Pedestrian Collision Avoidance for Autonomous Vehicles at Unsignalized Intersection Using Deep Q-Network

TL;DR

This paper develops and evaluates deep reinforcement learning methods for autonomous vehicle navigation at unsignalized intersections with pedestrians, achieving near-perfect collision avoidance and improved efficiency over rule-based approaches.

Contribution

It introduces a reward function and state representation for AV navigation among pedestrians and compares multiple deep RL methods, demonstrating superior performance over traditional rule-based methods.

Findings

Deep RL methods achieve ~100% collision-free episodes.

DDQN/PER outperforms other methods in safety and efficiency.

Proposed methods outperform rule-based approaches in complex pedestrian environments.

Abstract

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.