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Bayesian extreme value analysis of kinematic-based surrogate measure of safety to detect crash-prone conditions in connected vehicles environment: A driving simulator experiment
Research article (Transportation Research Part C Emerging Technologies, 2022) · cited 31× · AI/ML
Bayesian extreme value analysis of kinematic-based surrogate measure of safety to detect crash-prone conditions in connected vehicles environment: A driving simulator experiment
Summary
Bayesian extreme value analysis of kinematic-based surrogate measure of safety to detect crash-prone conditions in connected vehicles environment: A driving simulator experiment is a scholarly article[1].
Key Facts
Bayesian extreme value analysis of kinematic-based surrogate measure of safety to detect crash-prone conditions in connected vehicles environment: A driving simulator experiment's instance of is recorded as scholarly article[2].
References
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Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). Bayesian extreme value analysis of kinematic-based surrogate measure of safety to detect crash-prone conditions in connected vehicles environment: A driving simulator experiment. Retrieved May 24, 2026, from https://4ort.xyz/entity/bayesian-extreme-value-analysis-of-kinematic-based-surrogate-measure-of-safety-to-detect-crash-prone-conditions-in-conne
MLA“Bayesian extreme value analysis of kinematic-based surrogate measure of safety to detect crash-prone conditions in connected vehicles environment: A driving simulator experiment.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/bayesian-extreme-value-analysis-of-kinematic-based-surrogate-measure-of-safety-to-detect-crash-prone-conditions-in-conne.
BibTeX@misc{4ortxyz_bayesian-extreme-value-analysis-of-kinematic-based-surrogate-measure-of-safety-to-detect-crash-prone-conditions-in-conne_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Bayesian extreme value analysis of kinematic-based surrogate measure of safety to detect crash-prone conditions in connected vehicles environment: A driving simulator experiment}}, year = {2026}, url = {https://4ort.xyz/entity/bayesian-extreme-value-analysis-of-kinematic-based-surrogate-measure-of-safety-to-detect-crash-prone-conditions-in-conne}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Bayesian extreme value analysis of kinematic-based surrogate measure of safety to detect crash-prone conditions in connected vehicles environment: A driving simulator experiment — https://4ort.xyz/entity/bayesian-extreme-value-analysis-of-kinematic-based-surrogate-measure-of-safety-to-detect-crash-prone-conditions-in-conne (retrieved 2026-05-24)
