mlsanityData to 14 Oct 2026

Deep Q-Network Based Decision Making for Autonomous Driving

arXiv 2303.11634 ↗First posted 21 Mar 2023cs.RO cs.LGcited by 4

On the map: End-to-end autonomous driving → Reinforcement learning for driving

Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving. Builds on 13 of its 27 references that are in the map. First posted on arXiv 21 Mar 2023.

Builds on

13 of 27 references.

References · 13

  1. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  2. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  3. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  4. 2016End to End Learning for Self-Driving Carscited by 1,009
  5. 2015End-to-End Training of Deep Visuomotor Policiescited by 875
  6. 2018Learning Dexterous In-Hand Manipulationcited by 687
  7. 2016Safe, Multi-Agent, Reinforcement Learning for Autonomous Drivingcited by 311
  8. 2018How Many Random Seeds? Statistical Power Analysis in Deep Reinforcement Learning Experimentscited by 60
  9. 2018Automated Speed and Lane Change Decision Making using Deep Reinforcement Learningcited by 36
  10. 2016Watch This: Scalable Cost-Function Learning for Path Planning in Urban Environmentscited by 33
  11. 2016Playing Atari Games with Deep Reinforcement Learning and Human Checkpoint Replaycited by 22
  12. 2017Navigating Occluded Intersections with Autonomous Vehicles using Deep Reinforcement Learningcited by 21
1 more
  1. 2018Building a Winning Self-Driving Car in Six Monthscited by 1

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 2; 2025: 1.

4 papers.

  1. 2024SwapTransformer: highway overtaking tactical planner model via imitation learning on OSHA datasetcited by 2
  2. 2025A Review of Learning-Based Motion Planning: Toward a Data-Driven Optimal Control Approachcited by 1
  3. 2023Integrating Renewable Energy in Agriculture: A Deep Reinforcement Learning-based Approachcited by 0
  4. 2024Enhanced Deep Q-Learning for 2D Self-Driving Cars: Implementation and Evaluation on a Custom Track Environmentcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Research on Autonomous Driving Decision-making Strategies based Deep Reinforcement Learning
  2. 2024Mix Q-learning for Lane Changing: A Collaborative Decision-Making Method in Multi-Agent Deep Reinforcement Learning
  3. 2021DQ-GAT: Towards Safe and Efficient Autonomous Driving with Deep Q-Learning and Graph Attention Networks
  4. 2024Enhancing High-Speed Cruising Performance of Autonomous Vehicles through Integrated Deep Reinforcement Learning Framework
  5. 2024Let Hybrid A* Path Planner Obey Traffic Rules: A Deep Reinforcement Learning-Based Planning Framework
  6. 2022Prediction Based Decision Making for Autonomous Highway Driving
  7. 2021Reinforcement Learning Based Safe Decision Making for Highway Autonomous Driving
  8. 2026Safe Reinforcement Learning of Autonomous Highway Driving: A Unified Framework for Safety and Efficiency