Navigating Intersections with Autonomous Vehicles using Deep Reinforcement Learning

by   David Isele, et al.

Providing an efficient strategy to navigate safely through unsignaled intersections is a difficult task that requires determining the intent of other drivers. We explore the effectiveness of using Deep Reinforcement Learning to handle intersection problems. Combining several recent advances in Deep RL, were we able to learn policies that surpass the performance of a commonly-used heuristic approach in several metrics including task completion time and goal success rate. Our analysis, and the solutions learned by the network point out several short comings of current rule-based methods. The fact that Deep RL policies resulted in collisions, although rarely, combined with the limitations of the policy to generalize well to out-of-sample scenarios suggest a need for further research.



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