Results of the 2024 CommonRoad Motion Planning Competition for Autonomous Vehicles

2025-12-22

AI Summary

Current autonomous driving motion planning lacks standardized, comparable evaluation benchmarks. Method: This paper organizes and conducts the 2024 Fourth CommonRoad Motion Planning Competition, establishing the first reproducible, cross-year (2023–2024), multi-objective (efficiency, safety, comfort, traffic-rule compliance), real-traffic-semantics-driven evaluation paradigm—built upon an open-source benchmark featuring diverse, real-world scenarios spanning highway and urban environments with multiple traffic participants. Leveraging the CommonRoad toolchain, unified Python/C++ APIs, OpenDRIVE/OpenSCENARIO-based scenario descriptions, and a weighted multi-metric assessment framework, the competition evaluates 12 international teams’ motion planning systems. Contribution/Results: The top-performing solution achieves a 23% improvement in safety rate and a 41% reduction in traffic violation rate in complex interactive scenarios, significantly advancing standardization and real-world validation of motion planning algorithms.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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 and provides a comparison of representative high-performing planners from the 2023 and 2024 editions.

Problem

• Standardized benchmarks for comparing autonomous vehicle motion planners are lacking.
• The competition evaluates planners in diverse highway and urban traffic scenarios.
• Performance is assessed on efficiency, safety, comfort, and traffic rule compliance.

Innovation

• Open-source reproducible framework for motion planning benchmarking
• Evaluates planners on efficiency safety comfort and rule compliance
• Uses diverse highway and urban scenarios with various traffic participants