319 related articles for article (PubMed ID: 34673839)
1. Increase in rear-end collision risk by acute stress-induced fatigue in on-road truck driving.
Minusa S; Mizuno K; Ojiro D; Tanaka T; Kuriyama H; Yamano E; Kuratsune H; Watanabe Y
PLoS One; 2021; 16(10):e0258892. PubMed ID: 34673839
[TBL] [Abstract][Full Text] [Related]
2. Relationship between truck driver fatigue and rear-end collision risk.
Mizuno K; Ojiro D; Tanaka T; Minusa S; Kuriyama H; Yamano E; Kuratsune H; Watanabe Y
PLoS One; 2020; 15(9):e0238738. PubMed ID: 32915847
[TBL] [Abstract][Full Text] [Related]
3. Heavy-truck drivers' following behavior with intervention of an integrated, in-vehicle crash warning system: a field evaluation.
Bao S; LeBlanc DJ; Sayer JR; Flannagan C
Hum Factors; 2012 Oct; 54(5):687-97. PubMed ID: 23156615
[TBL] [Abstract][Full Text] [Related]
4. Steering or braking avoidance response in SHRP2 rear-end crashes and near-crashes: A decision tree approach.
Sarkar A; Hickman JS; McDonald AD; Huang W; Vogelpohl T; Markkula G
Accid Anal Prev; 2021 May; 154():106055. PubMed ID: 33691227
[TBL] [Abstract][Full Text] [Related]
5. Analysis of effects of driver's evasive action time on rear-end collision risk using a driving simulator.
Shah D; Lee C
J Safety Res; 2021 Sep; 78():242-250. PubMed ID: 34399920
[TBL] [Abstract][Full Text] [Related]
6. Investigation on occupant injury severity in rear-end crashes involving trucks as the front vehicle in Beijing area, China.
Yuan Q; Lu M; Theofilatos A; Li YB
Chin J Traumatol; 2017 Feb; 20(1):20-26. PubMed ID: 28162916
[TBL] [Abstract][Full Text] [Related]
7. A rear-end collision risk assessment model based on drivers' collision avoidance process under influences of cell phone use and gender-A driving simulator based study.
Li X; Yan X; Wu J; Radwan E; Zhang Y
Accid Anal Prev; 2016 Dec; 97():1-18. PubMed ID: 27565040
[TBL] [Abstract][Full Text] [Related]
8. Effects of collision warning characteristics on driving behaviors and safety in connected vehicle environments.
Zhao W; Gong S; Zhao D; Liu F; Sze NN; Huang H
Accid Anal Prev; 2023 Jun; 186():107053. PubMed ID: 37030178
[TBL] [Abstract][Full Text] [Related]
9. Autonomous emergency braking systems adapted to snowy road conditions improve drivers' perceived safety and trust.
Koglbauer I; Holzinger J; Eichberger A; Lex C
Traffic Inj Prev; 2018 Apr; 19(3):332-337. PubMed ID: 29227692
[TBL] [Abstract][Full Text] [Related]
10. Gender differences of young drivers on injury severity outcome of highway crashes.
Amarasingha N; Dissanayake S
J Safety Res; 2014 Jun; 49():113-20. PubMed ID: 24913475
[TBL] [Abstract][Full Text] [Related]
11. Driver behaviors assisted by different human machine interfaces to avoid rear-end collisions during level 2 automated driving.
Yang B; Saito T; Wang Z; Kitazaki S; Nakano K
Traffic Inj Prev; 2023; 24(6):475-481. PubMed ID: 37339499
[TBL] [Abstract][Full Text] [Related]
12. The role of off-path glances: A quantitative analysis of rear-end conflicts involving Chinese professional truck drivers as the striking partners.
Pipkorn L; Bianchi Piccinini G
J Safety Res; 2020 Feb; 72():259-266. PubMed ID: 32199571
[TBL] [Abstract][Full Text] [Related]
13. A comparison of tactile, visual, and auditory warnings for rear-end collision prevention in simulated driving.
Scott JJ; Gray R
Hum Factors; 2008 Apr; 50(2):264-75. PubMed ID: 18516837
[TBL] [Abstract][Full Text] [Related]
14. Analysis of naturalistic driving videos of fleet services drivers to estimate driver error and potentially distracting behaviors as risk factors for rear-end versus angle crashes.
Harland KK; Carney C; McGehee D
Traffic Inj Prev; 2016 Jul; 17(5):465-71. PubMed ID: 26760293
[TBL] [Abstract][Full Text] [Related]
15. Driving errors that predict simulated rear-end collisions in drivers with multiple sclerosis.
Krasniuk S; Classen S; Morrow SA
Traffic Inj Prev; 2021; 22(3):212-217. PubMed ID: 33688770
[TBL] [Abstract][Full Text] [Related]
16. Experiences of model year 2011 Dodge and Jeep owners with collision avoidance and related technologies.
Cicchino JB; McCartt AT
Traffic Inj Prev; 2015; 16():298-303. PubMed ID: 24983299
[TBL] [Abstract][Full Text] [Related]
17. A novel collision warning system based on the visual road environment schema: An examination from vehicle and driver characteristics.
Li Z; Yu B; Wang Y; Chen Y; Kong Y; Xu Y
Accid Anal Prev; 2023 Sep; 190():107154. PubMed ID: 37343457
[TBL] [Abstract][Full Text] [Related]
18. In-vehicle warnings for work zone and related rear-end collisions: A driving simulator experiment.
Hang J; Yan X; Li X; Duan K
Accid Anal Prev; 2022 Sep; 174():106768. PubMed ID: 35820314
[TBL] [Abstract][Full Text] [Related]
19. Factors Associated with Fatigued Driving among Australian Truck Drivers: A Cross-Sectional Study.
Ren X; Pritchard E; van Vreden C; Newnam S; Iles R; Xia T
Int J Environ Res Public Health; 2023 Feb; 20(3):. PubMed ID: 36768095
[TBL] [Abstract][Full Text] [Related]
20. Effects of peripheral transverse line markings on drivers' speed and headway choice and crash risk in car-following: A naturalistic observation study.
Ding N; Zhu S; Jiao N; Liu B
Accid Anal Prev; 2020 Oct; 146():105701. PubMed ID: 32823033
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
