Electric power steering (EPS) systems under existing vehicle power systems cannot provide enough power for heavy-duty commercial vehicles under pivot or low-speed steering conditions. To solve this problem, the paper proposes an EPS system that is based on the hybrid power system constituted by the vehicle power system and the supercapacitor in parallel. In order to provide a theoretical basis for the intervention and withdrawal mechanisms of a super-capacitor in the new EPS, the law of steering resistance torque at a low or extremely low vehicle speed should be explored. Firstly, the finite element model of tire/pavement was established to conduct the simulation and calculation of the low-speed steering friction force between the tire and pavement, and to obtain the fitting expression of the equivalent steering friction coefficient with the running speed of the tire. Secondly, the expression of the steering friction torque was deduced based on the calculus theory and mathematical model of the low-speed steering resistance torque, including the steering friction torque and aligning torques, established to conduct the simulation of the equivalent resistance torque applied on a steering column under low-speed condition. Subsequently, the real vehicle experiments were carried out and comparisons of the experimental results and simulation results was performed. The consistency indicated that the model of low-speed steering resistance torque had a high accuracy. Finally, the law of low-speed steering resistance torque with a vehicle speed and steering wheel angle were analyzed according to the 3D surface plot drawn from the simulation results.

The experienced drivers with good driving skills are used as objects of learning, and road steering test data of skilled drivers are collected in this article. First, a nonlinear fitting was made to the driving trajectory of skilled driver in order to achieve human-simulated control. The segmental polynomial expression was solved for two typical steering conditions of normal right-steering and U-turn, and the hp adaptive pseudo-spectral method was used to solve the connection problem of the vehicle segmental driving trajectory. Second, a new Electric Power Steering (EPS) system was proposed, and the intelligent vehicle human-simulated steering system control model based on human simulated intelligent control (HSIC) was established in Simulink/Carsim joint simulation environment to simulate and analyze. Finally, in order to further verify the effectiveness of the proposed algorithm in this article, an intelligent vehicle steering system test bench with a steering resistance torque simulation device was built, and the dSPACE rapid prototype controller was used to realize human-simulated intelligent control law. The results show that the human-simulated steering control algorithm is superior to the traditional proportion integration differentiation (PID) control in the tracking effect of the steering characteristic parameters and passenger comfort. The steering wheel angle and torque can better track the angle and torque variation curve of real vehicle steering experiment of the skilled driver, and the effectiveness of the intelligent vehicle human-simulated steering control algorithm based on HSIC proposed in this article is verified.

Regarding the lane keeping system, path tracking accuracy and lateral stability at high speeds need to be taken into account especially for commercial vehicles due to the characteristics of larger mass, longer wheelbase and higher mass center. To improve the performance mentioned above comprehensively, the control strategy based on improved artificial potential field (APF) algorithm is proposed. In the paper, time to lane crossing (TLC) is introduced into the potential field function to enhance the accuracy of path tracking, meanwhile the vehicle dynamics parameters including yaw rate and lateral acceleration are chosen as the repulsive force field source. The lane keeping controller based on improved APF algorithm is designed and the stability of the control system is proved based on Lyapunov theory. In addition, adaptive inertial weight particle swarm optimization algorithm (AIWPSO) is applied to optimize the gain of each potential field function. The co-simulation results indicate that the comprehensive evaluation index respecting lane tracking accuracy and lateral stability is reduced remarkably. Finally, the proposed control strategy is verified by the HiL test. It provides a beneficial reference for dynamics control of commercial vehicles and enriches the theoretical development and practical application of artificial potential field method in the field of intelligent driving.

Electric power steering system (EPS) with traditional power supply can't provide enough power for heavy-duty vehicle to turn at low speed. To solve the problem, a novel EPS with hybrid power supply was constructed. In this paper, firstly, mathematical model of the EPS with hybrid power supply was established including model of hybrid power supply system and basic model of EPS. Then the power mode recognition was obtained according to the certain threshold of steering resistance torque, state of charge (SOC) of super-capacitor and output voltage of the vehicle power supply, and the corresponding finite automaton model was built. The EPS system with hybrid power supply was regarded as a hybrid system due to continuous event in a single mode and discrete event during mode switching process, thus the multi-mode switching control strategy was proposed. Taken the different characteristics of hybrid power supply in different power modes into account, the corresponding local controllers were designed which were consisted of fuzzy-PID controller, active disturbance rejection controller (ADRC) and sliding mode controller. Besides, the fuzzy supervisory controller was also designed to ensure the stability of the hybrid system during mode switching process. The simulation results show that the local controllers can achieve fast and accurate track of target current in each power mode, and the fuzzy supervisor can effectively ensure the stability of the switching process, which demonstrates the feasibility of the control strategy.

In order to improve the adaptability of the lane keeping control system to complex environments, a dynamic lane tracking control strategy of the commercial vehicle based on the robust model predictive control (RMPC) algorithm is proposed considering the state of the preceding vehicle. An RMPC controller is designed with path deviation and control increment as the objective function. The model predictive control problem is transformed into a min–max optimization problem. The linear matrix inequality (LMI) is used for the optimal solution to obtain the optimal control quantity. The strategy to improve the safety and comfort dynamically in the process of lane keeping is designed by adjusting the weight coefficient matrix of RMPC based on fuzzy theory. The results of the simulation and HiL test show that the RMPC controller can meet the requirement of adjusting the lane tracking process dynamically according to the state of the preceding vehicle, which keeps the balance between safety and comfort.

In urban traffic, accurate prediction of pedestrian trajectory and advanced collision avoidance strategy can effectively reduce the collision risk between intelligent vehicles and pedestrians. In order to improve the prediction accuracy of pedestrian trajectory and the safety of collision avoidance, a longitudinal and lateral intelligent collision avoidance strategy based on pedestrian trajectory prediction is proposed. Firstly, the process of a pedestrian crossing the road is considered as a combination of free motion described by first-order Markov model and the constrained motion presented by improved social force model. The predicted pedestrian trajectory is obtained by weighted fusion of the trajectories of the two models with a multiple linear regression algorithm. Secondly, according to the predicted pedestrian trajectory and time to collision (TTC) the longitudinal and lateral collision avoidance strategy is designed. The improved artificial potential field method is used to plan the lateral collision avoidance path in real time based on the predicted pedestrian position, and a fuzzy controller is constructed to obtain the desired deceleration of the vehicle. Finally, the pedestrian motion fusion model and the longitudinal and lateral collision avoidance strategy are verified by Prescan and Simulink co-simulation. The results show that the average displacement error (ADE) and final displacement error (FDE) of pedestrian trajectory based on pedestrian motion fusion model are smaller compared with a Markov model and improved social force model, and the proposed pedestrian collision avoidance strategy can effectively achieve longitudinal and lateral collision avoidance.