Human-Road Dual Trust Mechanism in Adaptive Distributed Shared Control Framework under Lane Keeping Scenario

As intelligent systems that consider the driver in the loop (DiL), advanced driver-assistance systems (ADASs) have been widely applied in intelligent vehicles to improve driving safety and comfort. To provide more driving pleasure while maintaining driving safety, this paper researches the ADAS platform and proposes a human-road dual trust (HRDT) mechanism to achieve the adaptive distributed shared control under the lane keeping scenario. Differing from the existing trust mechanisms that only focus on driving information, road geometric information is introduced to assess the driving risk more comprehensively by using the fuzzy logic algorithm in the proposed mechanism. Moreover, a novel indicator is designed to provide the integrated measurement of the lateral deviation and the autopilot dependence. Finally, a hierarchical validation process with the driver-in-the-loop platform is applied to prove that the HRDT mechanism helps reduce the autopilot dependence significantly while keeping a minor lateral deviation to maintain driving safety.