mlsanityData to 7 Oct 2026

Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environment

arXiv 1909.11538 ↗First posted 18 Sep 2019cs.RO cs.AI cs.LGcited by 20

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Abstract (arXiv)

Autonomous lane changing is a critical feature for advanced autonomous driving systems, that involves several challenges such as uncertainty in other driver's behaviors and the trade-off between safety and agility. In this work, we develop a novel simulation environment that emulates these challenges and train a deep reinforcement learning agent that yields consistent performance in a variety of dynamic and uncertain traffic scenarios. Results show that the proposed data-driven approach performs significantly better in noisy environments compared to methods that rely solely on heuristics.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Sep 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 4; 2023: 5; 2024: 5; 2025: 2; 2026: 1.

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  1. 2023Motion Planning for Autonomous Driving: The State of the Art and Future Perspectivescited by 112
  2. 2021A Survey of Deep RL and IL for Autonomous Driving Policy Learningcited by 59
  3. 2024HighwayLLM: Decision-Making and Navigation in Highway Driving with RL-Informed Language Modelcited by 9
  4. 2021SAINT-ACC: Safety-Aware Intelligent Adaptive Cruise Control for Autonomous Vehicles Using Deep Reinforcement Learningcited by 6
  5. 2024Generalized Multi-Objective Reinforcement Learning with Envelope Updates in URLLC-enabled Vehicular Networkscited by 5
  6. 2022Prediction Based Decision Making for Autonomous Highway Drivingcited by 4
  7. 2022Self-Improving Safety Performance of Reinforcement Learning Based Driving with Black-Box Verification Algorithmscited by 4
  8. 2024Long and Short-Term Constraints Driven Safe Reinforcement Learning for Autonomous Drivingcited by 4
  9. 2025Perfect Prediction or Plenty of Proposals? What Matters Most in Planning for Autonomous Drivingcited by 4
  10. 2021Investigating Value of Curriculum Reinforcement Learning in Autonomous Driving Under Diverse Road and Weather Conditionscited by 3
  11. 2022Emerging Threats in Deep Learning-Based Autonomous Driving: A Comprehensive Surveycited by 3
  12. 2023Graph-based Prediction and Planning Policy Network (GP3Net) for scalable self-driving in dynamic environments using Deep Reinforcement Learningcited by 3
8 more
  1. 2022DeFIX: Detecting and Fixing Failure Scenarios with Reinforcement Learning in Imitation Learning Based Autonomous Drivingcited by 2
  2. 2023Comprehensive Training and Evaluation on Deep Reinforcement Learning for Automated Driving in Various Simulated Driving Maneuverscited by 2
  3. 2023Autonomous Slalom Maneuver Based on Expert Drivers' Behavior Using Convolutional Neural Networkcited by 1
  4. 2024Mix Q-learning for Lane Changing: A Collaborative Decision-Making Method in Multi-Agent Deep Reinforcement Learningcited by 1
  5. 2023Predictive Maneuver Planning with Deep Reinforcement Learning (PMP-DRL) for comfortable and safe autonomous drivingcited by 0
  6. 2024Safe Hybrid-Action Reinforcement Learning-Based Decision and Control for Discretionary Lane Changecited by 0
  7. 2025Learning to Drive Safely with Hybrid Optionscited by 0
  8. 2026Structured Reinforcement Learning for Bayesian Persuasion : Application to Intelligent Interactive Drivingcited by 0