A self-learning human-machine cooperative control method based on driver intention recognition

Abstract

Human-machine cooperative control has become an important area of intelligent driving, where driver intention recognition and dynamic control authority allocation are key factors for improving the performance of cooperative decision-making and control. In this paper, an online learning method is proposed for human-machine cooperative control, which introduces a priority control parameter in the reward function to achieve optimal allocation of control authority under different driver intentions and driving safety conditions. Firstly, a two-layer LSTM-based sequence prediction algorithm is proposed to recognise the driver's lane change (LC) intention for human-machine cooperative steering control. Secondly, an online reinforcement learning method is developed for optimising the steering authority to reduce driver workload and improve driving safety. The driver-in-the-loop simulation results show that our method can accurately predict the driver's LC intention in cooperative driving and effectively compensate for the driver's non-optimal driving actions. The experimental results on a real intelligent vehicle further demonstrate the online optimisation capability of the proposed RL-based control authority allocation algorithm and its effectiveness in improving driving safety.