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A novel multi‐input multi‐output adaptive control strategy applied to the combined active front steering (AFS) and direct yaw moment control (DYC) system is proposed in this paper. Studies have been conducted to show the potential for electric vehicles to be affected by time delay, which may appear in states. Aiming to address the state delay, radial basis function neural network (RBFNN) and Lyapunov‐Krakovskii functional‐based AFS and DYC controllers are constructed, which also can process the parameter uncertainties due to the powerful ability of RBFNN to approximate the unknown terms. Meanwhile, the controller is designed to incorporate the barrier Lyapunov function so that the state variables under control, including the sideslip angle and yaw rate, remain in their respective stable ranges while accurately tracking the corresponding desired values. Co‐simulations utilizing Simulink and CarSim are implemented for verifying the effectiveness of the proposed control scheme and comparing it with the traditional control scheme. [ABSTRACT FROM AUTHOR]

In the condition of actual obstacle avoidance, where longitudinal deceleration is large and variable, it is difficult for the autonomous vehicle to simultaneously track longitudinal velocity and lateral motion. In addition, when the planned trajectory exceeds the adhesion of the road, the vehicle is also prone to losing stability. Most studies of the obstacle avoidance process only consider constant velocity. This paper presents a novel control architecture for four-wheel driving (4WD) and four-wheel steering (4WS) autonomous vehicles to track predefined trajectory and velocity. It is proposed to use preview technology to obtain the desired state of the vehicle and a controller based on multi-input multi-output nonlinear model predictive control (MIMO-NMPC) that considers the deceleration-steering combination condition. According to the sensitivity analysis of the influence of tyre slip rate on tyre lateral force, the optimal working boundary of the tyre is determined. The output of the controller is constrained by estimating tyre force to prevent tyre force saturation and maximise tyre adhesion limit utilisation. The simulation results demonstrate that the proposed controller can better track the trajectory and longitudinal velocity with a faster response on the high-adhesion road. The overshoot of lateral displacement and the maximum error of longitudinal velocity are less than 0.03m and 0.2m/s, respectively. In addition, when the planned trajectory exceeds the adhesion limit of the road, it can still follow the desired trajectory as closely as possible while maintaining stability on low-adhesion roads. [ABSTRACT FROM AUTHOR]

Controlling an autonomous vehicle's unprotected left turn at an intersection is a challenging task. Traditional rule‐based autonomous driving decision and control algorithms struggle to construct accurate and trustworthy mathematical models for such circumstances, owing to their considerable uncertainty and unpredictability. To overcome this problem, a rule‐constrained reinforcement learning (RCRL) control method is proposed in this work for autonomous driving. To train a reinforcement learning controller with rule constraints, outcomes of the path planning module are used as a goal condition in the reinforcement learning framework. Since they include vehicle dynamics, the proposed approach is safer and more reliable compared to end‐to‐end learning, thereby ensuring that the generated trajectories are locally optimal while adjusting to unpredictable situations. In the experiments, a highly randomized two‐way four‐lane intersection is established based on the CARLA simulator to verify the effectiveness of the proposed RCRL control method. Accordingly, the results show that the proposed method can provide real‐time safe planning and ensure high passing efficiency for autonomous vehicles in the unprotected left turn task. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problem of a wheel slip ratio constraint control for the Antilock Braking System (ABS) with external interferences and state constraints. A quarter vehicle braking model with uncertainties is considered, and a constraint control method based on Tangent Type Barrier Lyapunov Function (Tan-BLF) is designed. In addition, due to the selection of the appropriate adaptive control rate, the designed control method can quickly track the ideal slip ratio and ensure that the slip ratio for ABS is always constrained in the stable region even if there are uncertainties in the control system and unknown external disturbances. The simulation results show that the control scheme proposed in this paper, compared with the traditional QLF controller scheme, can make the vehicle wheel slip rate stable at the optimal value early and keep it in the optimal range from the beginning of the simulation, thus reducing the braking distance and braking time of the vehicle, which means that the vehicle can avoid wheel locking. And the adaptive rate is always bounded. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problem of robust constraint control for direct yaw moment control (DYC) system with sideslip angle. A two degree of freedom vehicle dynamics model is considered here. The tangent barrier Lyapunov function (BLFs-Tan) is embedded into the systematic controller design for handling sideslip angle constraint problems. A robust nonlinear constraint control scheme based on BLFs-Tan is proposed not only to track the reference trajectory but also to guarantee no violation on the sideslip angle constraint and refrain from vehicle instability. Simulation results indicate that the proposed scheme can guarantee there will be no violation on sideslip angle constraints and avoid vehicle instability. [ABSTRACT FROM AUTHOR]

Safety Distance Model (SDM) is a key component of collision avoidance systems. However, traditional SDMs focus only on the obstacles that can be detected by vehicular sensors, ignoring obscured areas where obstacles cannot be detected timely by sensors due to tall trees and buildings on both sides of a road. This paper presents an innovative safety distance model (SDM-PTA) based on real-time identification of potential traffic accident (PTA) areas by a convolution neural network. The research focuses on analyzing relationships between relative speed and distance of an autonomous vehicle and PTA areas. The SDM-PTA accounting for rear-end collision is obtained, which could provide decision-making information for autonomous braking system of intelligent vehicles. Research results show that the SDM-PTA can increase collision avoidance performance of intelligent vehicles, especially at corners and crossroads which are PTA areas. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problem of constraint control for an Antilock Braking System (ABS) with asymmetric slip ratio constraints. A nonlinear backstepping control method based on Barrier Lyapunov Function (BLF) is proposed not only to track the optimal slip ratio but also to guarantee no violation on the slip ratio constraints. A second order slip rate model is established based on a quarter vehicle braking model and a Burckhardt's tire model. The Asymmetric Barrier Lyapunov Function (ABLF) is introduced into the backstepping controller for solving the asymmetric slip ratio constraint problems. The proposed controller can implement ABS zero steady state error tracking of the optimal wheel slip ratio and make slip ratio constraints flexible for various runway surfaces and runway transitions. Simulation results show that the control scheme can guarantee no violation on slip ratio constraints and avoid self-locking. [ABSTRACT FROM AUTHOR]