Lane Change Decision-Making through Deep Reinforcement Learning
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Abstract (arXiv)
Due to the complexity and volatility of the traffic environment, decision-making in autonomous driving is a significantly hard problem. In this project, we use a Deep Q-Network, along with rule-based constraints to make lane-changing decision. A safe and efficient lane change behavior may be obtained by combining high-level lateral decision-making with low-level rule-based trajectory monitoring. The agent is anticipated to perform appropriate lane-change maneuvers in a real-world-like udacity simulator after training it for a total of 100 episodes. The results shows that the rule-based DQN performs better than the DQN method. The rule-based DQN achieves a safety rate of 0.8 and average speed of 47 MPH
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 5 of its 15 references that are in the map. First posted on arXiv 24 Dec 2021.
Builds on
5 of 15 references.
References · 5
- 2016End to End Learning for Self-Driving Carscited by 1,000
- 2020Combining Deep Reinforcement Learning and Search for Imperfect-Information Gamescited by 110
- 2018Automated Speed and Lane Change Decision Making using Deep Reinforcement Learningcited by 36
- 2019Lane Change Decision-making through Deep Reinforcement Learning with Rule-based Constraintscited by 24
- 2020Waymo Public Road Safety Performance Datacited by 24
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Citing papers per year: 2025: 1.
1 paper.