Pedestrian Collision Avoidance for Autonomous Vehicles at Unsignalized Intersection Using Deep Q-Network
This addresses pedestrian safety for autonomous vehicles in urban environments, though it is incremental as it applies existing methods to a specific scenario.
The paper tackled autonomous vehicle navigation at crowded, unsignalized intersections by comparing deep reinforcement learning methods to a rule-based approach, achieving approximately 100% collision-free episodes versus less than 40% for the rule-based method.
Prior research has extensively explored Autonomous Vehicle (AV) navigation in the presence of other vehicles, however, navigation among pedestrians, who are the most vulnerable element in urban environments, has been less examined. This paper explores AV navigation in crowded, unsignalized intersections. We compare the performance of different deep reinforcement learning methods trained on our reward function and state representation. The performance of these methods and a standard rule-based approach were evaluated in two ways, first at the unsignalized intersection on which the methods were trained, and secondly at an unknown unsignalized intersection with a different topology. For both scenarios, the rule-based method achieves less than 40\% collision-free episodes, whereas our methods result in a performance of approximately 100\%. Of the three methods used, DDQN/PER outperforms the other two methods while it also shows the smallest average intersection crossing time, the greatest average speed, and the greatest distance from the closest pedestrian.