An adaptive model predictive approach for automated vehicle control in fallback procedure based on virtual vehicle scheme

Abstract

Purpose Automated driving systems (ADSs) are being developed to avoid human error and improve driving safety. However, limited focus has been given to the fallback behavior of automated vehicles, which act as a fail-safe mechanism to deal with safety issues resulting from sensor failure. Therefore, this study aims to establish a fallback control approach aimed at driving an automated vehicle to a safe parking lane under perceptive sensor malfunction. Design/methodology/approach Owing to an undetected area resulting from a front sensor malfunction, the proposed ADS first creates virtual vehicles to replace existing vehicles in the undetected area. Afterward, the virtual vehicles are assumed to perform the most hazardous driving behavior toward the host vehicle; an adaptive model predictive control algorithm is then presented to optimize the control task during the fallback procedure, avoiding potential collisions with surrounding vehicles. This fallback approach was tested in typical cases related to car-following and lane changes. Findings It is confirmed that the host vehicle avoid collision with the surrounding vehicles during the fallback procedure, revealing that the proposed method is effective for the test scenarios. Originality/value This study presents a model for the path-planning problem regarding an automated vehicle under perceptive sensor failure, and it proposes an original path-planning approach based on virtual vehicle scheme to improve the safety of an automated vehicle during a fallback procedure. This proposal gives a different view on the fallback safety problem from the normal strategy, in which the mode is switched to manual if a driver is available or the vehicle is instantly stopped.

Mode Switching Control Using Lane Keeping Assist and Waypoints Tracking for Autonomous Driving in a City Environment

“…The full simulation uses 29 waypoints in total, including waypoints on the main road in case LKA sensing failed and waypoints at the junction. The additional constraints compared with scenario 1 are a prescribed time headway t h = 1:5s from a lead vehicle and an acceleration in the range [23,2] m.s 22 . The thresholds of similarity e 1 , e 2 between steering and acceleration using waypoint tracking and LKA MPC are both equal to 0.002, such that LKA MPC is used on the main road (F\ G), where there are no lane pattern issues and G is the junction area in this example where N J = 1.…”

“…In Qu et al ( 22 ), MPC was used with five driving modes depending on a congestion dependent emergency coefficient and on the estimated acceleration of a lead vehicle, with improvements compared with single mode MPC in driving comfort and safety. In Xue et al ( 23 ), adaptive MPC was used for automated fallback maneuvers in presence of sensor malfunctions. Adaptive MPC formulations with responsibility-sensitive safety distance constraints were introduced in Chai et al ( 24 ).…”

Lateral stability regulation of intelligent electric vehicle based on model predictive control

“…As the characteristic of fast response and high accuracy, these intelligent and safety techniques can be realized and the traffic accidents can be effectively reduced. Lateral stability control of EV is critical to the handling and stability performance of EVs, thus is attached many interests from academics and industry (Jiang et al, 2020;Mangia et al, 2021;Zhao and Zhang, 2018;Xue et al, 2019;Guo et al, 2019;Ni et al, 2019;Cao et al, 2020).…”