# Vec-QMDP: Vectorized POMDP Planning on CPUs for Real-Time Autonomous Driving

arXiv: https://arxiv.org/abs/2602.08334 · HTML: https://mlsanity.com/paper/2602.08334/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Imitation-learning end-to-end planners, one of 9 directions in End-to-end autonomous driving.
- Builds on 14 of its 36 references that are in the map.
- First posted on arXiv 9 Feb 2026.

## Placed in

- End-to-end autonomous driving / [Imitation-learning end-to-end planners](https://mlsanity.com/driving/imitation-learning-end-to-end-planners/)

## Builds on (most cited first, 14 of 14)

- [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)
- [Diffusion-Based Planning for Autonomous Driving with Flexible Guidance](https://mlsanity.com/paper/2501.15564/) (2025)
- [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)
- [T-MAC: CPU Renaissance via Table Lookup for Low-Bit LLM Deployment on Edge](https://mlsanity.com/paper/2407.00088/) (2024)
- [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)
- [POMDP-lite for Robust Robot Planning under Uncertainty](https://mlsanity.com/paper/1602.04875/) (2016)
- [Scaling Long-Horizon Online POMDP Planning via Rapid State Space Sampling](https://mlsanity.com/paper/2411.07032/) (2024)
- [Parallelization of Monte Carlo Tree Search in Continuous Domains](https://mlsanity.com/paper/2003.13741/) (2020)
- [Hi-Drive: Hierarchical POMDP Planning for Safe Autonomous Driving in Diverse Urban Environments](https://mlsanity.com/paper/2409.18411/) (2024)
- [Vectorized Online POMDP Planning](https://mlsanity.com/paper/2510.27191/) (2025)

## Abstract (arXiv)

Planning under uncertainty for real-world robotics tasks, such as autonomous driving, requires reasoning in enormous high-dimensional belief spaces, rendering the problem computationally intensive. While parallelization offers scalability, existing hybrid CPU-GPU solvers face critical bottlenecks due to host-device synchronization latency and branch divergence on SIMT architectures, limiting their utility for real-time planning and hindering real-robot deployment. We present Vec-QMDP, a CPU-native parallel planner that aligns POMDP search with modern CPUs' SIMD architecture, achieving 227×--1073× speedup over state-of-the-art serial planners. Vec-QMDP adopts a Data-Oriented Design (DOD), refactoring scattered, pointer-based data structures into contiguous, cache-efficient memory layouts. We further introduce a hierarchical parallelism scheme: distributing sub-trees across independent CPU cores and SIMD lanes, enabling fully vectorized tree expansion and collision checking. Efficiency is maximized with the help of UCB load balancing across trees and a vectorized STR-tree for coarse-level collision checking. Evaluated on large-scale autonomous driving benchmarks, Vec-QMDP achieves state-of-the-art planning performance with millisecond-level latency, establishing CPUs as a high-performance computing platform for large-scale planning under uncertainty.