Results of the 2024 CommonRoad Motion Planning Competition for Autonomous Vehicles
Yanliang Huang, Xia Yan, Peiran Yin, Zhenduo Zhang, Zeyan Shao, Youran Wang, Haoliang Huang, and Matthias Althoff

TL;DR
The paper reports on the 2024 CommonRoad Motion Planning Competition, providing a standardized benchmarking framework for evaluating autonomous vehicle motion planning algorithms across diverse traffic scenarios and performance metrics.
Contribution
It introduces the setup and results of the 2024 competition, establishing a reproducible benchmark suite for comparing motion planning approaches in autonomous driving.
Findings
Comparison of high-performing planners from 2023 and 2024
Benchmark scenarios include highway and urban environments
Evaluation along efficiency, safety, comfort, and traffic rule compliance
Abstract
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…
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Taxonomy
TopicsAutonomous Vehicle Technology and Safety · Robotic Path Planning Algorithms · Traffic control and management
