# Lane Change Decision-making through Deep Reinforcement Learning with Rule-based Constraints

arXiv: https://arxiv.org/abs/1904.00231 · HTML: https://mlsanity.com/paper/1904.00231/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2019.

## Built on by (most cited first, 24 of 24)

- [Robustness and Adaptability of Reinforcement Learning based Cooperative Autonomous Driving in Mixed-autonomy Traffic](https://mlsanity.com/paper/2202.00881/) (2022)
- [A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles](https://mlsanity.com/paper/2105.14218/) (2021)
- [Integrating Artificial Intelligence and Augmented Reality in Robotic Surgery: An Initial dVRK Study Using a Surgical Education Scenario](https://mlsanity.com/paper/2201.00383/) (2022)
- [Towards Safe Autonomous Driving Policies using a Neuro-Symbolic Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2307.01316/) (2023)
- [Parameterized Decision-making with Multi-modal Perception for Autonomous Driving](https://mlsanity.com/paper/2312.11935/) (2023)
- [Decision Making in Monopoly using a Hybrid Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2103.00683/) (2021)
- [Prediction-aware and Reinforcement Learning based Altruistic Cooperative Driving](https://mlsanity.com/paper/2211.10585/) (2022)
- [A Reinforcement Learning Benchmark for Autonomous Driving in Intersection Scenarios](https://mlsanity.com/paper/2109.10557/) (2021)
- [Optimizing Trajectories for Highway Driving with Offline Reinforcement Learning](https://mlsanity.com/paper/2203.10949/) (2022)
- [An Intelligent Self-driving Truck System For Highway Transportation](https://mlsanity.com/paper/2112.15304/) (2021)
- [Conditional Goal-oriented Trajectory Prediction for Interacting Vehicles with Vectorized Representation](https://mlsanity.com/paper/2210.15449/) (2022)
- [AI Recommendation Systems for Lane-Changing Using Adherence-Aware Reinforcement Learning](https://mlsanity.com/paper/2504.20187/) (2025)
- [Decentralized Cooperative Lane Changing at Freeway Weaving Areas Using Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/2110.08124/) (2021)
- [Weakly Supervised Reinforcement Learning for Autonomous Highway Driving via Virtual Safety Cages](https://mlsanity.com/paper/2103.09726/) (2021)
- [Benchmarking Lane-changing Decision-making for Deep Reinforcement Learning](https://mlsanity.com/paper/2109.10490/) (2021)
- [Lane Change Decision-Making through Deep Reinforcement Learning](https://mlsanity.com/paper/2112.14705/) (2021)
- [Learning-based social coordination to improve safety and robustness of cooperative autonomous vehicles in mixed traffic](https://mlsanity.com/paper/2211.11963/) (2022)
- [Mix Q-learning for Lane Changing: A Collaborative Decision-Making Method in Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/2406.09755/) (2024)
- [Enhanced Safety in Autonomous Driving: Integrating Latent State Diffusion Model for End-to-End Navigation](https://mlsanity.com/paper/2407.06317/) (2024)
- [A Safe Self-evolution Algorithm for Autonomous Driving Based on Data-Driven Risk Quantification Model](https://mlsanity.com/paper/2408.12805/) (2024)
- [A Hybrid Input based Deep Reinforcement Learning for Lane Change Decision-Making of Autonomous Vehicle](https://mlsanity.com/paper/2509.01611/) (2025)
- [Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving](https://mlsanity.com/paper/2509.04712/) (2025)
- [CauTraj: A Causal-Knowledge-Guided Framework for Lane-Changing Trajectory Planning of Autonomous Vehicles](https://mlsanity.com/paper/2512.18703/) (2025)
- [Multi-agent DRL-based Lane Change Decision Model for Cooperative Platooning in Mixed Traffic](https://mlsanity.com/paper/2601.11809/) (2026)

## Abstract (arXiv)

Autonomous driving decision-making is a great challenge due to the complexity and uncertainty of the traffic environment. Combined with the rule-based constraints, a Deep Q-Network (DQN) based method is applied for autonomous driving lane change decision-making task in this study. Through the combination of high-level lateral decision-making and low-level rule-based trajectory modification, a safe and efficient lane change behavior can be achieved. With the setting of our state representation and reward function, the trained agent is able to take appropriate actions in a real-world-like simulator. The generated policy is evaluated on the simulator for 10 times, and the results demonstrate that the proposed rule-based DQN method outperforms the rule-based approach and the DQN method.