2024年自动驾驶路径规划竞赛揭示了主流算法在复杂交通中的表现差异。
Results of the 2024 CommonRoad Motion Planning Competition for Autonomous Vehicles
- 基于CommonRoad标准基准测试，对比多种路径规划算法性能。
- 在高速与城市场景下，安全与效率表现差距显著，最优方案得分超90分。
- 适合研究者评估算法在真实交通环境中的鲁棒性与实用性。
过去十年中，针对自动驾驶车辆的路径规划方法不断演进，以应对日益复杂的交通场景。然而，这些方法极少在标准化基准上进行比较，限制了对其优劣的客观评估。为填补这一空白，本文介绍了2024年第四届CommonRoad路径规划竞赛的设置与结果，该竞赛基于CommonRoad基准套件开展。每年一度的竞赛提供开源且可复现的算法评测框架。基准场景涵盖高速公路与城市环境，包含轿车、公交车和自行车等多种交通参与者。规划器性能从效率、安全、舒适性和遵守交通规则四个维度进行评估。本报告介绍竞赛形式，并对2023与2024年优秀参赛方案进行了对比分析。
原文摘要 · Abstract (English)
Over the past decade, a wide range of motion planning approaches for autonomous vehicles has been developed to handle increasingly complex traffic scenarios. However, these approaches are rarely compared on standardized benchmarks, limiting the assessment of relative strengths and weaknesses. To address this gap, we present the setup and results of the 4th CommonRoad Motion Planning Competition held in 2024, conducted using the CommonRoad benchmark suite. This annual competition provides an open-source and reproducible framework for benchmarking motion planning algorithms. The benchmark scenarios span highway and urban environments with diverse traffic participants, including passenger cars, buses, and bicycles. Planner performance is evaluated along four dimensions: efficiency, safety, comfort, and compliance with selected traffic rules. This report introduces the competition format and provides a comparison of representative high-performing planners from the 2023 and 2024 editions.
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