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

TL;DR

Vec-QMDP is a CPU-native parallel planner that significantly accelerates POMDP-based autonomous driving planning by leveraging SIMD architecture and hierarchical parallelism, enabling real-time performance on large-scale problems.

Contribution

The paper introduces Vec-QMDP, a novel CPU-based parallel planning algorithm that overcomes GPU bottlenecks, achieving high-speed, real-time planning for autonomous driving under uncertainty.

Findings

Achieves 227x-1073x speedup over serial planners.

Attains millisecond-level planning latency on large benchmarks.

Demonstrates state-of-the-art performance in autonomous driving tasks.

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.