model confidence-based triggering mechanism is investigated to reduce redundant execution of the safety control strategy. Finally, numerical simulations are conducted to verify the effectiveness of the proposed LMPC framework.

Predicting pedestrian trajectories in complex environments presents a significant challenge for autonomous driving systems, primarily due to the intricate social interactions among pedestrians and their surrounding groups. Existing methods often struggle to fully capture the impact of group behavior on individual movement. To address these limitations, we propose the Multi-scale Group Interaction Transformer (MOAT) for pedestrian trajectory prediction in high-density, complex scenarios. Our approach introduces a dynamic group interaction module (DGIM) that clusters pedestrians based on proximity, determined by a distance matrix of neighboring pedestrians. In constructing interaction representations, we go beyond traditional features like speed, distance, and direction by incorporating crowd density, thus providing a more comprehensive understanding of group dynamics. To effectively process these features, we employ a multi-branch attention fusion (MBAF) module, which independently analyzes each feature set to capture the unique dynamics and density characteristics of each group and their varying effects on the target pedestrian. These spatial features are then combined with temporal information, allowing our model to account for both spatial and temporal dependencies. Additionally, we leverage a multi-scale Transformer to adaptively partition input trajectories, enhancing the model’s ability to capture dynamic patterns across various scales. Extensive evaluations on benchmark datasets show that our approach consistently outperforms state-of-the-art methods in terms of both prediction accuracy and robustness.

Switching communication topologies can cause instability in vehicle platoons, as vehicle information may be lost during the dynamic switching process. This highlights the need to design a controller capable of maintaining the stability of vehicle platoons under dynamically changing topologies. However, capturing the dynamic characteristics of switching topologies and obtaining complete vehicle information for controller design while ensuring stability remains a significant challenge. In this study, we propose an observer-based distributed model predictive control (DMPC) method for vehicle platoons under directed Markovian switching topologies. Considering the stochastic nature of the switching topologies, we model the directed switching communication topologies using a continuous-time Markov chain. To obtain the leader vehicle's information for controller design, we develop a fully distributed adaptive observer that can quickly adapt to the randomly switching topologies, ensuring that the observed information is not affected by the dynamic topology switches. Additionally, a sufficient condition is derived to guarantee the mean-square stability of the observer. Furthermore, we construct the DMPC terminal update law based on the observer and formulate a string stability constraint based on the observed information. Numerical simulations demonstrate that our method can reduce tracking errors while ensuring string stability.

Accurate pedestrian trajectory prediction is a crucial task for ensuring the safety of autonomous driving. However, most of the existing methods only model pedestrian trajectories in the spatial-temporal domain, which results in a lack of analysis of motion at different scales. In this work, we propose a framework based on wavelet transform and diffusion model, which is called DiffWT. Different from previous approaches, our method employs a discrete wavelet transform (DWT) to perform time-frequency analysis of trajectories. The high-frequency component of the DWT indicates local motion details of the trajectory, while the low-frequency one represents overall motion trends. Second, we propose a trajectory decoder comprising a conditional diffusion model and a cross-constrained bidirectional trajectory generator (C2Bid). The diffusion model generates the distribution of implicit pedestrian behaviors by taking the multiscale motion behaviors as conditions. Furthermore, the C2Bid module is designed as a cross-constrained bidirectional structure to decode behavioral distribution into multimodal trajectories. This trajectory decoder can generate precise distribution of trajectories and reduce accumulation of prediction errors. Extensive experimental results on the ETH/UCY and the Stanford drone datasets (SDDs) demonstrate that our method achieves better performance as well as higher efficiency compared to other state-of-the-art approaches.

In this paper, a novel adaptive dynamic programming (ADP)-based optimal control method is developed for discrete-time systems subject to constraints and disturbances. Particularly, a safe policy iteration scheme is designed to handle state and input constraints, including both hard and soft constraints, by converting the original policy improvement strategy into a constrained optimization problem with a prescribed state cost function. After that, an actor-critic-disturbance framework is introduced to address the constrained optimal control problem. The robust safety against disturbances is treated as a two-player zero-sum game, where the actor and disturbance neural networks are used to approximate the optimal control input and the disturbance policy, respectively. The convergence property of the proposed algorithm is analyzed, and the multi-step version of the proposed ADP scheme is derived based on this property. Simulation results are demonstrated and discussed to validate the effectiveness and performance of the proposed method.Note to Practitioners—Addressing constraints in optimal control problems is essential for guaranteeing the safe operation of controlled systems. However, conventional ADP algorithms struggle to simultaneously manage state and control input constraints during the search for the optimal solution. In real-world applications, another critical and common issue is the presence of external disturbances, where disturbances that cause the control object to deviate from the safe region must be constrained while seeking an optimal control policy. Bearing these factors in mind, this study presents a novel ADP scheme for solving optimal control problems of discrete-time systems, taking into account state and control constraints as well as the impact of disturbances. Moreover, the convergence analysis of the proposed SADP scheme is provided, offering a powerful theoretical foundation for guaranteeing the safety and feasibility of the controlled system during operation.

This article addresses the cooperative tracking control problem for heterogeneous multiple marine vehicles with a nonautonomous leader. A fully distributed smooth observer is proposed to estimate the trajectory of the leader, mitigating the influence of its control input. Based on the observer, three decentralized adaptive fuzzy event-based controllers are designed with distinct triggering strategies, i.e., fixed, relative, and switching threshold triggering strategies, which utilize fuzzy-logic systems and event-triggering mechanisms to address the challenge of model uncertainties and communication constraints of marine vehicles. The proposed methods ensure the zero-error tracking without Zeno behavior, as demonstrated through Lyapunov analysis. Numerical simulations validate the effectiveness of the proposed approaches.

Limited transmission bandwidth and actuator faults in intelligent and connected vehicles (ICVs) pose significant challenges to controller design. To address these issues, this paper proposes a transmission-efficient fault-tolerant control strategy for a platoon of ICVs. Adaptive laws are developed to estimate the bounds of unknown fault characteristics, which eliminates the need for prior fault knowledge. A quantized event-triggered (QET) mechanism that integrates a hysteresis quantizer and a dynamic event-triggered mechanism (DETM) is designed to improve signal transmission efficiency. By appropriately designing the dynamic variable in the DETM, the Zeno behavior is avoided. Furthermore, with the aid of smooth functions, an adaptive controller is constructed to address the impact of unknown actuator faults, as well as signal deviations induced by the QET mechanism. Simulation results in both representative and real-world driving scenarios demonstrate that the proposed strategy significantly reduces communication burden while ensuring robustness against unknown actuator faults.

The emerging vehicle-to-vehicle communication technique enables vehicle platoon control, which greatly increases road throughput and travel efficiency. For cybernetically connected vehicles (CVs) with dynamics heterogeneities subject to actuator uncertainties, a dynamic event-triggered platoon control protocol is proposed in this paper to significantly reduce communication overheads. By considering the platoon problem of CVs as an output tracking consensus problem of heterogeneous multi-agent systems (MASs), a hierarchical control framework composed of an upper-level interactive event-triggered observer layer and a lower-level local tracking controller layer is established. Within the proposed framework, considering a virtual dynamic leader with non-zero inputs and external disturbances, a distributed observer is first designed for each following vehicle to observe the leader in an event-triggered manner. An internal dynamic variable is introduced to construct the dynamic triggering law, which not only relaxes the requirement on continuous state transmission in triggering detection but also benefits the exclusion of the Zeno behavior. Then, the solutions of a set of regulator equations associated with the proposed observer and the observer itself are integrated into the local tracking controller design to guarantee the tracking ability of each follower agent to its own observer. As thus, an event-triggered platoon control mechanism of heterogeneous CVs is proposed. Simulations and experiments on unmanned ground vehicles (UGVs) are conducted, which validate the effectiveness of the proposed platoon control method.

An active simultaneously transmitting and reflecting reconfigurable intelligent surface (ASTAR-RIS)-aided rate-splitting multiple access (RSMA) system is investigated in this paper. Specifically, a multi-antenna base station (BS) employs the RSMA protocol to communicate with a covert user and a public user with the help of an ASTAR-RIS in the presence of hardware impairments. For this setup, the outage probability (OP) and the detection error probability (DEP) of the warden are derived to characterize the covert communication performance. The covert transmission rate ($C_{R}$) in high signal-to-noise ratio (SNR) regions is also taken into consideration, for which an accurate approximate expression is presented. Based on the analytical results, the influences of the number of ASTAR-RIS elements, the RSMA factors, and the reflection coefficient of the ASTAR-RIS elements are analyzed. Finally, numeric simulations validate the correctness of the theoretical results presented in this paper.

This paper proposes a novel sub-polytope integrated approach (sPIA) that features an intrinsic bump-free transition, aiming to reduce conservatism in the design of path tracking control for autonomous vehicles with large range time-varying longitudinal velocity. The approach encapsulates the interdependent time-varying parameters associated with longitudinal velocity as a set of finite-vertex sub-polytopes interconnected via junction points, thereby reducing the conservatism induced by modeling overbounding. The integration of junction points and the formulation of sub-region activation rules provide a theoretical foundation for avoiding abrupt changes in feedback gains, ensuring a bump-free transition between sub-regions. A gain-scheduling state feedback controller is designed, employing parameter-dependent Lyapunov functions to further attenuate design conservatism. The effectiveness of the proposed method in reducing design conservatism is demonstrated by a comparative analysis of the optimal H∞performance indices across various sub-polytope integration schemes. Furthermore, the superiority of the method is exemplified via simulations within real-world driving scenarios, utilizing the high-fidelity CarSim-Simulink platform. The results indicate that the proposed sPIA outperforms traditional polytopic methods in path tracking performance. This improvement, together with the effective avoidance of bumps during sub-regional transitions, confirms the efficacy of the proposed approach. Moreover, the real-time performance of the method is verified by hardware-in-the-loop experiments.

The article investigates the decentralized periodic dynamic event-based load frequency control problem for a class of multiarea nonlinear power systems with uncertain parameters. For overcoming the limitations on the knowledge of studied power systems, the interval type-2 (IT2) fuzzy model is synthesized by using local linear models relevant to some operation points. Under the IT2 fuzzy framework, the decentralized periodic dynamic event-based fuzzy control law is proposed to reduce the bandwidth burden of communication networks. Based on the Lyapunov stability theory, a sufficient condition is presented such that closed-loop systems are exponentially stable with a given$H_{\infty }$performance. The existence condition of the controller gains and the triggering scheme's parameters is expressed in terms of matrix inequalities. The obtained results are extended to two situations, i.e., the decentralized periodic static event-based fuzzy control and the decentralized periodic sampling fuzzy control. Compared with the latter two control approaches, the developed decentralized periodic dynamic triggering strategy can provide the lowest communication frequency. Finally, the validity and superiority of the developed method are demonstrated by simulation results.

The tire-road friction coefficient (TRFC) is the critical parameter that significantly improves the control performance of distributed electric vehicles. Nonetheless, achieving precise TRFC estimation during straight stationary driving maneuvers, characterized by constant longitudinal speed (e.g., where the longitudinal acceleration is nearly zero) on a straight road, poses a particularly formidable challenge. In the paper, we propose a new learning strategy that leverages multi-domain fusion feature extraction in both the time domain and time-frequency domain to estimate the TRFC during straight stationary driving maneuvers. Specifically, the frequency response function of the in-wheel-motor-drive system first is inferred from the longitudinal dynamics model and single wheel dynamics model. Then, the input selection of learning strategy is determined through frequency response characteristics analysis and explainable dynamics mechanism. In addition, a parallel spatial-temporal convolutional neural network (PSTCNN) is built to extract features in both the time domain and in the time-frequency domain, respectively. Finally, the TRFC learning strategy is verified by experimental tests on different road surfaces. Our results demonstrate that the proposed methodology is capable of estimating the TRFC with a lower error than the traditional learning-based method and the classical slip-slope method.

In the foreseeable Intelligent Transportation System, Intelligent Connected Vehicles (ICVs) will play an important role in improving travel efficiency and safety. However, it is challenging for ICVs to support the resource-hungry autonomous driving applications due to the limitation of hardware computing power. Fortunately, the emergence of Multi-access Edge Computing helps overcome this limitation effectively. This paper addresses the vehicle-to-edge server computation offloading conundrum by optimizing the trade-offs in partial offloading and resource allocation. Proposing a distributed approach, this study confronts the multi-variable non-convex challenge directly by decoupling variables and deriving constraint-based bounds that guide the decisions for offloading and allocation. A novel low-complexity distributed algorithm is introduced that not only tends toward optimal but also demonstrates superior real-time applicability and efficiency, illustrated through enhanced performances both in simulated trials and genuine vehicular edge computing settings. The algorithm’s practical effectiveness addresses a notable gap between the theoretical models for computation offloading and actual real-life execution, reinforcing the soundness and relevance of the proposed method. Furthermore, its advanced integration with federated learning frameworks marks a leading-edge application, substantiating significant enhancements in computational efficiency and robustness.

This work deals with the Multi-Vehicle Trajectory Planning (MVTP) problem in unstructured conflict areas. Compared with MVTP on structured roads, the more complex intersection and the more conflicting motions of vehicles, set higher requirements for the real-time of the planner. To address the issue, this work proposes a centralized decision-making distributed planning framework, to generate trajectories for multiple vehicles navigating conflict areas on unstructured roads. In the multi-vehicle collaboration process, vehicles are assigned a priority upon entering the control area, with higher-priority vehicles treated as dynamic obstacles to be avoided. The distributed planning is divided into three stages: 1) trajectory search utilizing Velocity-Hybrid A* (V-Hybrid A*), 2) path optimization with respect to both dynamic and static obstacles, and 3) speed optimization leveraging the convex space created by the initial solution. In the trajectory search of individual vehicles, each expansion node of V-Hybrid A* is given an acceleration to get a coarse trajectory with discrete velocity. Moreover, a box constraint is established to optimize the path of the coarse trajectory. Then, according to the homotopy class provided by the coarse trajectory, speed optimization is carried out to obtain the final single vehicle trajectory. In our simulation experiments, we randomized the generation of numerous vehicles assigned with varied tasks to evaluate the algorithm’s effectiveness. Ultimately, real-vehicle experiments substantiated the algorithm’s practical feasibility. The results indicate that the proposed method can significantly improve the traffic efficiency in conflict areas and has practical applicability.

In this paper, we consider a simultaneously transmitting and reflecting reconfigurable intelligent surface (STARRIS)-assisted Internet of Vehicles non-orthogonal multiple access network. For practical considerations, the impacts of residual hardware impairments and imperfect channel state information are investigated. For such a setup with three different protocols, including time switching (TS), energy splitting, and mode switching (MS), we present the outage probability (OP) analysis for the network. Moreover, the probability of signal-to-noise ratio (SNR) gain and the delayed outage probability are further investigated. Compared with the traditional relaying scheme, the obtained results show that the STARRIS-assisted system achieves better delay performance. Furthermore, for the STARRIS-assisted system, the TS protocol achieves the best performance, while the MS protocol has the worst performance. In addition, considering the base station with multiple antennas, the expression for the OP is derived and error floors at high SNRs due to imperfect channel state information constraints exist. Finally, one can readily observe that increasing the number of STARRIS elements and antennas of the source can improve the outage performance.

In this article, an adaptive dynamic programming (ADP) scheme utilizing a costate function is proposed for optimal control of unknown discrete-time nonlinear systems. The state-action data are obtained by interacting with the environment under the iterative scheme without any model information. In contrast with the traditional ADP scheme, the collected data in the proposed algorithm are generated with different policies, which improves data utilization in the learning process. In order to approximate the cost function more accurately and to achieve a better policy improvement direction in the case of insufficient data, a separate costate network is introduced to approximate the costate function under the actor-critic framework, and the costate is utilized as supplement information to estimate the cost function more precisely. Furthermore, convergence properties of the proposed algorithm are analyzed to demonstrate that the costate function plays a positive role in the convergence process of the cost function based on the alternate iteration mode of the costate function and cost function under a mild assumption. The uniformly ultimately bounded (UUB) property of all the variables is proven by using the Lyapunov approach. Finally, two numerical examples are presented to demonstrate the effectiveness and computation efficiency of the proposed method.

Cooperation among multiple connected vehicles (CVs) brings swarm intelligence to our transportation systems and helps improve their performance. This study proposes a fuel economy optimization approach for a platooning vehicle swarm via distributed economic model predictive control (DEMPC). Within the DEMPC framework, each CV shares its assumed trajectory in the predictive horizon with its neighboring CVs at each control loop. With its neighbors’ and its own assumed trajectories, each CV first solves an open-loop control optimization problem for platoon formation, and then solves an open-loop economic optimization problem for direct fuel economy improvement. In particular, the optimal cost of the former optimization problem is used in the latter one to build an upperbound constraint for stability guarantee. The asymptotic convergence of assumed terminal states is given in the analysis, and the recursive feasibility of the two optimization problems are proved with an explicit constraint on the weight matrices in the open-loop control optimization problem. Based on these analyses, the asymptotic stability of the closed-loop system is finally proved through Lyapunov analysis. Numerical simulation results validate the effectiveness of the proposed approach in terms of closed-loop stability and fuel economy improvement. Note to Practitioners—This work aims to optimize the fuel economy for a swarm of vehicles running in a platoon. Existing approaches on platoon control mainly focus on the accurate tracking of the following vehicles, but this may cause aggressive control and hence lead to poor fuel economy. Therefore, this paper proposes a distributed economic model predictive control approach to explicitly optimize the fuel consumption rate of each vehicle. The proposed method can improve fuel economy and reduce communication requirements and burdens for platooning vehicle swarms. The paper assumes no communication time delay and packet drops, so the impact of unreliable communication will be studied in the future work.

Vehicle platoon control based on vehicle-to-vehicle (V2V) communication is one of the promising technologies to improve the performance of transportation systems. This paper presents a distributed controller to optimize a vehicle platoon’s fuel consumption by combining the switching feedback control and economic model predictive control (EMPC) methods. The closed-loop dynamics involving switching feedback gains with the constant time headway (CTH) policy are established firstly, and the multiple-predecessor following (MPF) communication topology is considered. In order to obtain the economy optimal feedback gain, we design a local optimal control problem for each vehicle, based on which the average dwell time is defined and the distributed EMPC algorithm is designed. Based on linear matrix inequalities (LMIs) and the Lyapunov theorem, the feedback gain selection method and the lower bound for average dwell time that guarantees asymptotic stability are analyzed rigorously. Then a modified algorithm that can ensure string stability is designed. Numerical simulations show a maximum of 6.84% fuel benefit compared with pure tracking-oriented methods.

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.

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.

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.