Identifying typical pre-crash scenarios based on in-depth crash data with deep embedded clustering for autonomous vehicle safety testing

Abstract

Choosing appropriate scenarios is critical for autonomous vehicles (AVs) safety testing. Real-world crash scenarios can be used as critical scenarios to test the safety performance of AVs. As one of the dominant types of traffic crashes, the car to powered-two-wheelers (PTWs) crash results in a higher possibility of fatality than ordinary car-to-car crashes. Generally, typical testing scenarios are chosen according to the subjective understanding of the safety experts with limited static features of crashes (e.g., geometric features, weather). This study introduced a novel method to identify typical car-to-PTWs crash scenarios based on real-world crashes with dynamic pre-crash features investigated from the China In-depth Mobility Safety Study-Traffic Accident (CIMSS-TA) database. First, we present crash data collection and construction methods of the CIMSS-TA database to construct testing scenarios. Second, the stacked autoencoder methods are used to learn and obtain embedded features from the high-dimensional data. Third, the extracted features are clustered using k-means clustering algorithm, and then the clustering results are interpreted. Six typical car-to-PTWs scenarios are obtained with the proposed processes. This study introduces a typical high-risk scenario construction method based on deep embedded clustering. Unlike existing researches, the proposed method eliminates the negative impacts of manually selecting clustering variables and provides a more detailed scenario description. As a result, the typical scenarios obtained from AV testing are more robust.

Keywords: Autonomous vehicles; Clustering analysis; Stacked autoencoder; Testing scenarios.