mlsanityData to 7 Oct 2026

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

arXiv 1904.00231 ↗First posted 30 Mar 2019cs.RO cs.AI cs.LGcited by 24

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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.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Mar 2019.

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Citing papers per year: 2021: 8; 2022: 6; 2023: 2; 2024: 3; 2025: 4; 2026: 1.

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  1. 2022Robustness and Adaptability of Reinforcement Learning based Cooperative Autonomous Driving in Mixed-autonomy Trafficcited by 15
  2. 2021A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehiclescited by 10
  3. 2022Integrating Artificial Intelligence and Augmented Reality in Robotic Surgery: An Initial dVRK Study Using a Surgical Education Scenariocited by 7
  4. 2023Towards Safe Autonomous Driving Policies using a Neuro-Symbolic Deep Reinforcement Learning Approachcited by 7
  5. 2023Parameterized Decision-making with Multi-modal Perception for Autonomous Drivingcited by 7
  6. 2021Decision Making in Monopoly using a Hybrid Deep Reinforcement Learning Approachcited by 6
  7. 2022Prediction-aware and Reinforcement Learning based Altruistic Cooperative Drivingcited by 6
  8. 2021A Reinforcement Learning Benchmark for Autonomous Driving in Intersection Scenarioscited by 4
  9. 2021An Intelligent Self-driving Truck System For Highway Transportationcited by 3
  10. 2022Optimizing Trajectories for Highway Driving with Offline Reinforcement Learningcited by 3
  11. 2022Conditional Goal-oriented Trajectory Prediction for Interacting Vehicles with Vectorized Representationcited by 3
  12. 2025AI Recommendation Systems for Lane-Changing Using Adherence-Aware Reinforcement Learningcited by 3
12 more
  1. 2021Decentralized Cooperative Lane Changing at Freeway Weaving Areas Using Multi-Agent Deep Reinforcement Learningcited by 2
  2. 2021Weakly Supervised Reinforcement Learning for Autonomous Highway Driving via Virtual Safety Cagescited by 1
  3. 2021Benchmarking Lane-changing Decision-making for Deep Reinforcement Learningcited by 1
  4. 2021Lane Change Decision-Making through Deep Reinforcement Learningcited by 1
  5. 2022Learning-based social coordination to improve safety and robustness of cooperative autonomous vehicles in mixed trafficcited by 1
  6. 2024Mix Q-learning for Lane Changing: A Collaborative Decision-Making Method in Multi-Agent Deep Reinforcement Learningcited by 1
  7. 2024Enhanced Safety in Autonomous Driving: Integrating Latent State Diffusion Model for End-to-End Navigationcited by 1
  8. 2024A Safe Self-evolution Algorithm for Autonomous Driving Based on Data-Driven Risk Quantification Modelcited by 1
  9. 2025A Hybrid Input based Deep Reinforcement Learning for Lane Change Decision-Making of Autonomous Vehiclecited by 0
  10. 2025Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Drivingcited by 0
  11. 2025CauTraj: A Causal-Knowledge-Guided Framework for Lane-Changing Trajectory Planning of Autonomous Vehiclescited by 0
  12. 2026Multi-agent DRL-based Lane Change Decision Model for Cooperative Platooning in Mixed Trafficcited by 0