Post-Impact Stabilization during Lane Change Maneuver

Abstract

This study addresses challenges in vehicle collisions, especially in non-front or non-rear impacts, causing rapid state changes and a loss of control. Electronic Stability Control (ESC) can stabilize a vehicle in minor impact cases, but it cannot effectively handle major collision cases. To overcome this, our research focuses on Post-Impact Stabilization Control (PISC). Existing PISC methods face issues like misidentifying collisions during cornering maneuvers due to assumptions of straight driving, rendering them ineffective for lane change accidents. Our study aims to design PISC specifically for cornering and lane change maneuvers, predicting collision forces solely from the ego vehicle’s data, ensuring improved collision stability control. We employ the unscented Kalman filter to estimate collision forces and develop a sliding mode controller with an optimal force allocation algorithm to counter the disturbances caused by collisions and stabilize the vehicle. Rigorous validation through simulations and tests with a driving simulator demonstrates the feasibility of our proposed methodology in effectively stabilizing vehicles during collision accidents, particularly in lane change situations.