Trailer-Aware End-To-End Autonomous Driving for Tractor-Trailers with Deep Reinforcement Learning

Abstract

End-to-end autonomous driving has been greatly advanced in recent years. However, most of existing work focuses on small vehicles (e.g., cars). Driving articulated trucks, such as tractor-trailers, still remains less being explored. The underactuated nature and extended wheelbase of tractor-trailers pose considerable driving challenges, especially when navigating narrow roads. For example, when a left-hand-drive tractor-trailer makes a right turn on a two-way two-lane narrow road, the tractor usually needs to encroach some spaces in the opposing lane. Otherwise, the trailer may have insufficient spaces to turn right and strike curbside objects. To provide a solution to this problem, we employ deep reinforcement learning to train an end-to-end autonomous driving policy with a trailer-aware reward function. Through planar rigid-body kinematics analysis, we locate the reference points on the tractor and the trailer. We also build a tractor-trailer model for CARLA. Experimental results demonstrate the effectiveness and superiority of our method in CARLA.