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

arXiv: https://arxiv.org/abs/2510.07210 · HTML: https://mlsanity.com/paper/2510.07210/

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

## Placed in

- End-to-end autonomous driving / [Reinforcement learning for driving](https://mlsanity.com/driving/reinforcement-learning-for-driving/)
- Deep reinforcement learning methods / [RL for autonomous driving and traffic](https://mlsanity.com/deeprl/rl-for-autonomous-driving-and-traffic/)

## Builds on (most cited first, 25 of 25)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Planning-oriented Autonomous Driving](https://mlsanity.com/paper/2212.10156/) (2022)
- [Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2002.00444/) (2020)
- [VAD: Vectorized Scene Representation for Efficient Autonomous Driving](https://mlsanity.com/paper/2303.12077/) (2023)
- [End-to-end Autonomous Driving: Challenges and Frontiers](https://mlsanity.com/paper/2306.16927/) (2023)
- [DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models](https://mlsanity.com/paper/2402.12289/) (2024)
- [A Survey of Deep Learning Techniques for Autonomous Driving](https://mlsanity.com/paper/1910.07738/) (2019)
- [LMDrive: Closed-Loop End-to-End Driving with Large Language Models](https://mlsanity.com/paper/2312.07488/) (2023)
- [A Survey on Multimodal Large Language Models for Autonomous Driving](https://mlsanity.com/paper/2311.12320/) (2023)
- [Learning to Drive in a Day](https://mlsanity.com/paper/1807.00412/) (2018)
- [Parting with Misconceptions about Learning-based Vehicle Motion Planning](https://mlsanity.com/paper/2306.07962/) (2023)
- [EMMA: End-to-End Multimodal Model for Autonomous Driving](https://mlsanity.com/paper/2410.23262/) (2024)
- [Latent Variable Sequential Set Transformers For Joint Multi-Agent Motion Prediction](https://mlsanity.com/paper/2104.00563/) (2021)
- [Navigation with Large Language Models: Semantic Guesswork as a Heuristic for Planning](https://mlsanity.com/paper/2310.10103/) (2023)
- [LLM-Assist: Enhancing Closed-Loop Planning with Language-Based Reasoning](https://mlsanity.com/paper/2401.00125/) (2023)
- [Collision Avoidance in Pedestrian-Rich Environments with Deep Reinforcement Learning](https://mlsanity.com/paper/1910.11689/) (2019)
- [Large Language Models for Planning: A Comprehensive and Systematic Survey](https://mlsanity.com/paper/2505.19683/) (2025)
- [Asynchronous Large Language Model Enhanced Planner for Autonomous Driving](https://mlsanity.com/paper/2406.14556/) (2024)
- [End-to-End Autonomous Driving without Costly Modularization and 3D Manual Annotation](https://mlsanity.com/paper/2406.17680/) (2024)
- [VeriPlan: Integrating Formal Verification and LLMs into End-User Planning](https://mlsanity.com/paper/2502.17898/) (2025)
- [Closing the Planning-Learning Loop with Application to Autonomous Driving](https://mlsanity.com/paper/2101.03834/) (2021)
- [CAR-DESPOT: Causally-Informed Online POMDP Planning for Robots in Confounded Environments](https://mlsanity.com/paper/2304.06848/) (2023)
- [LEADER: Learning Attention over Driving Behaviors for Planning under Uncertainty](https://mlsanity.com/paper/2209.11422/) (2022)
- [VLAD: A VLM-Augmented Autonomous Driving Framework with Hierarchical Planning and Interpretable Decision Process](https://mlsanity.com/paper/2507.01284/) (2025)
- [Hybrid Deep Reinforcement Learning and Planning for Safe and Comfortable Automated Driving](https://mlsanity.com/paper/2301.00650/) (2022)

## 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.