Robotics: Science and Systems XXII
Vec-QMDP: Vectorized POMDP Planning on CPUs for Real-Time Autonomous Driving
Jin Xuanjin, Yanxin Dong, Bin Sun, Huan Xu, Zhihui Hao, Xianpeng Lang, Panpan CaiAbstract:
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.
Bibtex:
@INPROCEEDINGS{XuanjinJ-RSS-26,
AUTHOR = {Jin Xuanjin AND Yanxin Dong AND Bin Sun AND Huan Xu AND Zhihui Hao AND Xianpeng Lang AND Panpan Cai},
TITLE = {{Vec-QMDP: Vectorized POMDP Planning on CPUs for Real-Time Autonomous Driving}},
BOOKTITLE = {Proceedings of Robotics: Science and Systems},
YEAR = {2026},
ADDRESS = {Sydney, Australia},
MONTH = {July},
DOI = {10.15607/RSS.2026.XXII.179}
}
