Accelerating Path Planning for Autonomous Driving with Hardware-Assisted Memoization

TL;DR

This paper introduces a hardware-assisted memoization technique combined with algorithm-hardware co-optimization to significantly accelerate high-dimensional path planning in autonomous driving, meeting real-time constraints.

Contribution

It presents a novel hardware extension and a co-optimization approach that reduces search time by mapping to lower dimensions and memoizing results.

Findings

Significant reduction in path planning execution time

Effective mapping to lower-dimensional space for faster search

Hardware extension improves memoization efficiency

Abstract

Path planning for autonomous driving with dynamic obstacles poses a challenge because it needs to perform a higher-dimensional search (with time-dimension) while still meeting real-time constraints. This paper proposes an algorithm-hardware co-optimization approach to accelerate path planning with high-dimensional search space. First, we reduce the time for a nearest neighbor search and collision detection by mapping nodes and obstacles to a lower-dimensional space and memoizing recent search results. Then, we propose a hardware extension for efficient memoization. The experimental results on a modern processor and a cycle-level simulator show that the hardware-assisted memoization significantly reduces the execution time of path planning.