Pre-crash scenarios for safety testing of autonomous vehicles: A clustering method for in-depth crash data
Open accessAbstract
Autonomous vehicles (AVs) provide an opportunity to enhance traffic safety. However, AVs market penetration is still restricted due to their safety concerns and dependability. For widespread adoption, it is crucial to thoroughly assess the safety response of AVs in various high-risk scenarios. To achieve this objective, a clustering method was used to construct typical testing scenarios based on the China In-depth Mobility Safety Study-Traffic Accident (CIMSS-TA) database. Initially, 222 car-to-powered two-wheelers (PTWs) crashes and 180 car-to-car crashes were reconstructed from CIMSS-TA database. Second, six variables were extracted and analyzed, including the motion of the two vehicles involved, relative movement, lighting condition, road condition, and visual obstruction. Third, these variables were clustered using the k-medoids algorithm, identifying five typical pre-crash scenarios for car-to-PTWs and seven for car-to-car. Additionally, we extracted the velocities and surrounding environmental information of the crash-involved parties to enrich the scenario description. The approach used in this study used in-depth case review and thus provided more insightful information for identifying and quantifying representative high-risk scenarios than prior studies that analyzed overall descriptive variables from Chinese crash databases. Furthermore, it is crucial to separately test car-to-car scenarios and car-to-PTWs scenarios due to their distinct motion characteristics, which significantly affect the resulting typical scenarios.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Cites
Silhouettes: A graphical aid to the interpretation and validation of cluster analysis
Rousseeuw, P. J. (1987) · Journal of Computational and Applied Mathematics · doi:10.1016/0377-0427(87)90125-7 · Cited by 21,392 · see citing works
Exploratory latent structure analysis using both identifiable and unidentifiable models
Goodman, L. A. (1974) · Biometrika · doi:10.1093/biomet/61.2.215 · Cited by 1,616 · see citing works
Kalra, N., & Paddock, S. M. (2016) · Transportation Research Part A: Policy and Practice · doi:10.1016/j.tra.2016.09.010 · Cited by 1,221 · see citing works
Metric and Euclidean properties of dissimilarity coefficients
Gower, J. C., & Legendre, P. (1986) · Journal of Classification · doi:10.1007/bf01896809 · Cited by 937 · see citing works
Survey on Scenario-Based Safety Assessment of Automated Vehicles
Riedmaier, S., Ponn, T., Ludwig, D., et al. (2020) · IEEE Access · doi:10.1109/access.2020.2993730 · Cited by 485 · see citing works
Autonomous Vehicles: Disengagements, Accidents and Reaction Times
Dixit, V. V., Chand, S., & Nair, D. J. (2016) · PLOS ONE · doi:10.1371/journal.pone.0168054 · Cited by 399 · see citing works
Detecting Stable Clusters Using Principal Component Analysis
Ben‐Hur, A., & Guyon, I. (2003) · Humana Press eBooks · doi:10.1385/1-59259-364-x:159 · Cited by 252 · see citing works
A systematic review of traffic conflict-based safety measures with a focus on application context
Arun, A., Haque, M. M., Washington, S. P., et al. (2021) · Analytic Methods in Accident Research · doi:10.1016/j.amar.2021.100185 · Cited by 204 · see citing works
Scanlon, J. M., Kusano, K. D., Daniel, T., et al. (2021) · Accident Analysis & Prevention · doi:10.1016/j.aap.2021.106454 · Cited by 178 · see citing works
Autonomous Vehicle: Security by Design
Chattopadhyay, A., Lam, K., & Tavva, Y. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3000797 · Cited by 159 · see citing works
Pre-crash scenarios at road junctions: A clustering method for car crash data
Nitsche, P., Thomas, P., Stütz, R., et al. (2017) · Accident Analysis & Prevention · doi:10.1016/j.aap.2017.07.011 · Cited by 125 · see citing works
Highly Automated Driving on Highways Based on Legal Safety
Vanholme, B., Gruyer, D., Lusetti, B., Glaser, S., & Mammar, S. (2013) · IEEE transactions on intelligent transportation systems (Print) · doi:10.1109/tits.2012.2225104 · Cited by 90 · see citing works
Ahmed, S. S., Pantangi, S. S., Eker, U., Fountas, G., Still, S. E., & Anastasopoulos, P. C. (2020) · Analytic Methods in Accident Research · doi:10.1016/j.amar.2020.100134 · Cited by 85 · see citing works
Sui, B., Lübbe, N., & Bärgman, J. (2019) · Accident Analysis & Prevention · doi:10.1016/j.aap.2019.07.018 · Cited by 70 · see citing works
Cui, Q., Ding, R., Wu, X., et al. (2019) · Vehicle System Dynamics · doi:10.1080/00423114.2019.1602732 · Cited by 67 · see citing works
Pan, D. L., Han, Y., Jin, Q., et al. (2021) · Accident Analysis & Prevention · doi:10.1016/j.aap.2021.106320 · Cited by 63 · see citing works
Adaptive Design of Experiments for Safety Evaluation of Automated Vehicles
Sun, J., Zhou, H., Xi, H., et al. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3130040 · Cited by 57 · see citing works
Self-citationSander, U., & Lübbe, N. (2018) · Accident Analysis & Prevention · doi:10.1016/j.aap.2018.01.010 · Cited by 54 · see citing works
Zhou, R., Huang, H., Lee, J. J., Huang, X., Chen, J., & Zhou, H. (2023) · Accident Analysis & Prevention · doi:10.1016/j.aap.2023.107218 · Cited by 52 · see citing works
Self-citationPowered two-wheeler crash scenario development
Atalar, D., & Thomas, P. (2019) · Accident Analysis & Prevention · doi:10.1016/j.aap.2019.02.001 · Cited by 33 · see citing works
Mining and comparative analysis of typical pre-crash scenarios from IGLAD
Hu, W., Xu, X., Zhou, Z., et al. (2020) · Accident Analysis & Prevention · doi:10.1016/j.aap.2020.105699 · Cited by 33 · see citing works
Tan, Z., Che, Y., Xiao, L., et al. (2020) · Accident Analysis & Prevention · doi:10.1016/j.aap.2020.105857 · Cited by 31 · see citing works
Wang, X., Peng, Y. L., Yi, S., et al. (2021) · Accident Analysis & Prevention · doi:10.1016/j.aap.2021.106150 · Cited by 27 · see citing works
An overview of car-to-two-wheeler accidents in China: guidance for AEB assessment
Sui, B., Zhou, S., Zhao, X., et al. (2017) · Cited by 15 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- Euro, 2021, Test protocol-aeb systems, Version, 3, 3
- Hu, 2018, A Research on Test Scenes of Two-wheeled Vehicles for Automatic Emergency Braking System Based on Real Accident Cases, Automot. Eng., 40
- WHO. (2018). Global Status Report on Road Safety 2018. In Geneva, Switzerland (Vol. 3, Issue 2).
Cited by
Still being checked.
- Authors
- Huang, H., Huang, X., Zhou, R., Zhou, H., Lee, J. J., & Cen, X.
- Affiliations
- Central South University (CN)
- University of Central Florida (US)
- Corresponding author
- Zhou, Rui
- Venue
- Accident Analysis & Prevention
- Publisher
- Elsevier BV
- Published
- 2024-08
- Volume
- 203
- Pages
- 107616
- ISSN
- 0001-4575 (ISSN-L)
- OpenAlex
- W4396758562
- Semantic Scholar
- 20e95bc103cdf61e9421fa8bb95a764fdd0e9783
- Cited by
- 25
- References
- 24
- Citations by year
- 2026: 10 · 2025: 12 · 2024: 3
- FWCI
- 6.59
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Accident Analysis & Prevention (publishedVersion)
- SSRN Electronic Journal (acceptedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (71971222)
- National Key Research and Development Program of China (2023YFB2504700, 2020YFB1600400)
- Topics
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Traffic and Road Safety
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
- Keywords
- autonomous vehicles, pre-crash scenarios, safety testing, K-medoids clustering, lighting conditions, road conditions, visual obstruction
- MeSH terms
- Accidents, Traffic, Algorithms, Automation, Automobile Driving, Automobiles, China, Humans, Safety, Cluster Analysis, Databases, Factual
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
