Human-machine Cooperative Control Strategy Considering Human-machine Trust Matching

Abstract:

The level of human-machine mutual trust is a key factor affecting the performance of human-machine cooperative systems. This paper presents a Stackelberg Game-based cooperative control strategy that considers human-machine trust matching. Firstly， a method was proposed for assessing the mutual trust level between drivers and machines. Based on this， the weight allocation in cooperative driving was performed according to the level of human-machine trust matching. Subsequently， a model predictive control framework was adopted， and the optimal cooperative control strategy was obtained by combining the Stackelberg Game theory for optimization. Finally， driver-in-the-loop experiments were conducted to validate the proposed cooperative control strategy. Results demonstrate that， for drivers with different trust matching levels， the strategy can improve the precision of path tracking by 70.91%， and reduce the driving burden by 44.03%. The proposed strategy enhances the driving performance and reduces the driver workload.

Key words:human-machine cooperative control;human-machine trust matching;weight allocation;Stackelberg Game