Motion control is a fundamental task in autonomous vehicle systems, and data-driven approaches offer the advantage of independence from explicit vehicle dynamics modeling. This paper presents a novel data-driven, model-free predictive control method that operates within a receding horizon framework, eliminating the need for pre-identified system models. First, we establish a signal-based system representation and provide a theoretical validation demonstrating its capability to encapsulate vehicle dynamics without requiring explicit model approximation. This representation is then seamlessly integrated into the predictive control framework, ensuring real-time adaptability to dynamic uncertainties. Unlike conventional methods, the proposed approach continuously updates vehicle dynamics using historical driving data, thereby enhancing robustness against time-varying parameters. Moreover, the method is designed to function effectively even when only partial state measurements are available, addressing a key challenge in real-world applications where full state observability is often impractical. To validate its performance, Rapid Control Prototype (RCP) experiments are conducted under both fully and partially measurable state conditions. Results confirm that the proposed approach achieves superior tracking performance compared to traditional model predictive control (MPC) methods, particularly in scenarios where vehicle state estimation is incomplete.

Conducting efficient and accurate safety evaluations within the context of naturalistic driving environments is critical in the design and large-scale deployment of Autonomous Vehicles (AV). However, existing closed box safety evaluation algorithms suffer from inefficiency and are challenged to achieve rapid exploration, precise identification of safety boundaries, and accurate estimation of the AV’s failure probability simultaneously. To address these issues, we propose a Trust Region Search-based Bayesian Safety Evaluation (TuRS-BSE) algorithm. It combines Gaussian Processes (GP), the logit function, and the logistic function to construct a surrogate model for the AV, enabling the prediction of test results under different test scenarios. It also introduces three acquisition functions to enable efficient and comprehensive exploration, precise identification of safety boundaries, and the generation of risk scenarios for the AV. Meanwhile, the Trust Region Search (TuRS) algorithm is used to solve for the optimal candidate test scenario iteratively, enhancing the search efficiency. Additionally, the innovative integration of likelihood weighting with an efficient surrogate model eliminates the dependency on parameterized proposal distributions, enabling efficient and accurate estimation of the AV’s failure probability. To validate the effectiveness of the proposed method, we evaluated the AV system in both 2-dimension and 4-dimension test scenarios. The results demonstrated that the proposed approach can get an unbiased estimation of the failure probability and improve the efficiency of safety evaluation.

The synergy of reinforcement learning (RL)-based energy management and vehicle-to-everything communication has been proved effective in boosting the fuel economy of connected plug-in hybrid electric vehicles (PHEVs). However, the intricate coupling of mechanical, electrical, thermal states and driving cycle results in a high-dimensional complex energy control problem for PHEVs, which is challenging to optimally solve within the same time scale. To this end, this study designs a multi-horizon reinforcement learning (MHRL)-based energy management of PHEVs, aware of the traffic preview from intelligent transportation systems to optimize the energy flow and thermal states as well as the transient dynamics of the powertrain. The proposed strategy features a novel state space representation, and solves the coordinated training among multiple sub-networks belonging to different control tasks in various time scales. Simulation and hardware-in-the-loop experiments are carried out based on a standard driving cycle and a real-world driving cycle with real-time traffic data demonstrate that the MHRL strategy improves fuel economy by 3.0%~7.9% compared to conventional RL-based energy management under various coolant temperature conditions and dynamic driving scenarios.

The key to achieving path-following and emergency obstacle avoidance in autonomous vehicles is the Steer-By-Wire (SBW) system. While enhancing control accuracy and response speed, the SBW system introduces an increased risk of electronic and electrical failures. To address this problem, the commonly adopted approach is dual steering motor redundancy, ensuring reliability and safety while considering structural and cost constraints. This paper focuses on the Dual-Motor Steer-By-Wire (DMSBW) system, specifically the master-master redundancy mode. Existing studies have paid less attention to the control after the failure of one steering motor. Therefore, this paper proposed an adaptive nonsingular fast terminal sliding mode redundant control algorithm to achieve accurate and swift steering angle tracking. This algorithm accounts for system uncertainty and parameter perturbation, enhancing system robustness while ensuring precise control accuracy. Additionally, considering cost implications in engineering applications, we propose an unknown input observer for accurate estimation of the front wheel steering angle. Finally, the effectiveness of the proposed strategy is validated through Carsim and Simulink, which demonstrate that the strategy exhibits outstanding control performance and robustness, enabling accurate and swift steering tracking at diverse working conditions.

The emergency collision avoidance capabilities of autonomous vehicles (AVs) are crucial for enhancing their active safety performance, particularly in extreme scenarios where standard methods fall short. This study introduces an Extreme Maneuver Controller (EMC) for AVs, utilizing reachability-guided reinforcement learning (RL) to address these challenging situations. By applying pseudospectral methods, we solve the minimum backward reachable tube (Min-BRT) to identify regions where conventional avoidance maneuvers are infeasible, establishing a theoretical basis for triggering extreme maneuvers. A novel controller, employing reachability-guided RL, enables vehicles to execute extreme maneuvers to escape these critical regions. During training, the value function derived from the Min-BRT solution informs the initialization of the Critic networks, enhancing training efficiency. Real-world scenario-based experimental results with actual vehicles validate that the proposed policy, effectively executes beyond-the-limit maneuvers, mitigating collision risks under emergency condition. Furthermore, these extreme maneuvers are executed with minimal deviation from the original driving objectives, ensuring a smooth and stable transition upon completion of extreme maneuvers.

One typical application of connected and automated vehicles (CAVs) is to coordinate multiple CAVs at a non-signalized intersection in mixed traffic, and it may take advantage of multi-agent deep reinforcement learning (MDRL) approaches to improve the overall coordination efficiency. This study proposes a heuristic-based MDRL algorithm (H-QMIX) developed based on a value-based MDRL algorithm, QMIX. This algorithm incorporates a heuristic-based action mask module to guide CAVs efficiently and safely through intersections, composed of a stimulative passing sequence and safety restrictions on CAVs’ action space in the junction area. Compared with other MDRL algorithms (e.g., IPPO, QMIX), the H-QMIX algorithm demonstrates improved training performance in terms of safety and efficiency in two case studies, where the first requires all CAVs to affix their routes, and another allows CAVs to choose random routes. Concerning the model’s generalization ability, the trained models with the maximal episodic return are then transferred to a more practical scenario with a certain vehicle-to-vehicle (V2V) communication delay in a zero-shot manner. The simulation results illustrate that H-QMIX is robust to a certain communication delay. The code for this paper is available at:https://github.com/flammingRaven/heuristic_based_qmix.

Precise path tracking and agilely avoiding obstacles are essential for the stability and safety of autonomous driving. In this paper, we introduce a uniform safe path tracking control strategy that combines obstacle avoidance with path tracking via a barrier function. Unlike the conventional hierarchical collision avoidance methods, our approach employs an integral heuristic barrier function that addresses obstacle avoidance planning and reference trajectory tracking problems simultaneously. Via this, the complex safe trajectory following problem is simplified into a tractable yaw angle tracking problem. We then present a novel finite-time adaptive barrier function-based sliding mode controller that handles input saturation and enhances robustness. This ensures precise and robust yaw angle tracking within specified performance constraints. Moreover, the proposed approach achieves accelerated finite-time convergence compared to the exponential convergence rate. Finally, the Carsim-Simulink co-simulations and real-vehicle experiments validate the effectiveness and superiority of our method in addressing the path-tracking challenge, while upholding driving safety.

This paper introduces an adaptive beyond-the-limit controller, aimed at striking a balance between high-performance maneuvers, such as transient drift, and ensuring safety in unpredictable environments. Our work is motivated by the necessity for autonomous beyond-the-limit control adaptable to real-world uncertainties, where reinforcement learning (RL) faces simulation-to-reality gap challenges in safety and performance. Our approach introduces a hybrid control mechanism that integrates data-driven performance optimization with a robust safety-centric control policy. By leveraging expert demonstrations and employing Jump-Start RL framework in Frenet coordinates, we greatly improve the learning efficiency of performance optimization. Further, an integrated safety control policy is designed to mitigate hazards through predictive trajectory planning, thus significantly reducing the risk of accidents in unforeseen situations. Meanwhile, the hybrid control mechanism employs adaptive weighting between performance and safety considerations, allowing for fusion control based on real-time environmental assessments. Through simulation experiments and initial real-vehicle testing, we validate the effectiveness of our adaptive hybrid controller. The findings confirm that our controller consistently ensures integrated safety in unpredictable environments, with an acceptable impact on performance.

The specific restrictions are breached in the case of vehicle platoon faults and result in unacceptable system performance degradation. This paper proposed a novel prescribed time performance recovery fault tolerant control method to ensure nominal platoon performance under multiple faults, including actuator faults with deferred backup actuator switching and leader-follower link faults in consideration. A novel barrier function based prescribed time sliding mode controller is devised to assure platoon consensus errors and convergence time within prescribed constraints under normal conditions at first. Under multiple faults conditions, to tackle with leader-follower link faults problem, a novel distributed recursive estimator is proposed to estimate the leader’s states and recover the previous leader-follower platooning control protocol in a prescribed time. Besides, in the presence of actuator failures, the nominal constraints violated problem under faults is put into consideration. Owing to the unavoidable deferred actuator replacement time, the previous platoon consensus error constraints are violated and cause platoon performance degradation. Under such circumstances, by exploiting one novel barrier function-based sliding mode controller with an error shifting function, the unfavorable exceeding platoon consensus errors can be recovered into the nominal constraints domains within a prescribed time. Numerical simulations and hardware-in-loop (HIL) experiments are demonstrated to validate the effectiveness and superiority of our performance recovery fault tolerant control algorithms.

Reliable path tracking control (PTC) method is essential for autonomous driving. However, existing PTC methods count on prior vehicle parameters to achieve good performance. This paper presents an optimal PTC method without requiring any prior vehicle parameters based on online modeling with strict parameter convergence ability. First, we build a virtual optimal control problem using adaptive dynamic programming (ADP) scheme to guide the data collection and solve two characteristic matrices containing parameter information. Then, the model construction method is derived using the solved matrices and the optimal PTC method is constructed using the constructed model. Finally, a fault-tolerant control scheme is further designed using the constructed model and the online modeling ability of the proposed method. The effectiveness of the proposed method is validated through co-simulation between Matlab/Simulink and high-fidelity vehicle dynamic simulation software CarSim® under both fault-free and fault-tolerant situations.

This paper investigates a human-machine cooperative trajectory planning and tracking control approach for automated vehicles. The proposed method is developed based on a novel algorithm of cooperative human-machine rapidly-exploring random (HM-RRT) for path planning, together with the risk assessment of driver behavior. First, the driver’s behaviour is assessed according to the information of the predicted vehicle trajectory, the identified safe driving area and the driving risks evaluated in both lateral and longitudinal directions. Based on the driver’s expected driving task, when driving risks are identified by real-time assessment, then the human-machine cooperation is activated during trajectory planning. By HM-RRT, the newly developed safety assurance mechanism for path planning, the cooperative trajectory is then generated, which incorporates the driver’s desire and actions and automation’s corrective actions, to ensure the safety, stability and smoothness of the human-vehicle system. The simulation and experimental results show that the proposed HM-RRT algorithm can effectively improve the convergence rate and reduce the computation load, comparing to the conventional method. Beyond this, the proposed human-machine cooperation approach is able to simultaneously ensure the safety, stability and smoothness of the vehicle and largely reduce human-machine conflicts in real-time applications, demonstrating its feasibility and effectiveness.

As an important safety-critical cyber-physical system (CPS), the braking system is essential to the safe operation of the electric vehicle. Accurate estimation of the brake pressure is of great importance for automotive CPS design and control. In this paper, a novel probabilistic estimation method of brake pressure is developed for electrified vehicles based on multilayer artificial neural networks (ANNs) with Levenberg-Marquardt backpropagation (LMBP) training algorithm. First, the high-level architecture of the proposed multilayer ANN for brake pressure estimation is illustrated. Then, the standard backpropagation (BP) algorithm used for training of the feed-forward neural network (FFNN) is introduced. Based on the basic concept of BP, a more efficient training algorithm of LMBP method is proposed. Next, real vehicle testing is carried out on a chassis dynamometer under standard driving cycles. Experimental data of the vehicle and the powertrain systems are collected, and feature vectors for FFNN training collection are selected. Finally, the developed multilayer ANN is trained using the measured vehicle data, and the performance of the brake pressure estimation is evaluated and compared with other available learning methods. Experimental results validate the feasibility and accuracy of the proposed ANN-based method for braking pressure estimation under real deceleration scenarios.

Equipped with the regenerative braking system, electric vehicle coordinates friction braking and regenerative braking appropriately in normal braking conditions and activates anti-lock braking system (ABS) in emergency braking conditions. This paper mainly focuses on the control strategy of electric-hydraulic blended brake for ABS control of an electric passenger car. According to the variation of the adhesion coefficient under different roads, the maximum adhesion force and the optimal slip ratio are calculated in real-time. Then, the control strategy of electric-hydraulic hybrid ABS, in which regenerative braking and hydraulic braking are coordinated in order to obtain the maximum available road adhesion and guarantee vehicle's braking stability, is proposed. Based on the control strategy developed, simulations and test-bench experiments are carried out. Simulation and test results indicate that braking stability and control performance of vehicle on different roads are guaranteed by the proposed hybrid ABS control, validating the feasibility and the effectiveness of the algorithms. Compared with conventional hydraulic ABS, the electric-hydraulic hybrid ABS, ensuring better braking performance on various road surfaces, provides a good solution to active safety control of EVs.