Identifying typical pre-crash scenarios based on in-depth crash data with deep embedded clustering for autonomous vehicle safety testing
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Article number | 107218 |
Journal / Publication | Accident Analysis and Prevention |
Volume | 191 |
Online published | 17 Jul 2023 |
Publication status | Published - Oct 2023 |
Link(s)
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. © 2023 Elsevier Ltd.
Research Area(s)
- Autonomous vehicles, Clustering analysis, Stacked autoencoder, Testing scenarios
Citation Format(s)
Identifying typical pre-crash scenarios based on in-depth crash data with deep embedded clustering for autonomous vehicle safety testing. / Zhou, Rui; Huang, Helai; Lee, Jaeyoung et al.
In: Accident Analysis and Prevention, Vol. 191, 107218, 10.2023.
In: Accident Analysis and Prevention, Vol. 191, 107218, 10.2023.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review