Adaptive Authority Allocation of Human-Automation Shared Control for Autonomous Vehicle

Abstract

Great advances had been achieved in the discipline of environmental perception, motion planning and control strategy implementation, however, fully autonomous vehicle is still far from large-scale commercial application. The concept of “human-automation shared control” provides a promising solution to enhance autonomous driving safety, to which great research effort has been contributed in recent years. Nevertheless, more attention should be given to the following aspects. The present shared control strategy either only considers the discontinuous switching control between driver and ADS or investigates the simple effect of driver’s behavior in specific scenarios. The adaptive authority allocation between the driver’s active assistance and ADS hasn’t been investigated yet. In this paper, a shared control experiment with driver’s active assistance is conducted in scheduled traffic scenarios to observe the state of vehicle and arm’ EMG signal. After that, we construct a feature classification algorithm for shared control authority by clustering the experimental data. Then, a SCS with incremental PID controller and 2 DOF vehicle dynamic model is proposed. For validation of the SCS, the comparison of vehicle performance for different control authority illustrates that SCS can allocate appropriate control authority to improve the safety.