Accelerating Path Planning for Autonomous Driving with Hardware-Assisted Memoization
Robotics
2022-05-30 v2 Hardware Architecture
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.
Cite
@article{arxiv.2205.02754,
title = {Accelerating Path Planning for Autonomous Driving with Hardware-Assisted Memoization},
author = {Mulong Luo and G. Edward Suh},
journal= {arXiv preprint arXiv:2205.02754},
year = {2022}
}