Collision avoidance for autonomous vehicles using reachability-based trajectory planning in highway driving

Abstract

As vehicle applications have evolved to a more intelligent and self-driving stage, autonomous vehicles have attracted more attention in recent years. This paper proposes a trajectory planner that considers feasibility, safety and passenger acceptance. This will ensure autonomous vehicles satisfy the constraints of the traffic environment, driving ability, and comfort drivers experience during collision avoidance. This paper deals with planning collision-free trajectories for autonomous vehicles on highways. The problem is formulated using reachability-based planning via zonotope. According to the vehicle dynamics model, the trajectory feasibility is determined by the vehicle motion feasibility set. The next step is to apply safety constraints to the base planner by collision avoidance checking. Given that this planner uses a receding horizon strategy, it selects a safe parameter in each planning iteration. At each stage of planning, the set of reachable vehicles should not intersect with any obstacles. Since braking cannot prevent a collision, this approach consists of lane changing and overtaking maneuvers to avoid collisions. Finally, knowledge from the safety of the intended functionality (SOTIF) standard is utilized to verify the algorithm performance. The efficiency and performance of different driving styles of trajectory planners are verified by vehicle tests under different vehicle velocities and different obstacle disturbances. Satisfactory results are obtained from the set of simulated scenarios.