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 cooper...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.展开更多
基金National Natural Science Foundation of China under Grant 61825305,62003361,U21A20518.
文摘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.
