Anti-Bullying Adaptive Cruise Control: A Proactive Right-of-Way Protection Approach

Abstract

Adaptive Cruise Control (ACC) systems have been widely commercialized in recent years. However, existing ACC systems remain vulnerable to close-range cut-ins, a behavior that resembles “road bullying”. To address this issue, this research proposes an Anti-bullying Adaptive Cruise Control (AACC) approach under a vehicle-cloud cooperation scheme. To handle diverse “road bullying” cut-in scenarios smoothly, the proposed approach first leverages an online Inverse Optimal Control (IOC)-based algorithm for individual driving style identification in the cloud. Then, based on Stackelberg competition, a game-theoretic-based motion planning framework is presented in which the identified individual driving styles are utilized to formulate cut-in vehicles’ reaction functions. By integrating such reaction functions into the ego vehicle’s motion planning, the cloud could consider cut-in vehicles’ all possible reactions to find optimal right-of-way protection maneuvers for the ego vehicle. To the best of our knowledge, this research is the first to model vehicles’ interaction dynamics and develop an interactive planner that adapts to various cut-in vehicle driving styles. Simulation results show that the proposed approach can prevent “road bullying” cut-ins and be adaptive to different cut-in vehicles’ driving styles. Compared with the traditional ACC, the proposed approach can improve safety and comfort by up to 79.8% and 20.8%. The driving efficiency is improved by up to 17.32% in traffic flow. Increasing the penetration rate of cloud-connected EVs improves lane-average speed by up to 1.36 km/h, demonstrating traffic-flow-level benefits. Furthermore, the proposed approach can support real-time field implementation by ensuring less than 50 milliseconds computation time in nearly all cases.