Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger comfort, transportation efficiency, and energy saving. This survey attempts to provide a comprehensive and thorough overview of the current state of vehicle control technology, focusing on the evolution from vehicle state estimation and trajectory tracking control in AVs at the microscopic level to collaborative control in CAVs at the macroscopic level. First, this review starts with vehicle key state estimation, specifically vehicle sideslip angle, which is the most pivotal state for vehicle trajectory control, to discuss representative approaches. Then, we present symbolic vehicle trajectory tracking control approaches for AVs. On top of that, we further review the collaborative control frameworks for CAVs and corresponding applications. Finally, this survey concludes with a discussion of future research directions and the challenges. This survey aims to provide a contextualized and in-depth look at the state of the art in vehicle control for AVs and CAVs, identifying critical areas of focus and pointing out the potential areas for further exploration.

Connected and automated vehicles (CAVs) have emerged as a potential solution to the future challenges of developing safe, efficient, and eco-friendly transportation systems. However, CAV control presents significant challenges significant challenges due to the complexity of interconnectivity and co-ordination required among vehicles. Multi-agent reinforcement learning (MARL), which has shown notable advancements in addressing complex problems in autonomous driving, robotics, and human-vehicle interaction, emerges as a promising tool to enhance CAV capabilities. Despite its potential, there is a notable absence of current reviews on mainstream MARL algorithms for CAVs. To fill this gap, this paper offers a comprehensive review of MARL’s application in CAV control. The paper begins with an introduction to MARL, explaining its unique advantages in handling complex and multi-agent scenarios. It then presents a detailed survey of MARL applications across various control dimensions for CAVs, including critical scenarios such as platooning control, lane-changing, and unsignalized intersections. Additionally, the paper reviews prominent simulation platforms essential for developing and testing MARL algorithms for CAVs. Lastly, it examines the current challenges in deploying MARL for CAV control, including safety, communication, mixed traffic, and sim-to-real challenges. Potential solutions discussed include hierarchical MARL, decentralized MARL, adaptive interactions, and offline MARL. The work has been summarized in MARL_in_CAV_Control_Repository.

The energy management system (EMS) of hybridization and electrification plays a pivotal role in improving the stability and cost-effectiveness of future vehicles. Existing efforts mainly concentrate on specific optimization targets, like fuel consumption, without sufficiently taking into account the degradation of on-board power sources. In this context, a novel multi-objective energy management strategy based on deep reinforcement learning is proposed for a hybrid electric vehicle (HEV), explicitly conscious of lithium-ion battery (LIB) wear. To be specific, this paper mainly contributes to three points. Firstly, a non-parametric reward function is introduced, for the first time, into the twin-delayed deep deterministic policy gradient (TD3) strategy, to facilitate the optimality and adaptability of the proposed energy management strategy and to mitigate the effort of parameter tuning. Then, to cope with the problem of state redundancy, state space refinement techniques are included in the proposed strategy. Finally, battery health is incorporated into this multi-objective energy management strategy. The efficacy of this framework is validated, in terms of training efficiency, optimality and adaptability, under various standard driving tests.

To reduce the fuel consumption of heavy duty logistic vehicles (HDLVs), P2 parallel hybridization is a promising solution, and deep reinforcement learning (DRL) is a promising method to optimize energy management strategies (EMSs). However, the complicated discrete-continuous hybrid action space lying in the P2 system presents a challenge to achieve real-time optimal control. Thus, this paper proposes a novel DRL algorithm combining auto-tune soft actor-critic (ATSAC) with ordinal regression to optimize the engine torque output and gear shifting simultaneously. ATSAC can adjust the update frequency and learning rate of SAC automatically to improve the generalization and ordinal regression can convert discrete variables into samplings in continuous space to handle the hybrid action. Moreover, a multi-dimensional scenario-oriented driving cycle (SODC) is established through naturalistic driving big data (NDBD) as the training cycle to further improve the EMS generalization. By comprehensive comparison with the widely used twin-delayed deep deterministic policy gradient (TD3) based EMSs, ATSAC achieves significant improvement with 53.70% higher computational efficiency and 12.31% lower negative total reward (NTR) in the training process. Application analysis in unseen real-world driving scenarios shows that only ATSAC based EMS can obtain real-time optimal control in the testing process. Furthermore, the EMS trained through SODC obtains 81.73% lower NTR than the standard China World Transient Vehicle Cycle (CWTVC) which demonstrates that SODC can represent the real-world driving scenarios much more accurately than CWTVC, especially in low-speed high-load conditions which are crucial for HDLVs.

Maintaining both path-tracking accuracy and yaw stability of distributed drive electric vehicles (DDEVs) under various driving conditions presents a significant challenge in the field of vehicle control. To address this limitation, a coordinated control strategy that integrates adaptive model predictive control (AMPC) path-tracking control and direct yaw moment control (DYC) is proposed for DDEVs. The proposed strategy, inspired by a hierarchical framework, is coordinated by the upper layer of path-tracking control and the lower layer of direct yaw moment control. Based on the linear time-varying model predictive control (LTV MPC) algorithm, the effects of prediction horizon and weight coefficients on the path-tracking accuracy and yaw stability of the vehicle are compared and analyzed first. According to the aforementioned analysis, an AMPC path-tracking controller with variable prediction horizon and weight coefficients is designed considering the change of vehicle speed in the upper layer. The lower layer involves DYC based on the linear quadratic regulator (LQR) technique. Specifically, the intervention rule of DYC is determined by the threshold of the yaw rate error and the phase diagram of the sideslip angle. Extensive simulation experiments are conducted to evaluate the proposed coordinated control strategy under different driving conditions. The results show that, under variable speed and low adhesion conditions, the vehicle yaw stability and path-tracking accuracy have been improved by 21.58% and 14.43%, respectively, compared to AMPC. Similarly, under high speed and low adhesion conditions, the vehicle yaw stability and path-tracking accuracy have been improved by 44.30% and 14.25%, respectively, compared to the coordination of LTV MPC and DYC. The results indicate that the proposed adaptive path-tracking controller is effective across different speeds. Furthermore, the proposed coordinated control strategy successfully enhances the vehicle stability while maintaining good path-tracking accuracy even under extreme conditions.

Full drive-by-wire electric vehicles (FDWEV) equipped with X-by-wire technology can enable independent driving, braking, and steering of each wheel, making them an ideal platform for developing autonomous driving technology. However, designing a robust control algorithm that comprehensively integrates vehicle path planning in a complex and unstructured scenario is a challenging task for FDWEV. To address this issue, this paper (1) proposes the artificial potential field (APF) method for path planning in the prescribed park with different static obstacles to generate the reference path information, where speed planning is incorporated considering kinematics and dynamic constraints; (2) designs curvature calculation (CC-based) and model predictive control (MPC-based) tracking methods with the lateral dynamics model to track the desired path under different driving conditions, in which a forward-looking behavior model of the driver with variable preview distance is designed based on the fuzzy control theory; (3) conducts CarSim-AMESim-Simulink co-simulation with the existence of obstacles. The simulation results show that the proposed two control approaches are practical for classical driving scenarios. Especially the MPC-based path-tracking controller enhances dynamic tracking performance and ensures good maneuverability under high-dynamic driving conditions.