论文标题
通过硬件辅助回忆加速自动驾驶的路径计划
Accelerating Path Planning for Autonomous Driving with Hardware-Assisted Memoization
论文作者
论文摘要
具有动态障碍的自动驾驶的路径计划构成了挑战，因为它需要执行更高维度的搜索（具有时间维度），同时仍会达到实时限制。本文提出了一种算法 - 硬件合作的方法，以使用高维搜索空间加速路径计划。首先，我们通过将节点和障碍物映射到较低维空间并记忆最近的搜索结果，从而减少了最近的邻居搜索和碰撞检测的时间。然后，我们提出了一个硬件扩展程序，以进行有效的记忆。现代处理器和周期级别模拟器的实验结果表明，硬件辅助的记忆大大减少了路径计划的执行时间。
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.
