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

• URL: http://arxiv.org/abs/2602.08334v1
• Date: Mon, 09 Feb 2026 07:15:19 GMT
• Title: Vec-QMDP: Vectorized POMDP Planning on CPUs for Real-Time Autonomous Driving
• Abstract summary: We present Vec-QMDP, a CPU-performance parallel planner that aligns POMDP search with modern CPUs' SIMD architecture.<n>Vec-QMDP achieves $227times$--$1073times speedup over state-of-the-art serial planners.
• Score: 8.933077584568268
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 $227\times$--$1073\times$ 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.

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