Reinforced Damage Minimization in Critical Events for Self-driving Vehicles

Self-driving systems have recently received massive attention in both academic and industrial contexts, leading to major improvements in standard navigation scenarios typically identified as well-maintained urban routes. Critical events like road accidents or unexpected obstacles, however, require the execution of specific emergency actions that deviate from the ordinary driving behavior and are therefore harder to incorporate in the system. In this context, we propose a system that is specifically built to take control of the vehicle and perform an emergency maneuver in case of a dangerous scenario. The presented architecture is based on a deep reinforcement learning algorithm, trained in a simulated environment and using raw sensory data as input. We evaluate the system's performance on several typical pre-accident scenario and show promising results, with the vehicle being able to consistently perform an avoidance maneuver to nullify or minimize the incoming damage.