Navigating Autonomous Vehicle at the Road Intersection with Reinforcement Learning

EasyChair Preprint 4347

Abstract

In this paper, we consider the problem of controlling an intelligent agent that simulates the behavior of an unmanned car when passing an road intersection together with other vehicles. We consider the case of using smart city systems, which allow the agent to get full information about what is happening at the intersection in the form of video frames from surveillance cameras. The paper proposes the implementation of a control system based on a trainable behavior generation module. Agent's model is implemented using reinforcement learning (RL) methods. In our work, we analyze various RL methods (PPO, Rainbow, TD3), and variants of the computer vision subsystem of the agent. Also, we present our results of the best implementation of the agent when driving together with other participants in compliance with traffic rules.

Keyphrases: Off-policy Methods, Reinforcement Learning, Road Intersection, computer vision, policy gradient, self-driving car

@booklet{EasyChair:4347,
  author    = {Michael Martinson and Alexey Skrynnik and Aleksandr I. Panov},
  title     = {Navigating Autonomous Vehicle at the Road Intersection  with Reinforcement Learning},
  howpublished = {EasyChair Preprint 4347},
  year      = {EasyChair, 2020}}