Shared Control for Intelligent Vehicle Based on Handling Inverse Dynamics and Driving Intention

Abstract

When we are studying the copiloting system, how to consider the driver's intention more comprehensively is a difficult issue. To this end, this paper presents a novel shared control framework based on handling inverse dynamics and driving intention for lane changing. In particular, the influence of the driver's lane-changing start point and end point is considered in the design of the shared controller. The human driver's driving intention, desired maneuver, and the start and end point are recognized. Moreover, the infinite-dimensional generalized ridge regression in reproducing kernel Hilbert spaces (RKHS) is used to deal with the lane change's start and end point prediction. Besides, a handling inverse dynamics model, including time-varying vehicle speed, is established for the automated controller, and the Multi-stage Gaussian pseudo-spectral method (GPM) is designed to solve the optimal control problem. With a shared fuzzy controller based on the state assessment, the controller generates outputs that can relieve the driver's burden. Finally, the shared control framework is validated in both overtaking and emergency obstacle avoidance scenarios. The results prove that the shared driving control framework can effectively relieve the burden on drivers and improve lane changing safety.