Adversarial Reinforcement Learning Framework for Benchmarking Collision Avoidance Mechanisms in Autonomous Vehicles

It is widely believed that the transportation systems of future will be dominated by autonomous vehicles (AVs). With the rapid advancements of this field in recent years, many have come to predict that this shift will occur within the next ten years. A major motivation for the interest and push towards development of AVs stems from the demand for safer transportation. It is generally assumed that replacing the intrinsic imperfections of human drivers with expert computational models may significantly reduce the number of accidents caused by driver error [1]. Yet, development of reliable and robust AV technologies remains an ongoing challenge, and is actively pursued from various directions of research and development [2]. Of particular importance is the research on reliable motion planning and collision avoidance mechanisms. Over the span of multiple decades, numerous approaches towards this problem have been proposed [3], ranging from control theoretic formalizations and optimal control methods to potential field-and rule-based techniques. More recently, advances in machine learning have enabled new data-driven approaches to collision avoidance based on techniques such as imitation learning [4] and deep Reinforcement Learning (RL) [5]. However, with the growing complexity in their deployment settings and mechanisms, the challenge of providing safety guarantees on these solutions is becoming increasingly difficult [2].