Aiming at the instability of high-speed vehicles in windy and rainy environments, the Euler-Lagrange method was used to numerically simulate the external flow field of automobiles under such conditions. The aerodynamic characteristics was investigated at different side wind speeds and rainfall intensities. The whole vehicle dynamics model was established and subjected to aerodynamic loads. The lateral displacement of the vehicle under the windy and rainy conditions was calculated. The results show that, at a constant side wind speed, rainfall increases the drag vortex and the wake vortex diffusion region on the leeward side. Changes in the vortex structure increase the negative pressure area on the leeward side of the body and reduce the the trailing airflow velocity. As a result, aerodynamic drag and side forces are increased. Additionally, changes in road surface adhesion further reduces the vehicle's lateral stability.

Parking tracking accuracy directly affects parking safety, efficiency, and available parking space. Currently, most autonomous parking path tracking relies on model-based feedback control. High tracking errors can arise from a decline in the algorithm's control performance due to uncertainties in system model parameters. In this paper, a feedforward control approach based on iterative learning was developed to reduce the impact of model parameter uncertainty on parking path tracking. Considering that iterative learning control of the system in the time domain was usually affected by the actual speed of the actuator, the system was transformed from the time domain to the space domain, which was related to the desired path. Due to the difficulty in measuring some state variables in the system model and the system's failure to meet the D-type iterative learning rate convergence condition, the design criteria for an H_∞ observer were proposed to accurately estimate state information. Meanwhile, an augmented system with observation errors was constructed to implement iterative learning control, which further reduced the parking path tracking error based on the initial parking tracking information from linear quadratic optimal control (LQR). Finally, a hardware-in-the-loop (HIL) test was established, which proved that the proposed method had excellent practical application potential. The experimental results show that after several iterations, the proposed control method tracks the desired path more accurately than the initial LQR control.

To address the issues of poor yaw stability and low trajectory tracking accuracy in in-wheel motor-driven vehicles under complex operating conditions, a coordinated control method was proposed. By using neural networks for dynamic identification, the stable region in the phase plane of vehicle center-of-mass sideslip angle and sideslip angular velocity was determined. And the instability factor was obtained based on the boundary line features. This factor served as a parameter influencing the objective function weight, while sliding mode control was employed to prevent excessive wheel slip. Simulation results show that compared to the single-target MPC trajectory tracking control strategy, the proposed method reduces the maximum lateral tracking error on low-adhesion road surfaces by 61.7%, and decreases the maximum sideslip angle of the vehicle's center of mass by 75.7%. Even at high speeds, the vehicle maintains stable motion, achieving a balanced improvement in both trajectory tracking accuracy and yaw stability.

Considering the redundancy advantages of the drive system in all-electric drive-brake electric vehicles, the paper focuses on the electric vehicles equipped with a novel distributed steer-by-wire system. The differential drive assisted steering (DDAS) and assisted return-to-center characteristics are studied after the steering motor fails. The DDAS control strategy for reference steering wheel torque tracking is developed using an adaptive fuzzy PID algorithm. The assisted return-to-center control strategy for steering wheel angle tracking is formulated based on a PID algorithm. To adaptively adjust the assist return torque at various vehicle speeds in this strategy, the PID parameters are optimized using a particle swarm optimization algorithm with adaptive weights and learning factors. An 8 DOF vehicle model, a driver model, a steering system model and a motor model are constructed by using Matlab/Simulink/Simscape. The effects of assisted steering and return-to-center on the studied vehicle are verified through simulations. The results show that the steering wheel torque can be decreased by 54.3%, 48.7% and 40.7% under step, double lemniscate and sine conditions, respectively. The road feel of the vehicle at high speed can be improved effectively. And the differential torque can assist in returning the steering wheel to center under hands-off and return-to-normal conditions.

To address the difficulty in determining the optimal return angular speed at various vehicle speeds in traditional active Return-to-Center (RTC) control, a new active RTC control method for steer-by-wire systems is proposed, combining a return speed reference model and sliding mode control. A time-window-based mechanism for determining the active return-to-center state is designed. The return speed reference model is established based on the tire aligning torque, and an active RTC sliding mode control strategy is developed accordingly. Hardware-in-the-loop simulation results indicate that the proposed active return state determination mechanism can accurately switch system states, the return speed reference model exhibits high accuracy, and the sliding mode control strategy effectively ensures that the steering wheel reliably follows the reference return speed.

Rack force is a critical parameter in road feel design and steering follow-up control for steer-by-wire systems. Since it is difficult to measure rack force directly in mass-produced vehicles, its estimation becomes a key aspect in steer-by-wire. This article adopts two methods to estimate the rack force. The first method is based on vehicle dynamics, using Luenberger observer to obtain lateral velocity and the tire brush model is applied to analyze steering resistance torque. The second method is based on steering dynamics, involving a dynamic model for the steering actuator and the direct estimation of rack force using a Kalman filter. The results of the two methods are compared through hardware-in-the-loop (HIL) testing, and then a rule-based fusion strategy using multiple variables is proposed to combine the strengths of the two methods. Finally, bench test results show that the proposed fusion strategy effectively improves the accuracy and real-time performance of rack force estimation under different working conditions.

With the rapid development of advanced intelligent connected vehicles, their safety has become a widespread concern. Currently, the mechanisms to ensure functional safety are a major research focus both domestically and internationally. Countries are gradually establishing standards for vehicle safety performance, and the International Organization for Standardization (ISO) continues to refine the Functional Safety (ISO 26262) and Safety of the Intended Functionality (ISO 21448) standards. On this basis, the safety objectives of most automotive products, especially intelligent chassis systems related to autonomous driving, such as brakebywire and steerbywire actuators, are classified under Automotive Safety Integrity Level D (ASIL D). This paper summarizes industry research on redundancy mechanisms in system safety architecture, analyzes and organizes failoperational safety architecture across different levels of autonomous driving, and examines functional safety degradation strategies. Finally it discusses the potential for integrating ISO 21448 into the ISO 26262 standard to effectively prevent accidents due to errors in the vehicle's Electronic/Electrical (E/E) systems and insufficient actuator performance, thereby providing stronger assurance for safe vehicle operation.

Lightweight alloy wheels reduce vehicle weight, enhance fuel efficiency, and improve power performance, braking efficiency, and suspension system responsiveness. Through simulated impact testing of lightweight alloy wheels, engineers can gain a more comprehensive understanding of their performance under various road conditions, and conduct corresponding design optimizations to enhance safety and durability. This paper systematically reviews the current state of research on 13degree and 90degree impact simulations of wheels, both domestically and internationally. From the perspectives of simulation efficiency, accuracy, and convergence, it discusses the influence of tire models, tire pressure models, and contact properties on the 90degree impact simulations. The paper also introduces the application of automated simulation and deep learning technologies in wheel impact simulation research. The combination of these technologies help achieve standardization, normalization, automation, and intelligence in impact simulations.

A geometric obstacle avoidance model is proposed for traffic environments with dynamic obstacles, which can describe the relationship between vehicle and obstacle movements. By decomposing the spatial distance between the vehicle and the obstacle into two directional components and incorporating their relative speed, three key elements are obtained. Based on these elements, an improved obstacle avoidance model is developed. Using the Model Predictive Control (MPC) principle, the discrete vehicle kinematics model is employed as the predictive model. The objective function and constraints are constructed by adopting the Frenet coordinate system and considering factors such as road boundaries, the vehicle's mechanical structure, driving safety and comfort. Finally, a nonlinear programming problem is established and solved. In this paper, the SF5 is used as the experimental vehicle, with hardware and sensors installed to build an autonomous driving platform. A trajectory planning algorithm was deployed on a ROS and Matlab/Simulinkbased software platform for realworld vehicle testing. The results show that this method not only ensures smooth obstacle avoidance, but also produces a reasonable and comfortable driving path.

To improve the safety and comfort of autonomous vehicles during lane changes, the proposed approach incorporates the impact of lanechanging on local traffic flow and introduces a lanechanging inertia factor based on traditional decisionmaking models. To overcome the limitations of decoupled longitudinal and lateral trajectory planning, a joint constraint planning approach is proposed. Using dynamic programming and quadratic programming algorithms, the current lateral trajectory curvature is adjusted based on the longitudinal constraints from the previous frame. In the longitudinal planning process, key obstacles are filtered based on the current lateral planning results, and curvaturebased speed constraints are