# Hi-Drive: Hierarchical POMDP Planning for Safe Autonomous Driving in Diverse Urban Environments

arXiv: https://arxiv.org/abs/2409.18411 · HTML: https://mlsanity.com/paper/2409.18411/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 44 references that are in the map.
- First posted on arXiv 27 Sep 2024.

## Builds on (most cited first, 20 of 20)

- [NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles](https://mlsanity.com/paper/2106.11810/) (2021)
- [Parting with Misconceptions about Learning-based Vehicle Motion Planning](https://mlsanity.com/paper/2306.07962/) (2023)
- [DESPOT: Online POMDP Planning with Regularization](https://mlsanity.com/paper/1609.03250/) (2016)
- [PLUTO: Pushing the Limit of Imitation Learning-based Planning for Autonomous Driving](https://mlsanity.com/paper/2404.14327/) (2024)
- [Neural Map Prior for Autonomous Driving](https://mlsanity.com/paper/2304.08481/) (2023)
- [Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving](https://mlsanity.com/paper/1905.02680/) (2019)
- [Multimodal Probabilistic Model-Based Planning for Human-Robot Interaction](https://mlsanity.com/paper/1710.09483/) (2017)
- [EPSILON: An Efficient Planning System for Automated Vehicles in Highly Interactive Environments](https://mlsanity.com/paper/2108.07993/) (2021)
- [HyP-DESPOT: A Hybrid Parallel Algorithm for Online Planning under Uncertainty](https://mlsanity.com/paper/1802.06215/) (2018)
- [MAGIC: Learning Macro-Actions for Online POMDP Planning](https://mlsanity.com/paper/2011.03813/) (2020)
- [Motions in Microseconds via Vectorized Sampling-Based Planning](https://mlsanity.com/paper/2309.14545/) (2023)
- [Efficient Game-Theoretic Planning with Prediction Heuristic for Socially-Compliant Autonomous Driving](https://mlsanity.com/paper/2207.03673/) (2022)
- [LeTS-Drive: Driving in a Crowd by Learning from Tree Search](https://mlsanity.com/paper/1905.12197/) (2019)
- [Improving Automated Driving through POMDP Planning with Human Internal States](https://mlsanity.com/paper/2005.14549/) (2020)
- [LEADER: Learning Attention over Driving Behaviors for Planning under Uncertainty](https://mlsanity.com/paper/2209.11422/) (2022)
- [Safe POMDP Online Planning via Shielding](https://mlsanity.com/paper/2309.10216/) (2023)
- [Risk-averse Behavior Planning for Autonomous Driving under Uncertainty](https://mlsanity.com/paper/1812.01254/) (2018)
- [Efficient Hierarchical Robot Motion Planning Under Uncertainty and Hybrid Dynamics](https://mlsanity.com/paper/1802.04205/) (2018)
- [Density Planner: Minimizing Collision Risk in Motion Planning with Dynamic Obstacles using Density-based Reachability](https://mlsanity.com/paper/2210.02131/) (2022)
- [Graph-based Motion Planning for Automated Vehicles using Multi-model Branching and Admissible Heuristics](https://mlsanity.com/paper/2102.07812/) (2021)

## Built on by (most cited first, 2 of 2)

- [Vec-QMDP: Vectorized POMDP Planning on CPUs for Real-Time Autonomous Driving](https://mlsanity.com/paper/2602.08334/) (2026)
- [Breaking the Epistemic Trap: Active Perception Under Compound Uncertainty](https://mlsanity.com/paper/2605.26627/) (2026)

## Abstract (arXiv)

Uncertainties in dynamic road environments pose significant challenges for behavior and trajectory planning in autonomous driving. This paper introduces Hi-Drive, a hierarchical planning algorithm addressing uncertainties at both behavior and trajectory levels using a hierarchical Partially Observable Markov Decision Process (POMDP) formulation. Hi-Drive employs driver models to represent uncertain behavioral intentions of other vehicles and uses their parameters to infer hidden driving styles. By treating driver models as high-level decision-making actions, our approach effectively manages the exponential complexity inherent in POMDPs. To further enhance safety and robustness, Hi-Drive integrates a trajectory optimization based on importance sampling, refining trajectories using a comprehensive analysis of critical agents. Evaluations on real-world urban driving datasets demonstrate that Hi-Drive significantly outperforms state-of-the-art planning-based and learning-based methods across diverse urban driving situations in real-world benchmarks.