2023年自动驾驶路径规划竞赛结果出炉，对比多算法在复杂交通场景表现。
Results of the 2023 CommonRoad Motion Planning Competition for Autonomous Vehicles
- 基于CommonRoad基准测试平台，统一评估路径规划算法。
- 涵盖高速与城市道路，包含多种交通参与者类型。
- 从效率、安全、舒适性及交通规则遵守度综合评分。
近年来，针对自动驾驶车辆的路径规划方法不断涌现，能够处理复杂的交通场景。然而，这些方法很少在相同的基准测试集上进行比较。为解决这一问题，本文展示了基于CommonRoad基准套件的大规模自动驾驶车辆路径规划竞赛结果。基准场景包含高速公路和城市环境，涉及乘客、汽车、公交车等多种交通参与者。解决方案根据效率、安全性、舒适性以及对部分交通规则的遵守情况进行评估。本报告总结了竞赛的主要成果。
原文摘要 · Abstract (English)
In recent years, different approaches for motion planning of autonomous vehicles have been proposed that can handle complex traffic situations. However, these approaches are rarely compared on the same set of benchmarks. To address this issue, we present the results of a large-scale motion planning competition for autonomous vehicles based on the CommonRoad benchmark suite. The benchmark scenarios contain highway and urban environments featuring various types of traffic participants, such as passengers, cars, buses, etc. The solutions are evaluated considering efficiency, safety, comfort, and compliance with a selection of traffic rules. This report summarizes the main results of the competition.
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