United States Department of Transportation
i
Labeling Roads with Different Types of Automated Driving Functional Requirements using Machine Learning
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2020-07-07
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[PDF-394.95 KB]
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Edition:Final Research Report
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Abstract:Automated vehicles (AVs) should be deployed gradually and geometrically selectively to ensure safety. Frequent collisions of AVs in certain driving scenarios, such as in dark streets or crowded areas, have posed wide concerns of AV safety. People want to know what kinds of driving circumstances or areas are easy for AVs and what are relatively hard and how to quantify the degree. In the new Federal guidance - Automated Vehicles 3.0: Preparing for the Future of Transportation 3.0, released in 2018, the Department of Transportation proposed the concept of Operational Design Domain (ODD) to describe the driving complexity considering roadway types, geographic area, and speed range. This concept sheds a light on the evaluation of the difficulty of driving, but it is still not clear how to apply it in practice as neither the automation level nor the ODD provide a numerical solution, hence likely resulting in subjective, incomplete, and inherently somewhat ambiguous analysis to fully describe the complex nature of real-world traffic, and thus causing biased confidence and disqualification of AVs for public deployment. In order to reduce the risks and lay the foundation of autonomous vehicles deployment, this project aims to label the roads of the city with different risk levels based on large scale real-world datasets of Pittsburgh city including multi-dimensional and multi-fidelity data. The research team develops a framework to identify typical driving scenarios based on Nonparametric Bayesian (NPBayes) methods. The team further visualizes the scenarios using the velocity fields which help provide a reference for characterizing the complexity of the possible situations and the risk level.
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