Global vehicle positioning through real-time LiDAR perception matched with online digital maps like OpenStreetMap (OSM) offers a promising solution to reduce reliance on high-quality maps in urban scenarios. However, the accuracy of global positioning based on LiDAR–OSM matching suffers from imprecise planar spatial information in OSM and frame-to-frame inconsistencies with the road network, which limits LiDAR–OSM matching to serving only as global position initialization rather than continuous positioning. To extend the application scenarios and performance of global positioning using OSM, a LiDAR–OSM matching framework is proposed to solve the planar association problem between real-time local perception and corresponding global position initialization sequences. Specifically, the motion consistency assumption is used to extract and reconstruct global candidate sequences that satisfy temporal and spatial continuity, which serves as the foundation for sequence-to-sequence matching with the local odometry. Furthermore, an online sequence-to-sequence transformation matrix estimation and verification mechanism is proposed to obtain a local-to-global solution that is subject to the activity range of vehicles and achieves optimal consistency with the road network. The performance of the proposed method is validated under different odometries and global initialization position confidence in the KITTI dataset. The results demonstrate that the proposed method achieves sub-meter level precision, showing a significant improvement over other LiDAR–OSM positioning methods in most urban sequences.

This paper presents a systematic investigation into path planning and trajectory tracking for autonomous vehicles. By integrating an improved A* algorithm, a fuzzy dynamic window approach, and a Fuzzy PID control strategy, the proposed method enables effective driving of an autonomous vehicle. Firstly, in the global path planning phase, to address the issues of low computational efficiency and suboptimal path quality in traditional A* algorithms for large-scale map searches, an improved A* algorithm incorporating an enhanced heuristic function, redundant node removal strategy, and path smoothing approach is introduced, significantly increasing search efficiency and optimizing path quality. Secondly, in the local path planning phase, the dynamic adjustment of vehicle speed and steering is achieved by combining fuzzy logic control with the dynamic window approach. This allows for smooth obstacle avoidance in dynamic environments. Furthermore, a path smoothing algorithm is integrated to refine the generated trajectory, ensuring its continuity and smoothness. Finally, a Fuzzy PID control algorithm is integrated into the trajectory tracking controller. By introducing fuzzy logic, the PID parameters are adaptively adjusted to ensure precise vehicle following of the planned path, improving path tracking stability and response speed. The proposed method is validated and evaluated in a variety of complex road scenarios using a real vehicle based on ROS. The simulation and real-world experimental results clearly illustrate that the proposed method achieves substantially better performance than conventional approaches with regard to path planning efficiency, obstacle avoidance success rate, and path smoothness.

Autonomous vehicles (AVs) promise substantial gains in safety, reliability, and decarbonization, yet safe and efficient interaction in dynamic, heterogeneous traffic remains a key barrier to large-scale deployment. Deep reinforcement learning (DRL) has emerged as a data-driven approach for learning adaptive decision policies that handle complex, unpredictable environments better than rule-based methods. However, different scenarios impose distinct requirements, necessitating scenario-specific algorithms. This survey systematically reviews DRL for four typical scenarios (highways, on-ramp merging, roundabouts, and unsignalized intersections), summarizes road features and recent advances, and evaluates methods using five criteria: driving safety, driving efficiency, training efficiency, unselfishness, and interpretability (DDTUI). Each DDTUI criterion is analyzed with respect to the reviewed algorithms. In addition, a dedicated scenario-centric learning transferability analysis is introduced that systematically evaluates whether each reviewed method demonstrates scene-specific learning improvements and assesses how effectively their designs transfer across the four scenarios. Finally, the challenges for future DRL-based decision-making algorithms are summarized.

This study presents a novel Multi-Agent Reinforcement Learning (MURL) architecture for autonomous vehicle (AV) navigation in complex urban traffic environments. By integrating a Social Value Orientation (SVO) model into a model-free SARSA reinforcement learning framework, our approach effectively balances individual agents’ social preferences with safety and performance objectives. A logistic regression-based risk assessment module evaluates collision probabilities in real time by analyzing spatiotemporal dynamics such as distances and velocities. Additionally, a dynamic optimizer adapts the learning rate and exploration strategies of the SARSA algorithm to provide efficient convergence to optimal policies. Extensive simulation experiments demonstrate that the proposed method significantly enhances safety and efficiency, achieving a 55.6% reduction in collision risk and increasing average rewards per episode by 2.1 compared to traditional SARSA without SVO. Furthermore, the optimized policy reduces average episode length, indicating the framework’s effectiveness in providing robust decision-making and adaptability across various traffic scenarios.

In this paper, a novel approach is presented for modeling the interaction dynamics between an ego car and a bicycle in a traffic scenario using a hybrid reinforcement learning framework combined with a social value orientation (SVO) model. The proposed framework leverages the SARSA algorithm to learn the optimal policy for the ego vehicle while incorporating risk cost as the negative log-likelihood of collision. Additionally, a customized SVO model is introduced to capture the social preferences of the ego car and the bicycle, defining the SVO of each agent as a continuous variable between egoistic and cooperative orientations. Furthermore, a weight parameter is incorporated in the framework to regulate the influence of the SVO model on the learning process. We demonstrate the effectiveness of our approach through extensive simulations, showing that the ego car can balance between maximizing its reward and avoiding collisions while considering the social preferences of the agents. The obtained results are compared to other models in the literature, and it is shown that the proposed method contributes to the development of safe and efficient autonomous driving systems that interact with human-driven vehicles in a socially intelligent mannerNote to Practitioners—This proposed framework is motivated by the pressing challenge of navigation for autonomous cars in complex urban driving scenarios and mixed traffic situations. With the increasing prevalence of autonomous vehicles on roads, developing intelligent navigation systems that can effectively interact with other road users has become essential. Our novel framework addresses this need by leveraging the SARSA algorithm to learn the optimal policy for the ego vehicle while incorporating risk cost as the negative log-likelihood of collision. Additionally, a customized SVO model is introduced to capture the social preferences of the ego car and the bicycle, defining the SVO of each agent as a continuous variable between egoistic and cooperative orientations. This enables autonomous vehicles to make informed decisions and navigate safely and efficiently. Our framework can enormously help the field of autonomous vehicle navigation and contribute significantly to developing safe, human-centric, and reliable transportation systems. The versatility of our approach is evident in its potential to support a network of autonomous vehicles interacting with multiple road users, thereby enhancing scalability. By leveraging the power of machine learning, our solution provides a robust and adaptable approach that can handle the diverse and ever-changing conditions of urban driving scenarios.

This article addresses the issue of networked lane keeping security control for autonomous vehicles subject to aperiodic controller-targeted denial-of-service (DoS) attacks, taking into account time-varying driving speed and nonlinear tire cornering stiffness. To accurately estimate the incompletely measured states and capture the dynamic behaviors appearing at the end of aperiodic DoS attacks, full state gain adjustable switching observer is established for the fuzzy vehicle-road integrated dynamic systems obtained via the tensor product model transformation method. In order to ensure the control quality and simultaneously save the communication resource, new resilient adaptive event-triggered scheme is proposed with a reinforcement learning-based intelligent optimal threshold regulation mechanism based on observed states. Then, an augmented observer-based fuzzy switching system is constructed using the time delay method. In addition, sufficient conditions are established to guarantee global exponential stability of the closed-loop nonlinear lane keeping system with prescribed H∞ performance using a piecewise Lyapunov functional analysis approach. Subsequently, the gains for controller, observer, and trigger are co-designed and computed by solving certain matrix inequalities. Finally, the effectiveness of the proposed security control method is demonstrated through typical maneuver scenario in terms of reasonable triggered times, better tracking performances and acceptable lateral dynamics.

Human-involved interactive environments pose significant challenges for autonomous vehicle decision-making processes due to the complexity and uncertainty of human behavior. It is crucial to develop an explainable and trustworthy decision-making system for autonomous vehicles interacting with pedestrians. Previous studies often used traditional game theory to describe interactions for its interpretability. However, it assumes complete human rationality and unlimited reasoning abilities, which is unrealistic. To solve this limitation and improve model accuracy, this paper proposes a novel framework that integrates the partially observable markov decision process with behavioral game theory to dynamically model AV-pedestrian interactions at the unsignalized intersection. Both the AV and the pedestrian are modeled as dynamic-belief-induced quantal cognitive hierarchy (DB-QCH) models, considering human reasoning limitations and bounded rationality in the decision-making process. In addition, a dynamic belief updating mechanism allows the AV to update its understanding of the opponent’s rationality degree in real-time based on observed behaviors and adapt its strategies accordingly. The analysis results indicate that our models effectively simulate vehicle-pedestrian interactions and our proposed AV decision-making approach performs well in safety, efficiency, and smoothness. It captures key patterns of the driving behavior operated by real human drivers in virtual reality(VR) experiments and even achieves more comfortable navigation compared to our previous VR experimental data.

As autonomous driving technology advances, automated vehicles (AVs) will increasingly share road space with pedestrians, creating significant challenges for AV systems. Effective interaction between AVs and pedestrians is one of the key hurdles. Pedestrian simulation tools offer the potential to expedite the evaluation and refinement of these interactive capabilities. However, existing research lacks efforts to model pedestrian behavior in vehicle-yielding scenarios, resulting in distorted modeling results. This paper proposes a perceptually plausible road-crossing decision model that creates temporal-dynamic crossing decisions across a range of vehicle-yielding scenarios. Specifically, a proposed hybrid perception strategy explains how pedestrians may apply psychophysical cues to make crossing decisions. Discrete choice models based on the hybrid perception strategy combined with a crossing initiation model reproduce the details of crossing decisions: the decision and its timing. An empirical dataset collected in a pedestrian simulator is applied to validate the model. Additionally, the latest crossing decision models, i.e., the evidence accumulation model and the artificial neural networks approach, are employed as comparisons. The results show that the proposed model accurately reproduces crossing decision patterns affected by diverse vehicle kinematics in vehicle-yielding scenarios in a perceptually plausible manner. Our results strengthen the notion that there is a perceptual threshold for pedestrians to control their decision-making strategy. The proposed theory and approach bring insights into the computational pedestrian road-crossing behavior and have practical implications in traffic simulation and AV development.

This study develops novel event triggered finite-time adaptive sliding-mode coordinated control method for networked hysteretic leaf spring suspension system subject to limited bandwidth, uncertainty sprung mass, dynamic constraints, and multiple control objectives. For inherent contradiction between body vibration and suspension chatter space, nonlinear filtering coordinated strategy with variable cut-off frequency is tailored to synthesize controlled variable composed of body and filtered tire displacements to realize smooth switch between hard and soft suspension under large/small suspension deformation. For the transmission congestion of control signals, event-triggered scheme relying on active force is designed to flexibly regulate command release interval to save communication resource and guarantee vibration suppression capability under abrupt road disturbance. To further improve convergence abilities of encapsulated variable and mitigate deterioration of performances from sparse control command, finite-time prescribed performance control is proposed to ensure transient and steady-state performance by accelerating convergence of controlled error to small region within preset time. For reconstructed active suspension system with unknown body weight, step control inputs with estimated weight are generated by employing event-based adaptive sliding-mode control to improve ride comfort and enhance handling properties. Additionally, global stability of networked active suspension system is proved by Lyapunov theory. Finally, the effectiveness and benefits of the proposed control method in real leaf spring suspension system are demonstrated by the simulation and hardware-in-the-loop tests.

The advancement of automated driving technology has led to new challenges in the interaction between automated vehicles and human road users. However, there is currently no complete theory that explains how human road users interact with vehicles, and studying them in real-world settings is often unsafe and time-consuming. This study proposes a 3D Virtual Reality (VR) framework for studying how pedestrians interact with human-driven vehicles. The framework uses VR technology to collect data in a safe and cost-effective way, and deep learning methods are used to predict pedestrian trajectories. Specifically, graph neural networks have been used to model pedestrian future trajectories and the probability of crossing the road. The results of this study show that the proposed framework can be for collecting high-quality data on pedestrian-vehicle interactions in a safe and efficient manner. The data can then be used to develop new theories of human-vehicle interaction and aid the Autonomous Vehicles research.

Increasing attention has been drawn to computational pedestrian behavior models aimed at understanding the interaction mechanisms between pedestrians and vehicles. Nevertheless, existing research lacks exploration of the underlying behavioral mechanisms of pedestrian crossing decisions, which leads to unrealistic modeling results. In particular, when dealing with continuous traffic flow scenarios, the concept of waiting time is frequently used to account for all intricate traffic flow effects. Moreover, very few studies considered the time-dynamic nature of crossing decisions. To address these research limitations, this study deconstructs pedestrian crossing decisions at uncontrolled intersections with continuous traffic flow through a cognitive process and proposes an anthropomorphic crossing decision model. Specifically, we propose a novel visual collision cue-based crossing decision-initiation model to characterize time-dynamic crossing decisions. In light of the risk-aversion theory, a traffic gap comparison strategy is put forward to explain and model pedestrian waiting behavior in traffic flow. Two datasets collected from a CAVE-based immersive pedestrian simulator are applied to calibrate and validate the model. The proposed model accurately predicts pedestrian crossing decisions across all traffic scenarios. The modeling performance is significantly enhanced by considering the proposed traffic gap comparison strategy. Moreover, the model accurately captures the timing of crossing decisions. This work concisely demonstrates how pedestrians dynamically adapt their crossings in continuous traffic based on visual collision cues, potentially offering insights into modeling pedestrian-vehicle interactions or serving as a tool to realize anthropomorphic pedestrian crossing decisions in simulators.

Although artificial intelligence has made tremendous progress recently, there remain various technical issues and ethical problems before autonomous vehicles can be available to the general public. In the interim, collaboration between automated systems and human drivers becomes a promising solution, where the merits of machine intelligence and human intelligence are blended in a complementary way. To this end, the paper proposes a reference-free human-vehicle shared control framework based on reinforcement learning. Firstly, a personalized human-like driver agent is derived from highway driving data by means of generative adversarial imitation learning integrated with Gaussian mixture model. The driver model is responsible for real-time interaction with the reinforcement learning agent to relieve the burden of human operators in the course of training. Then, a copilot agent learns the policies to cooperatively control the vehicle steering based on three distinct implementations for the search of the best coordination strategy. Heuristic reward functions are designed to guide the agent policy optimization for multi-objective equilibrium between driver synchronization against intervention. To verify the control performance of the proposed shared driving system, simulation experiments with driver models and human-in-the-loop tests with real-life participants are conducted in the end of this paper. The results demonstrate that the shared steering control method can effectively follow human intentions, facilitate driving goals, improve road safety and reduce driver’s workload simultaneously in the challenging dynamic obstacle avoidance scenarios.

As one of the alternatives to conventional fuel vehicles, hybrid electric vehicles (HEV) offer lower fuel consumption and fewer exhaust emissions. To improve the performance of the HEV, the energy management strategy (EMS) is one of the most critical technologies. Classic EMS can be broadly classified into rule-based and optimization-based. With the development of machine learning technology, the deep reinforcement learning (DRL) algorithm of intelligent learning algorithms has been applied to the EMS. This paper mainly reviews the research progress of the EMS based on DRL from two aspects of the algorithm and training environment, and the EMS research involving combining the intelligent transportation system (ITS) is reviewed. In addition, the experimental test progress situations of DRL-based EMS research are discussed. Finally, the challenge of DRL-based EMSs is analyzed and some solutions are provided. In particular, it also involves some discussion about automotive cyber security in the intelligent transportation environment.

This paper presents a robust finite frequency${H} _{\infty }$control strategy for improving vibration performance and ride comfort of electric vehicles through in-wheel motor-active suspension system(IWM-ASS). Since the human body is much sensitive to the vertical vibration of 4 -8 Hz, the main objective is dedicated to deal with the vibration challenge that matches the characteristics of the human body by applying the finite-frequency technique. Firstly, the uncertain quarter-vehicle active suspension model with dynamic damping in-wheel motor driven system is established, in which in-wheel motor is suspended as dynamic vibration absorber(DVA) to isolate the force transmitted to motor bearing in IWM-ASS. Based on the framework of generalized Kalman–Yakubovich–Popov lemma and stability theory, then the performance index of${H} _{\infty }$norm from external disturbance to controlled output for IWM-ASS is attenuated within the concerned frequency range while other system requirements such as parameter uncertainty, suspension deflection constraint and actuator saturation are also guaranteed in controller design. The resulting robust finite frequency state feedback${H} _{\infty }$controller is finally designed utilizing two new theorems, and solved via a set of linear matrix inequalities. Simulations for frequency-domain and time-domain responses are implemented and compared with the entire frequency control method to evaluate the effectiveness of the proposed strategy. It can be concluded from the results that the developed control strategy can effectively attenuate the negative vibration and enhance ride comfort and road-holding ability for electric vehicles of IWM-ASS.

A timely understanding of target vehicles (TVs) lateral behavior is essential for the decision-making and control of host vehicle. Existing physical model-based methods such as motion-based method and multiple centerline-based method are generally constructed based on TV pose and longitudinal velocity, and tend to ignore TV preview driving characteristic and other useful information such as lateral velocity and yaw rate. To address these issues, a driver preview and multiple centerline model-based probabilistic behavior recognition architecture is proposed for timely and accurate TV lateral behavior prediction. Firstly, a driver preview model is used to describe vehicle preview driving characteristic, and TV preview lateral offset and preview lateral velocity are calculated with TV states and road reference information. Then, the preview lateral offset and preview lateral velocity are combined with multiple centerline model for TV lateral behavior reasoning based on the interacting multiple model-based probabilistic behavior recognition algorithm. With this method, TV preview driving characteristic and lateral motion states are combined for precise TV lateral behavior description. Furthermore, to predict short-term lateral behavior, a preview lateral velocity-dependent transition probability matrix model constructed with Gaussian cumulative distribution function is proposed. Simulation and experimental results show that the proposed method considering vehicle preview driving characteristic predicts TV lateral behavior earlier than the conventional method.

This paper proposes a novel robust path-following strategy for autonomous road vehicles based on type-2 fuzzy PID neural network (PIDT2FNN) method coupled to an Extended Kalman Filter-based Fuzzy Neural Network (EKFNN) observer. Uncertain Gaussian membership functions (MFs) are employed to self-adjust the universe of discourse for MFs using the adaptation mechanism derived from Lyapunov stability theory and Barbalat's lemma. External disturbances are significant in autonomous vehicles by changing the driving condition. Furthermore, parametric uncertainties related to the physical limits of tires and the change of the vehicle mass may significantly affect the desired performance of autonomous vehicles. The robustness of the proposed controller against the parametric uncertainties and external disturbances is compared with one active disturbance rejection control (ADRC) algorithm, and a linear-quadratic tracking (LQT) method. The obtained results in terms of the maximum error and root mean square error (RMSE), demonstrate the effectiveness of the proposed control algorithm to reach the minimized path-tracking error.

This article investigates the integrated lane-keeping and roll control for autonomous ground vehicles (AGVs) considering the transient performance and system disturbances. The robust integral of the sign of error (RISE) control strategy is proposed to achieve the lane-keeping control purpose with rollover prevention, by guaranteeing the asymptotic stability of the closed-loop system, attenuating systematic disturbances, and maintaining the controlled states within the prescribed performance boundaries. Three contributions have been made in this article: 1) a new prescribed performance function (PPF) that does not require accurate initial errors is proposed to guarantee the tracking errors restricted within the predefined asymptotic boundaries; 2) a modified neural network (NN) estimator which requires fewer adaptively updated parameters is proposed to approximate the unknown vertical dynamics; and 3) the improved RISE control based on PPF is proposed to achieve the integrated control objective, which analytically guarantees both the controller continuity and closed-loop system asymptotic stability by integrating the signum error function. The overall system stability is proved with the Lyapunov function. The controller effectiveness and robustness are finally verified by comparative simulations using two representative driving maneuvers, based on the high-fidelity CarSim-Simulink simulation.

Automated vehicle control requires full knowledge of motion behavior of the preceding target vehicles (PTVs), and the states such as longitudinal/lateral velocity and yaw rate are critical for the PTV behavior description. However, the PTV's lateral states estimation have seldom been addressed in the state-of-the-art literatures. Aimed at providing reliable PTV lateral states, this paper presents a novel combined model-based estimation scheme. Different from the conventional PTV models, the proposed model is constructed based on the host-target vehicle dynamics and road constraints. Specifically, steering angle of the PTV is included in the state vector. The measurements, such as heading angle, road curvature, and lateral distance to the lane center, are available from an onboard vision system. As a vision system inevitably has measurement delay, a modified Kalman filter is developed to address the sampling issue. To verify the proposed approach, hardware-in-the-loop experiments are conducted in designed testing scenarios.

Differential drive assistance steering (DDAS) is an emerging assisted steering mechanism in in-wheel-motor driven (IWMD) electric vehicles, yielded by the differential moment of the front tires in the steering system. DDAS can steer the front wheels when there is no steering power from the steering motor, and thus can be used as a redundant steering mechanism. To realize the yaw control when the active front steering entirely breaks down and guarantee the transient control performance therein, this paper proposes an integral sliding mode control (ISMC) approach for IWMD electric vehicles steered by DDAS. Two contributions are made in this paper: 1) An improved disturbance observer based ISMC strategy is designed to cope with the unknown mismatched disturbances, and the composite nonlinear feedback technique is employed to design the nominal part of the controller to restrain overshoots and remove steady-state errors considering the tire force saturations; 2) An adaptive super-twisting control approach is proposed to deal with the disturbances with unknown boundaries using a continuous controller while eliminating the chattering effect. The system stability and robustness are proved via Lyapunov approach. CarSim-Simulink simulation has verified the effectiveness of the proposed control approach in the case of the steering fault.