Abstract

Automated vehicles (AVs) require proactive safety systems to safely operate in diverse driving scenarios. The complexity and challenges of high-risk driving maneuvers, such as complete overtaking, have led the research community to make significant efforts toward developing reliable operating modes for overtaking maneuver systems. These efforts aim to better understand the benefits and limitations of intrinsic operating modes in complete overtaking to take AVs to the next level. This paper delves into the challenges of complete overtaking maneuvers by developing and evaluating an overtaking system for AVs. Specifically, a vision and radar-based automated overtaking system is designed, which utilizes data from cameras and radars to track surrounding vehicles, evaluate overtaking feasibility based on overtaking time (OT) and time-to-collision (TTC), and decide the driving behavior of the ego vehicle. A modular architecture is developed encompassing perception, sensor fusion and tracking, collision detection and decision-making, overtaking planning, and motion control. Extensive simulations are conducted under varying conditions, utilizing a specialized framework consisting of RoadRunner, Automated Driving Toolbox, and MATLAB/Simulink, to assess the system's ability to avoid potential collisions and enhance maneuver reliability. The findings highlight the significance of the operating mode in accident mitigation and contribute to the broader goal of achieving safe automated driving.