The operation of the steer-by-wire (SBW) system without physical transmission mechanism is relied on the control of steering actuation. The main disturbance during the tracking control of steering actuation is the nonlinear rack force caused by the vehicle tire-road interaction. To improve the steering performance of SBW system, a steering controller with an active rack force compensation concept is proposed in this paper for the tracking control algorithm design. Three kinds of disturbance observers are established to show the efficiency of rack force estimation during the steering control. By utilizing an extended disturbance observer into a robust sliding mode control, an active tracking controller with feedforward compensation is designed for the SBW system. The proposed algorithms are validated with different driving test cycles from ISO standards in software-in-loop and hardware-in-loop experiments. The results have shown the effectiveness of the rack force estimation and tracking performance by the proposed controller, even in the extreme testing conditions.

With vehicle-to-everything (V2X) information, connected and automated vehicle (CAV) eco-driving strategy allows the vehicle to plan its speed and choose the optimal lane based on actual conditions, resulting in improved driving performance. This study presents a novel eco-driving strategy framework based on deep reinforcement learning (DRL) techniques for CAVs driving in urban scenarios. This framework integrates longitudinal speed planning with lateral lane change decision-making and aims to co-optimize the energy efficiency, driving safety, and travel efficiency. By leveraging traffic information and multi-objective reward functions, the twin delayed deep deterministic (TD3) algorithm is employed to train the actor-critic (AC) network which generates both longitudinal and lateral control commands based on its estimation for lane preference score. The proposed strategy is tested in a complex urban scenario based on Simulation Urban Mobility (SUMO) which reflects real urban traffic conditions. Experimental results indicate that the longitudinal speed planning module of the proposed strategy can shorten the travel time by up to 7.94% or reduce the electricity consumption by 18.15%, depending on the degree of importance placed on economy by the TD3 agent. By integrating the lateral lane decision module, the proposed strategy can further shorten the travel time by 5.7% and save 1.75% energy consumption.

Although artificial intelligence has made tremendous progress recently, there remain various technical issues and ethical problems before autonomous vehicles can be available to the general public. In the interim, collaboration between automated systems and human drivers becomes a promising solution, where the merits of machine intelligence and human intelligence are blended in a complementary way. To this end, the paper proposes a reference-free human-vehicle shared control framework based on reinforcement learning. Firstly, a personalized human-like driver agent is derived from highway driving data by means of generative adversarial imitation learning integrated with Gaussian mixture model. The driver model is responsible for real-time interaction with the reinforcement learning agent to relieve the burden of human operators in the course of training. Then, a copilot agent learns the policies to cooperatively control the vehicle steering based on three distinct implementations for the search of the best coordination strategy. Heuristic reward functions are designed to guide the agent policy optimization for multi-objective equilibrium between driver synchronization against intervention. To verify the control performance of the proposed shared driving system, simulation experiments with driver models and human-in-the-loop tests with real-life participants are conducted in the end of this paper. The results demonstrate that the shared steering control method can effectively follow human intentions, facilitate driving goals, improve road safety and reduce driver’s workload simultaneously in the challenging dynamic obstacle avoidance scenarios.