用博弈论让自动驾驶车预判行人司机行为，生成更真实路径。
Search-Based Autonomous Vehicle Motion Planning Using Game Theory
- 将其他道路参与者视为智能体，而非静止障碍物。
- 实测可在全天气自动驾驶巴士上实时运行。
- 相比传统方法，路径更符合真实交通交互行为。
本文提出一种基于搜索的交互式运动规划方案，用于自动驾驶车辆（AV），采用博弈论方法。与传统基于搜索的方法不同，该方法将其他道路使用者（如驾驶员和行人）视为智能体，而非静态障碍物，从而生成更真实的自动驾驶路径。由于计算时间低，该方案可实现实时应用。所提出的运动规划方案在WATonoBus——一辆全气候自动驾驶电动接驳巴士——上通过实验进行了性能对比与验证。
原文摘要 · Abstract (English)
In this paper, we propose a search-based interactive motion planning scheme for autonomous vehicles (AVs), using a game-theoretic approach. In contrast to traditional search-based approaches, the newly developed approach considers other road users (e.g. drivers and pedestrians) as intelligent agents rather than static obstacles. This leads to the generation of a more realistic path for the AV. Due to the low computational time, the proposed motion planning scheme is implementable in real-time applications. The performance of the developed motion planning scheme is compared with existing motion planning techniques and validated through experiments using WATonoBus, an electrical all-weather autonomous shuttle bus.
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