Abstract
This study utilized a recently released crash dataset of Level 3 automated vehicles (AVs) made publicly available by the National Highway Traffic Safety Administration (NHTSA). The primary objective was to investigate various crash types and identify factors that influence crash severity. To achieve this, we employed a lightweight Natural Language Processing (NLP) pipeline to automatically extract relevant information from crash narratives and categorized the crashes into 15 distinct types. By analyzing the dependency triples derived from the crash narrative using the Stanford CoreNLP library, we determined the similarity between each narrative and the predefined categories. Our findings highlight safety-critical crash scenarios based on real-world data encompassing diverse operational design domains (ODDs), revealing a statistically significant impact of lighting conditions on crash severity. These results contribute to a better understanding of AV crashes and provide valuable insights to enhance the safe testing, integration, and development of AVs in real-world environments.
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