mlsanityData to 7 Oct 2026

HyPlan: Hybrid Learning-Assisted Planning Under Uncertainty for Safe Autonomous Driving

arXiv 2510.07210 ↗First posted 8 Oct 2025cs.RO cs.AIcited by 0

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

Abstract (arXiv)

We present a novel hybrid learning-assisted planning method, named HyPlan, for solving the collision-free navigation problem for self-driving cars in partially observable traffic environments. HyPlan combines methods for multi-agent behavior prediction, deep reinforcement learning with proximal policy optimization and approximated online POMDP planning with heuristic confidence-based vertical pruning to reduce its execution time without compromising safety of driving. Our experimental performance analysis on the CARLA-CTS2 benchmark of critical traffic scenarios with pedestrians revealed that HyPlan may navigate safer than selected relevant baselines and perform significantly faster than considered alternative online POMDP planners.

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 25 of its 33 references that are in the map. First posted on arXiv 8 Oct 2025.

Builds on

25 of 33 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022Hybrid Deep Reinforcement Learning and Planning for Safe and Comfortable Automated Driving30 Dec

Other references · 24

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2022Planning-oriented Autonomous Drivingcited by 1,068
  3. 2020Deep Reinforcement Learning for Autonomous Driving: A Surveycited by 698
  4. 2023VAD: Vectorized Scene Representation for Efficient Autonomous Drivingcited by 613
  5. 2023End-to-end Autonomous Driving: Challenges and Frontierscited by 485
  6. 2024DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Modelscited by 482
  7. 2019A Survey of Deep Learning Techniques for Autonomous Drivingcited by 401
  8. 2023LMDrive: Closed-Loop End-to-End Driving with Large Language Modelscited by 301
  9. 2023A Survey on Multimodal Large Language Models for Autonomous Drivingcited by 288
  10. 2018Learning to Drive in a Daycited by 266
  11. 2023Parting with Misconceptions about Learning-based Vehicle Motion Planningcited by 238
  12. 2024EMMA: End-to-End Multimodal Model for Autonomous Drivingcited by 226
12 more
  1. 2021Latent Variable Sequential Set Transformers For Joint Multi-Agent Motion Predictioncited by 131
  2. 2023Navigation with Large Language Models: Semantic Guesswork as a Heuristic for Planningcited by 88
  3. 2023LLM-Assist: Enhancing Closed-Loop Planning with Language-Based Reasoningcited by 59
  4. 2019Collision Avoidance in Pedestrian-Rich Environments with Deep Reinforcement Learningcited by 44
  5. 2024Asynchronous Large Language Model Enhanced Planner for Autonomous Drivingcited by 38
  6. 2025Large Language Models for Planning: A Comprehensive and Systematic Surveycited by 38
  7. 2024End-to-End Autonomous Driving without Costly Modularization and 3D Manual Annotationcited by 27
  8. 2025VeriPlan: Integrating Formal Verification and LLMs into End-User Planningcited by 19
  9. 2021Closing the Planning-Learning Loop with Application to Autonomous Drivingcited by 12
  10. 2023CAR-DESPOT: Causally-Informed Online POMDP Planning for Robots in Confounded Environmentscited by 11
  11. 2022LEADER: Learning Attention over Driving Behaviors for Planning under Uncertaintycited by 9
  12. 2025VLAD: A VLM-Augmented Autonomous Driving Framework with Hierarchical Planning and Interpretable Decision Processcited by 4

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  1. 2021A Safe Hierarchical Planning Framework for Complex Driving Scenarios based on Reinforcement Learning
  2. 2020Safe Trajectory Planning Using Reinforcement Learning for Self Driving
  3. 2024Hybrid Imitation-Learning Motion Planner for Urban Driving
  4. 2026Learning-Based Behavior Planning for Automated Driving: Real-World Integration and Deployment
  5. 2025Goal-conditioned Hierarchical Reinforcement Learning for Sample-efficient and Safe Autonomous Driving at Intersections
  6. 2026Efficient Multi-Modal Planning with Reward-Guided Preference Optimization for Autonomous Driving
  7. 2021Building Safer Autonomous Agents by Leveraging Risky Driving Behavior Knowledge
  8. 2021SafetyNet: Safe planning for real-world self-driving vehicles using machine-learned policies