385 related articles for article (PubMed ID: 34273622)
1. Crash comparison of autonomous and conventional vehicles using pre-crash scenario typology.
Liu Q; Wang X; Wu X; Glaser Y; He L
Accid Anal Prev; 2021 Sep; 159():106281. PubMed ID: 34273622
[TBL] [Abstract][Full Text] [Related]
2. Analysis of pre-crash scenarios and contributing factors for autonomous vehicle crashes at intersections.
Liu Q; Wang X; Liu S; Yu C; Glaser Y
Accid Anal Prev; 2024 Feb; 195():107383. PubMed ID: 37984113
[TBL] [Abstract][Full Text] [Related]
3. Advancing investigation of automated vehicle crashes using text analytics of crash narratives and Bayesian analysis.
Lee S; Arvin R; Khattak AJ
Accid Anal Prev; 2023 Mar; 181():106932. PubMed ID: 36580765
[TBL] [Abstract][Full Text] [Related]
4. Causation analysis of crashes and near crashes using naturalistic driving data.
Wang X; Liu Q; Guo F; Fang S; Xu X; Chen X
Accid Anal Prev; 2022 Nov; 177():106821. PubMed ID: 36055150
[TBL] [Abstract][Full Text] [Related]
5. Automated vehicle crash sequences: Patterns and potential uses in safety testing.
Song Y; Chitturi MV; Noyce DA
Accid Anal Prev; 2021 Apr; 153():106017. PubMed ID: 33578268
[TBL] [Abstract][Full Text] [Related]
6. How would autonomous vehicles behave in real-world crash scenarios?
Zhou R; Zhang G; Huang H; Wei Z; Zhou H; Jin J; Chang F; Chen J
Accid Anal Prev; 2024 Jul; 202():107572. PubMed ID: 38657314
[TBL] [Abstract][Full Text] [Related]
7. A comparative study of collision types between automated and conventional vehicles using Bayesian probabilistic inferences.
Novat N; Kidando E; Kutela B; Kitali AE
J Safety Res; 2023 Feb; 84():251-260. PubMed ID: 36868654
[TBL] [Abstract][Full Text] [Related]
8. Integration of automated vehicles in mixed traffic: Evaluating changes in performance of following human-driven vehicles.
Mahdinia I; Mohammadnazar A; Arvin R; Khattak AJ
Accid Anal Prev; 2021 Mar; 152():106006. PubMed ID: 33556655
[TBL] [Abstract][Full Text] [Related]
9. Safety in higher level automated vehicles: Investigating edge cases in crashes of vehicles equipped with automated driving systems.
Moradloo N; Mahdinia I; Khattak AJ
Accid Anal Prev; 2024 Aug; 203():107607. PubMed ID: 38723333
[TBL] [Abstract][Full Text] [Related]
10. Autonomous driving testing scenario generation based on in-depth vehicle-to-powered two-wheeler crash data in China.
Wang X; Peng Y; Xu T; Xu Q; Wu X; Xiang G; Yi S; Wang H
Accid Anal Prev; 2022 Oct; 176():106812. PubMed ID: 36054982
[TBL] [Abstract][Full Text] [Related]
11. Determination of functional scenarios for intersection collisions.
Bangert LG; Lubash T; Scanlon JM; Kusano KD; Riexinger LE
Accid Anal Prev; 2023 Dec; 193():107326. PubMed ID: 37793217
[TBL] [Abstract][Full Text] [Related]
12. Exploratory analysis of automated vehicle crashes in California: A text analytics & hierarchical Bayesian heterogeneity-based approach.
Boggs AM; Wali B; Khattak AJ
Accid Anal Prev; 2020 Feb; 135():105354. PubMed ID: 31790970
[TBL] [Abstract][Full Text] [Related]
13. Communication via motion - Suitability of automated vehicle movements to negotiate the right of way in road bottleneck scenarios.
Rettenmaier M; Dinkel S; Bengler K
Appl Ergon; 2021 Sep; 95():103438. PubMed ID: 33895469
[TBL] [Abstract][Full Text] [Related]
14. Comparison of automated vehicle struck-from-behind crash rates with national rates using naturalistic data.
Goodall NJ
Accid Anal Prev; 2021 May; 154():106056. PubMed ID: 33756426
[TBL] [Abstract][Full Text] [Related]
15. Comparison of teen and adult driver crash scenarios in a nationally representative sample of serious crashes.
McDonald CC; Curry AE; Kandadai V; Sommers MS; Winston FK
Accid Anal Prev; 2014 Nov; 72():302-8. PubMed ID: 25103321
[TBL] [Abstract][Full Text] [Related]
16. Road safety from the perspective of driver gender and age as related to the injury crash frequency and road scenario.
Russo F; Biancardo SA; Dell'Acqua G
Traffic Inj Prev; 2014; 15(1):25-33. PubMed ID: 24279963
[TBL] [Abstract][Full Text] [Related]
17. Exploring the mechanism of crashes with automated vehicles using statistical modeling approaches.
Wang S; Li Z
PLoS One; 2019; 14(3):e0214550. PubMed ID: 30921396
[TBL] [Abstract][Full Text] [Related]
18. Comprehensive target populations for current active safety systems using national crash databases.
Kusano KD; Gabler HC
Traffic Inj Prev; 2014; 15(7):753-61. PubMed ID: 24433115
[TBL] [Abstract][Full Text] [Related]
19. Identifying typical pre-crash scenarios based on in-depth crash data with deep embedded clustering for autonomous vehicle safety testing.
Zhou R; Huang H; Lee J; Huang X; Chen J; Zhou H
Accid Anal Prev; 2023 Oct; 191():107218. PubMed ID: 37467602
[TBL] [Abstract][Full Text] [Related]
20. Characteristics of rear-end crashes involving passenger vehicles with automatic emergency braking.
Cicchino JB; Zuby DS
Traffic Inj Prev; 2019; 20(sup1):S112-S118. PubMed ID: 31381436
[No Abstract] [Full Text] [Related]
[Next] [New Search]
