Autonomous Vehicle Overtaking in a Bidirectional Mixed-Traffic Setting

With the advent of autonomous vehicles on public roads imminent in the near future, special emphasis needs to be placed on addressing scenarios pertaining to mixed-traffic settings, comprised of human-driven and autonomous vehicles. In this paper, we address the problem of autonomous vehicle overtaking in a bidirectional mixed-traffic setting. We design a mixed-integer model predictive controller that maximizes the ego vehicle's velocity while prioritizing safety and accounting for driver comfort. The proposed approach: (i) operates in a limited sensing range while accounting for occlusion; (ii) is able to retract the overtake decision through a receding horizon approach; (iii) is robust to the variations in sensory input and driving behaviors of external agents due to behavior-dependent safety margins; and (iv) reduces to a mixed-integer optimization problem with linear constraints, yielding low computational complexity. We demonstrate the behavior of the proposed approach in a realistic traffic simulation environment.