This paper proposes a novel coordination control strategy through reinforcement learning approach for human-machine cooperative steering of intelligent vehicles, so as to realize a more flexible and efficient way for the human driver and the automated driving system to jointly complete the path-following. Firstly, the human-machine cooperative steering is modeled by the optimal preview driver model with adaptive preview time and the MPC lateral tracking model with adaptive prediction horizon and step, as well as the steering system with a realistic dual-motor steering-by-wire structure is also built into the human-machine cooperative steering system. Then, the human-machine coordination control strategy is designed and trained based on two reinforcement learning agents to further optimize the allocation of cooperative steering weights. Finally, the strategy is fully verified in lane departure simulation scenarios and also in real-world Steering-in-the-Loop experiments with real-time controller, which shows that both agents can effectively keep the vehicle with the small lateral and yaw errors in the simulation scenarios, and also can be successfully applied in the realistic human-machine cooperative system to maintain the lateral and yaw errors within a safe range during path-following.

As a key component of intelligent vehicle technology, automatic parking technology has become a popular research topic. Automatic parking technology can complete parking operations safely and quickly without a driver and can improve driving comfort, while greatly reducing the probability of parking accidents. An automatic parking system based on parking scene recognition is proposed in this paper to resolve the following issues with existing automatic parking systems: parking scene recognition methods are less intelligent, vehicle control has a low degree of automation, and the research scope is limited to traditional fuel vehicles. To increase the utilization of parking spaces and parking convenience, machine vision and pattern recognition techniques are introduced to intelligently recognize a vertical parking scenario, plan a reasonable parking path, develop a path tracking control strategy to improve the vehicle control automation, and explore a highly intelligent automatic parking technology roadmap. This paper gives three key aspects of system solutions for an automatic parking system based on parking scene recognition: parking scene recognition, parking path planning, and path tracking and control.