Deep Q-Network Based Decision Making for Autonomous Driving

Max Peter Ronecker, Yuan Zhu
3/21/2023
cs.ROcs.LG

No official code found. Best implementation: Farama-Foundation/HighwayEnv (3,331 stars, MIT, last commit 2026-04-07).

Abstract

Currently decision making is one of the biggest challenges in autonomous driving. This paper introduces a method for safely navigating an autonomous vehicle in highway scenarios by combining deep Q-Networks and insight from control theory. A Deep Q-Network is trained in simulation to serve as a central decision-making unit by proposing targets for a trajectory planner. The generated trajectories in combination with a controller for longitudinal movement are used to execute lane change maneuvers. In order to prove the functionality of this approach it is evaluated on two different highway traffic scenarios. Furthermore, the impact of different state representations on the performance and training process is analyzed. The results show that the proposed system can produce efficient and safe driving behavior.

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Code Implementations(1)

Farama-Foundation/HighwayEnvCommunity71%
MIT

A minimalist environment for decision-making in autonomous driving

3,331905Python, Jupyter NotebookNov 15, 20176 months agoMIT
autonomous-drivinggym-environmentreinforcement-learning

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Cite this paper

@article{ronecker2023deep,
  title  = {Deep Q-Network Based Decision Making for Autonomous Driving},
  author = {Max Peter Ronecker and Yuan Zhu},
  year   = {2023},
  eprint = {2303.11634},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2303.11634v1}
}

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