mlsanityData to 7 Oct 2026

Hybrid Deep Reinforcement Learning and Planning for Safe and Comfortable Automated Driving

arXiv 2301.00650 ↗First posted 30 Dec 2022cs.RO cs.AIcited by 3

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

Abstract (arXiv)

We present a novel hybrid learning method, HyLEAR, for solving the collision-free navigation problem for self-driving cars in POMDPs. HyLEAR leverages interposed learning to embed knowledge of a hybrid planner into a deep reinforcement learner to faster determine safe and comfortable driving policies. In particular, the hybrid planner combines pedestrian path prediction and risk-aware path planning with driving-behavior rule-based reasoning such that the driving policies also take into account, whenever possible, the ride comfort and a given set of driving-behavior rules. Our experimental performance analysis over the CARLA-CTS1 benchmark of critical traffic scenarios revealed that HyLEAR can significantly outperform the selected baselines in terms of safety and ride comfort.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving. Builds on 15 of its 33 references that are in the map. First posted on arXiv 30 Dec 2022.

Builds on

15 of 33 references.

Closest ancestors · 2

Same direction, newest first

  1. 2021Improved Deep Reinforcement Learning with Expert Demonstrations for Urban Autonomous Driving18 Feb
  2. 2021A Survey of Deep RL and IL for Autonomous Driving Policy Learning6 Jan

Other references · 13

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  2. 2020Deep Reinforcement Learning for Autonomous Driving: A Surveycited by 698
  3. 2017Socially Aware Motion Planning with Deep Reinforcement Learningcited by 128
  4. 2018CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-drivingcited by 96
  5. 2019Model-free Deep Reinforcement Learning for Urban Autonomous Drivingcited by 96
  6. 2018Foundations for Restraining Bolts: Reinforcement Learning with LTLf/LDLf restraining specificationscited by 69
  7. 2018PEORL: Integrating Symbolic Planning and Hierarchical Reinforcement Learning for Robust Decision-Makingcited by 51
  8. 2019Collision Avoidance in Pedestrian-Rich Environments with Deep Reinforcement Learningcited by 44
  9. 2019Liability, Ethics, and Culture-Aware Behavior Specification using Rulebookscited by 41
  10. 2017Intention-Net: Integrating Planning and Deep Learning for Goal-Directed Autonomous Navigationcited by 19
  11. 2020Risk-Aware High-level Decisions for Automated Driving at Occluded Intersections with Reinforcement Learningcited by 19
  12. 2020Towards Learning Multi-agent Negotiations via Self-Playcited by 17
1 more
  1. 2019Faster and Safer Training by Embedding High-Level Knowledge into Deep Reinforcement Learningcited by 7

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 1; 2025: 1.

3 papers.

  1. 2023Nav-Q: Quantum Deep Reinforcement Learning for Collision-Free Navigation of Self-Driving Carscited by 6
  2. 2024Quantum Artificial Intelligence: A Brief Surveycited by 3
  3. 2025HyPlan: Hybrid Learning-Assisted Planning Under Uncertainty for Safe Autonomous Drivingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Learning-Based Behavior Planning for Automated Driving: Real-World Integration and Deployment
  2. 2020Safe Trajectory Planning Using Reinforcement Learning for Self Driving
  3. 2022Tackling Real-World Autonomous Driving using Deep Reinforcement Learning
  4. 2024Hybrid Imitation-Learning Motion Planner for Urban Driving
  5. 2026Efficient Multi-Modal Planning with Reward-Guided Preference Optimization for Autonomous Driving
  6. 2023Integration of Reinforcement Learning Based Behavior Planning With Sampling Based Motion Planning for Automated Driving
  7. 2022Discrete Control in Real-World Driving Environments using Deep Reinforcement Learning
  8. 2023Parameterized Decision-making with Multi-modal Perception for Autonomous Driving