Research on game-based human-machine shared driving (HMSD) has traditionally been constrained by stringent game theory assumptions, high requirements for automated steering controllers, and the need to acquire the human driver’s target trajectory. To address these issues, this paper proposes a bargaining game-based HMSD control authority allocation strategy for a steer-by-wire vehicle. First, two types of lateral path tracking controllers are developed to simulate the human driver and the automatic steering system. Then, a bargaining game-based HMSD control authority allocation strategy is created using the Nash bargaining solution. Finally, a Simulink/CARSIM co-simulation platform and a driver-in-the-loop experimental bench are utilized to verify the proposed strategy. The results show that the proposed bargaining game-based HMSD control authority allocation strategy has potential advantages over existing game-based HMSD, including decoupling of human-machine interaction, lower requirements for automated steering controllers, and feasible game modeling ability. Moreover, the proposed strategy demonstrates excellent performance in terms of shared driving, which extends the applicability of HMSD in various operating maneuvers. Both the human driver and the automatic steering system benefit from the proposed HMSD system, which could increase the acceptance and trustworthiness of the human driver with the HMSD system and reduce the human driving load to make the driver-vehicle interaction more reliable and smoother.

To address the suboptimal leveling performance and insufficient slope stability of existing agricultural machinery chassis in hilly and mountainous regions, this study proposes an innovative omnidirectional leveling system based on a “double-layer frame” crawler-type agricultural chassis. The system employs servo-electric cylinders as its actuation components. A control model for the servo-electric cylinders has been established, accompanied by the design of an adaptive sliding mode controller (ASMC). A co-simulation platform was developed utilizing Matlab/Simulink and Adams to evaluate system performance. Comparative simulations were conducted between the ASMC and a conventional PID controller, followed by comprehensive machine testing. Experimental results demonstrate that the proposed double-layer frame crawler chassis achieves longitudinal leveling adjustments of up to 25° and lateral adjustments of 20°. Through structural optimization and the application of ASMC (in contrast to PID), both longitudinal and lateral leveling response times were reduced by 1.12 s and 0.95 s, respectively. Furthermore, leveling velocities increased by a factor of 1.5 in the longitudinal direction and by a factor of 1.3 in the lateral direction, while longitudinal and lateral angular accelerations decreased by 15.8% and 17.1%, respectively. Field tests confirm the system’s capability for adaptive leveling on inclined terrain, thereby validating the enhanced performance of the proposed omnidirectional leveling system.

The images from a monocular camera can be processed to detect depth information regarding obstacles in the blind spot area captured by the side-view camera of a vehicle. The depth information is given as a classification result “near” or “far” when two blocks in the image are compared with respect to their distances and the depth information can be used for the purpose of blind spot area detection. In this paper, the proposed depth information is inferred from a combination of blur cues and texture cues. The depth information is estimated by comparing the features of two image blocks selected within a single image. A preliminary experiment demonstrates that a convolutional neural network (CNN) model trained by deep learning with a set of relatively ideal images achieves good accuracy. The same CNN model is applied to distinguish near and far obstacles according to a specified threshold in the vehicle blind spot area, and the promising results are obtained. The proposed method uses a standard blind spot camera and can improve safety without other additional sensing devices. Thus, the proposed approach has the potential to be applied in vehicular applications for the detection of objects in the driver’s blind spot.

The energy crisis and environmental pollution have driven the rapid development of new energy vehicles (NEVs). As a core technology for integrating electrification and intelligence in NEVs, the brake-by-wire (BBW) system has become a research hotspot due to its excellent braking energy recovery efficiency and precise active safety control performance. This paper provides a comprehensive review of the research progress in BBW technology for NEVs and provides a forward-looking perspective on its future development. First, the types and structures of the BBW system are introduced, and the development history and representative products are systematically reviewed. Next, this paper focuses on key technologies, such as the design and modeling methods of the BBW system, braking force optimization and distribution strategies, precise actuator control, multi-system coordination, driver operation perception, intelligent decision-making, personalized control, and fault diagnosis and fault-tolerant control. Finally, the main challenges faced in the research of BBW technology for NEVs are analyzed, and future development directions are proposed, providing insights for the optimization designs and industrial application of the BBW system in the future.

Vehicle intelligence is an effective way to improve driving safety and comfort and reduce traffic accidents. The trajectory tracking control of unmanned vehicles is the core module of intelligent vehicles. As a redundant system, the X-by-wire electric vehicle has the advantage that the turning angles and driving torque of the four wheels can be precisely controlled and it has a higher degree of controllability and flexibility. In this paper, a trajectory tracking control algorithm based on a hierarchical control architecture is designed based on x-by-wire vehicles. The hierarchical control algorithm architecture includes the trajectory tracking layer, tire force distribution layer, and actuator control layer. The trajectory tracking layer uses the longitudinal force, lateral force, and yaw moment as the control variables; the model predictive control algorithm controls the vehicle to follow the desired trajectory. The tire force distribution layer is solved by transforming the tire force distribution problem into a quadratic programming problem with constraints. Based on the expected resultant force and resultant moment, the longitudinal force and lateral force of each tire in the vehicle coordinate system are obtained. The actuator control layer converts the coordinate system to obtain the longitudinal force and lateral force in the tire coordinate system, which uses the arctangent function tire model to solve the desired tire slip angle, and then obtains the vehicle steer angle and driving torque. To verify the effectiveness of the trajectory tracking control algorithm of the hierarchical control architecture, the proposed trajectory tracking control algorithm is simulated and verified through the variable speed double line change condition and the low road friction coefficient double line change condition. The simulation results show that the control algorithm proposed in this paper has the accuracy to follow the desired trajectory.Definition:

This paper conducts an in-depth study on anti-lock braking technology in electronic hydraulic braking systems, focusing on a road-surface recognition algorithm based on fitted curves and a slip-rate control method based on sliding-mode variable structure. Firstly, a road-surface recognition algorithm using fitted curves is proposed, which extracts characteristic information by fitting the μ-λ curve, achieving the accurate identification of different road-surface conditions and providing optimal slip rates for subsequent braking control. Secondly, a slip-rate control strategy based on sliding-mode variable structure is designed to achieve optimal slip-rate control during vehicle braking, ensuring braking stability and safety under varying road conditions. Through theoretical analysis and simulation experiments, the results show that the proposed road-surface recognition algorithm can effectively identify various typical road surfaces (such as dry, wet, and icy/snowy surfaces) with high accuracy. The sliding-mode variable-structure control strategy can achieve good slip-rate control under different road conditions, effectively improving vehicle braking performance. This study provides an efficient and reliable technical solution for anti-lock braking control in electronic hydraulic braking systems, with significant theoretical and practical implications for enhancing vehicle braking safety.