共同道路犯罪:自动驾驶汽车临界测量工具箱

Yuanfei Lin, M. Althoff
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引用次数: 4

摘要

关键措施对于自动驾驶汽车捕捉周围环境的复杂性、触发紧急机动和验证安全性至关重要。然而,目前还没有公开可用的工具箱允许研究人员在任意交通场景下使用或评估大量的临界度量。为了解决这个问题,我们提出了CommonRoad-CriMe,这是一个开源工具箱,用于在统一框架中测量自动驾驶汽车的临界性。我们的工具箱涵盖了广泛的最先进的临界度量,并提供可视化信息以方便调试和展示。数值实验证明了我们的工具箱如何促进不同临界度量的比较和交通冲突的分析。我们的工具箱可在commonroad.in.tum.de找到。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CommonRoad-CriMe: A Toolbox for Criticality Measures of Autonomous Vehicles
Criticality measures are essential for autonomous vehicles to capture the complexity of the surrounding environment, trigger emergency maneuvers, and verify safety. However, there is currently no publicly available toolbox that allows researchers to use or evaluate a large number of criticality measures on arbitrary traffic scenarios. To address this issue, we present CommonRoad-CriMe, an open-source toolbox for measuring the criticality of autonomous vehicles in a unified framework. Our toolbox covers a wide range of state-of-the-art criticality measures and provides visualized information to facilitate debugging and showcasing. Numerical experiments demonstrate how our toolbox facilitates the comparison of different criticality measures and the analysis of traffic conflicts. Our toolbox is available at commonroad.in.tum.de.
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