mlsanityData to 7 Oct 2026

Predictive Maneuver Planning with Deep Reinforcement Learning (PMP-DRL) for comfortable and safe autonomous driving

arXiv 2306.09055 ↗First posted 15 Jun 2023cs.RO cs.AI cs.LGcited by 0

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

Abstract (arXiv)

This paper presents a Predictive Maneuver Planning with Deep Reinforcement Learning (PMP-DRL) model for maneuver planning. Traditional rule-based maneuver planning approaches often have to improve their abilities to handle the variabilities of real-world driving scenarios. By learning from its experience, a Reinforcement Learning (RL)-based driving agent can adapt to changing driving conditions and improve its performance over time. Our proposed approach combines a predictive model and an RL agent to plan for comfortable and safe maneuvers. The predictive model is trained using historical driving data to predict the future positions of other surrounding vehicles. The surrounding vehicles' past and predicted future positions are embedded in context-aware grid maps. At the same time, the RL agent learns to make maneuvers based on this spatio-temporal context information. Performance evaluation of PMP-DRL has been carried out using simulated environments generated from publicly available NGSIM US101 and I80 datasets. The training sequence shows the continuous improvement in the driving experiences. It shows that proposed PMP-DRL can learn the trade-off between safety and comfortability. The decisions generated by the recent imitation learning-based model are compared with the proposed PMP-DRL for unseen scenarios. The results clearly show that PMP-DRL can handle complex real-world scenarios and make better comfortable and safe maneuver decisions than rule-based and imitative models.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving. Builds on 21 of its 31 references that are in the map. First posted on arXiv 15 Jun 2023.

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21 of 31 references.

Closest ancestors · 1

Same direction, newest first

  1. 2021A Survey of Deep RL and IL for Autonomous Driving Policy Learning6 Jan

Other references · 20

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  3. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  4. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,873
  5. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,704
  6. 2017End-to-end Driving via Conditional Imitation Learningcited by 479
  7. 2018ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worstcited by 391
  8. 2022A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problemscited by 228
  9. 2020Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learningcited by 99
  10. 2019Model-free Deep Reinforcement Learning for Urban Autonomous Drivingcited by 96
  11. 2021VISTA 2.0: An Open, Data-driven Simulator for Multimodal Sensing and Policy Learning for Autonomous Vehiclescited by 81
  12. 2018Deep Imitative Models for Flexible Inference, Planning, and Controlcited by 75
8 more
  1. 2020Automated Lane Change Strategy using Proximal Policy Optimization-based Deep Reinforcement Learningcited by 30
  2. 2020Probabilistic End-to-End Vehicle Navigation in Complex Dynamic Environments with Multimodal Sensor Fusioncited by 23
  3. 2019Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environmentcited by 20
  4. 2020Trajectory Planning for Autonomous Vehicles Using Hierarchical Reinforcement Learningcited by 17
  5. 2021DeepTrack: Lightweight Deep Learning for Vehicle Path Prediction in Highwayscited by 16
  6. 2021Maneuver-based Trajectory Prediction for Self-driving Cars Using Spatio-temporal Convolutional Networkscited by 14
  7. 2022An efficient Deep Spatio-Temporal Context Aware decision Network (DST-CAN) for Predictive Manoeuvre Planningcited by 2
  8. 2021Spatio-Temporal Look-Ahead Trajectory Prediction using Memory Neural Networkcited by 1

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  1. 2023Review of Deep Reinforcement Learning for Autonomous Driving
  2. 2021Reinforcement Learning Based Safe Decision Making for Highway Autonomous Driving
  3. 2022Prediction Based Decision Making for Autonomous Highway Driving
  4. 2020Deep Reinforcement Learning for Autonomous Driving: A Survey
  5. 2025Evaluating Scenario-based Decision-making for Interactive Autonomous Driving Using Rational Criteria: A Survey
  6. 2022Discrete Control in Real-World Driving Environments using Deep Reinforcement Learning
  7. 2023Towards Safe Autonomous Driving Policies using a Neuro-Symbolic Deep Reinforcement Learning Approach
  8. 2021A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles