Anti-bullying Adaptive Cruise Control: A proactive right-of-way protection approach

TL;DR

This paper introduces an innovative Anti-bullying Adaptive Cruise Control system that proactively protects right-of-way against aggressive cut-ins by modeling vehicle interactions and adapting to different driving styles.

Contribution

It develops a novel game-theoretic motion planning framework combined with online driving style identification for enhanced ACC safety and adaptability.

Findings

Prevents road bullying cut-ins effectively in simulations.

Improves safety and comfort by up to 79.8% and 20.4%.

Enhances traffic flow efficiency by up to 19.33%.

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, which is capable of proactively protecting right-of-way against such "road bullying" cut-ins. 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. 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 ego vehicle could consider cut-in vehicles' all possible reactions to find its optimal right-of-way protection maneuver. To the best of our knowledge, this research is the first to model vehicles' interaction dynamics and develop an interactive planner that adapts cut-in vehicle's various 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. It can improve safety and comfort by up to 79.8% and 20.4%. The driving efficiency has benefits by up to 19.33% in traffic flow. The proposed approach can also adopt more flexible driving strategies. Furthermore, the proposed approach can support real-time field implementation by ensuring less than 50 milliseconds computation time.