# Deep Q-Network Based Decision Making for Autonomous Driving

arXiv: https://arxiv.org/abs/2303.11634 · HTML: https://mlsanity.com/paper/2303.11634/

## 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.

## Placed in

- End-to-end autonomous driving / [Reinforcement learning for driving](https://mlsanity.com/driving/reinforcement-learning-for-driving/)

## Builds on (most cited first, 13 of 13)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [End-to-End Training of Deep Visuomotor Policies](https://mlsanity.com/paper/1504.00702/) (2015)
- [Learning Dexterous In-Hand Manipulation](https://mlsanity.com/paper/1808.00177/) (2018)
- [Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/1610.03295/) (2016)
- [How Many Random Seeds? Statistical Power Analysis in Deep Reinforcement Learning Experiments](https://mlsanity.com/paper/1806.08295/) (2018)
- [Automated Speed and Lane Change Decision Making using Deep Reinforcement Learning](https://mlsanity.com/paper/1803.10056/) (2018)
- [Watch This: Scalable Cost-Function Learning for Path Planning in Urban Environments](https://mlsanity.com/paper/1607.02329/) (2016)
- [Playing Atari Games with Deep Reinforcement Learning and Human Checkpoint Replay](https://mlsanity.com/paper/1607.05077/) (2016)
- [Navigating Occluded Intersections with Autonomous Vehicles using Deep Reinforcement Learning](https://mlsanity.com/paper/1705.01196/) (2017)
- [Building a Winning Self-Driving Car in Six Months](https://mlsanity.com/paper/1811.01273/) (2018)

## Built on by (most cited first, 4 of 4)

- [SwapTransformer: highway overtaking tactical planner model via imitation learning on OSHA dataset](https://mlsanity.com/paper/2401.01425/) (2024)
- [A Review of Learning-Based Motion Planning: Toward a Data-Driven Optimal Control Approach](https://mlsanity.com/paper/2512.11944/) (2025)
- [Integrating Renewable Energy in Agriculture: A Deep Reinforcement Learning-based Approach](https://mlsanity.com/paper/2308.08611/) (2023)
- [Enhanced Deep Q-Learning for 2D Self-Driving Cars: Implementation and Evaluation on a Custom Track Environment](https://mlsanity.com/paper/2402.08780/) (2024)

## 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.