Adaptive Crash-Avoidance Predictive Control Under Multi-Vehicle Dynamic Environment for Intelligent Vehicles

Abstract

Intelligent vehicles (IVs) play a pivotal role within the Intelligent Transportation System (ITS), significantly enhancing transportation efficiency and mitigating the risks of accidents. Nevertheless, the ever-evolving challenge environment, characterized by diverse scenarios with multiple dynamic vehicles and varying road conditions, present a new challenge for IVs' path planning and following algorithms in the adaption improvement under different traffic scenarios, thereby limiting IVs wider integration within ITS. This paper introduces an innovative adaptive integrated predictive control framework, which treats multi-vehicle dynamic interaction as a process of system model reconfiguration, enhancing the versatility of controller under complex scenarios. The dynamic multiple surrounding vehicles' states, the nonlinear tire model, and actuator characteristics are incorporated into the reconfigurable predictive model. Based on the arbitrary driving behavior of multiple vehicles and diverse road conditions, traffic risks are quantitatively assessed, which is applied to optimize the output of actuators within time-varying stability constraints. To assess its effectiveness, robustness, and real-time performance, the adaptive integrated controller is tested in a range of complex scenarios using a driver-in-the-loop platform. The results demonstrate that the adaptive integrated controller can effectively prevent crashes with multiple dynamic vehicles under different road conditions by employing coordinated control among actuators while ensuring driving stability.