mlsanityData to 14 Oct 2026

A Hybrid Input based Deep Reinforcement Learning for Lane Change Decision-Making of Autonomous Vehicle

arXiv 2509.01611 ↗First posted 1 Sep 2025cs.ROcited by 0

On the map: End-to-end autonomous driving → Reinforcement learning for driving

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Abstract (arXiv)

Lane change decision-making for autonomous vehicles is a complex but high-reward behavior. In this paper, we propose a hybrid input based deep reinforcement learning (DRL) algorithm, which realizes abstract lane change decisions and lane change actions for autonomous vehicles within traffic flow. Firstly, a surrounding vehicles trajectory prediction method is proposed to reduce the risk of future behavior of surrounding vehicles to ego vehicle, and the prediction results are input into the reinforcement learning model as additional information. Secondly, to comprehensively leverage environmental information, the model extracts feature from high-dimensional images and low-dimensional sensor data simultaneously. The fusion of surrounding vehicle trajectory prediction and multi-modal information are used as state space of reinforcement learning to improve the rationality of lane change decision. Finally, we integrate reinforcement learning macro decisions with end-to-end vehicle control to achieve a holistic lane change process. Experiments were conducted within the CARLA simulator, and the results demonstrated that the utilization of a hybrid state space significantly enhances the safety of vehicle lane change decisions.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving. Builds on 11 of its 26 references that are in the map. First posted on arXiv 1 Sep 2025.

Builds on

11 of 26 references.

References · 11

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2017CARLA: An Open Urban Driving Simulatorcited by 2,449
  3. 2018Learning to Drive in a Daycited by 273
  4. 2020Transformer Networks for Trajectory Forecastingcited by 181
  5. 2018Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learningcited by 111
  6. 2017Virtual-to-real Deep Reinforcement Learning: Continuous Control of Mobile Robots for Mapless Navigationcited by 98
  7. 2017Flow: A Modular Learning Framework for Mixed Autonomy Trafficcited by 73
  8. 2021Multi-modal Motion Prediction with Transformer-based Neural Network for Autonomous Drivingcited by 73
  9. 2018Automated Speed and Lane Change Decision Making using Deep Reinforcement Learningcited by 36
  10. 2019Driving in Dense Traffic with Model-Free Reinforcement Learningcited by 35
  11. 2019Lane Change Decision-making through Deep Reinforcement Learning with Rule-based Constraintscited by 24

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  1. 2024Let Hybrid A* Path Planner Obey Traffic Rules: A Deep Reinforcement Learning-Based Planning Framework
  2. 2024Discretionary Lane-Change Decision and Control via Parameterized Soft Actor-Critic for Hybrid Action Space
  3. 2024Mix Q-learning for Lane Changing: A Collaborative Decision-Making Method in Multi-Agent Deep Reinforcement Learning
  4. 2021Multi-agent Reinforcement Learning for Cooperative Lane Changing of Connected and Autonomous Vehicles in Mixed Traffic
  5. 2023Parameterized Decision-making with Multi-modal Perception for Autonomous Driving
  6. 2022Prediction Based Decision Making for Autonomous Highway Driving
  7. 2024Deep Reinforcement Learning in Autonomous Car Path Planning and Control: A Survey
  8. 2020Meta Reinforcement Learning-Based Lane Change Strategy for Autonomous Vehicles