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.
翻译：过去十年间，为应对日益复杂的交通场景，自动驾驶车辆运动规划领域已发展出多种方法。然而，这些方法很少在标准化基准测试中进行比较，限制了对各自优劣的评估。为填补这一空白，我们介绍了2024年第四届CommonRoad运动规划竞赛的赛制与结果，该竞赛采用CommonRoad基准测试套件进行。这项年度竞赛为运动规划算法提供了开源且可复现的基准测试框架。测试场景涵盖高速公路与城市环境，包含乘用车、公交车、自行车等多种交通参与者。规划器性能从四个维度进行评估：效率、安全性、舒适度及对特定交通规则的遵守程度。本报告介绍了竞赛形式，并对2023年与2024年赛事中具有代表性的高性能规划器进行了比较分析。
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