These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

146 related articles for article (PubMed ID: 36681017)

  • 21. Evaluation of smartphone interactions on drivers' brain function and vehicle control in an immersive simulated environment.
    Baker JM; Bruno JL; Piccirilli A; Gundran A; Harbott LK; Sirkin DM; Marzelli M; Hosseini SMH; Reiss AL
    Sci Rep; 2021 Jan; 11(1):1998. PubMed ID: 33479322
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Examination of drivers' cell phone use behavior at intersections by using naturalistic driving data.
    Xiong H; Bao S; Sayer J; Kato K
    J Safety Res; 2015 Sep; 54():89-93. PubMed ID: 26403907
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Determining the risk of driver-at-fault events associated with common distraction types using naturalistic driving data.
    Liang OS; Yang CC
    J Safety Res; 2021 Dec; 79():45-50. PubMed ID: 34848019
    [TBL] [Abstract][Full Text] [Related]  

  • 24. How safe is tuning a radio?: using the radio tuning task as a benchmark for distracted driving.
    Lee JY; Lee JD; Bärgman J; Lee J; Reimer B
    Accid Anal Prev; 2018 Jan; 110():29-37. PubMed ID: 29101787
    [TBL] [Abstract][Full Text] [Related]  

  • 25. Factors affecting drivers' off-road glance behavior while interacting with in-vehicle voice interfaces.
    Zhang F; Roberts SC
    Accid Anal Prev; 2023 Jan; 179():106883. PubMed ID: 36356510
    [TBL] [Abstract][Full Text] [Related]  

  • 26. The Effect of Partial Automation on Driver Attention: A Naturalistic Driving Study.
    Gaspar J; Carney C
    Hum Factors; 2019 Dec; 61(8):1261-1276. PubMed ID: 30920852
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Design and evaluation of cooperative human-machine interface for changing lanes in conditional driving automation.
    Muslim H; Kiu Leung C; Itoh M
    Accid Anal Prev; 2022 Sep; 174():106719. PubMed ID: 35660872
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Driver behavior while using Level 2 vehicle automation: a hybrid naturalistic study.
    Cooper JM; Crabtree KW; McDonnell AS; May D; Strayer SC; Tsogtbaatar T; Cook DR; Alexander PA; Sanbonmatsu DM; Strayer DL
    Cogn Res Princ Implic; 2023 Dec; 8(1):71. PubMed ID: 38117387
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Effective cues for accelerating young drivers' time to transfer control following a period of conditional automation.
    Wright TJ; Agrawal R; Samuel S; Wang Y; Zilberstein S; Fisher DL
    Accid Anal Prev; 2018 Jul; 116():14-20. PubMed ID: 29031513
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Classification of Driver Distraction: A Comprehensive Analysis of Feature Generation, Machine Learning, and Input Measures.
    McDonald AD; Ferris TK; Wiener TA
    Hum Factors; 2020 Sep; 62(6):1019-1035. PubMed ID: 31237788
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Is take-over time all that matters? The impact of visual-cognitive load on driver take-over quality after conditionally automated driving.
    Zeeb K; Buchner A; Schrauf M
    Accid Anal Prev; 2016 Jul; 92():230-9. PubMed ID: 27107472
    [TBL] [Abstract][Full Text] [Related]  

  • 32. Safety critical event prediction through unified analysis of driver and vehicle volatilities: Application of deep learning methods.
    Arvin R; Khattak AJ; Qi H
    Accid Anal Prev; 2021 Mar; 151():105949. PubMed ID: 33385957
    [TBL] [Abstract][Full Text] [Related]  

  • 33. A Machine-Learning Approach to Distinguish Passengers and Drivers Reading While Driving.
    Torres R; Ohashi O; Pessin G
    Sensors (Basel); 2019 Jul; 19(14):. PubMed ID: 31330929
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Evaluation of driver demand for in-vehicle information: An integrated method combining clustering and multivariate ordered probit model.
    Li J; Zhang W; Zhu D; Feng Z; He Z; Yue Q; Huang Z
    J Safety Res; 2023 Jun; 85():222-233. PubMed ID: 37330872
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Characteristics of driver cell phone use and their influence on driving performance: A naturalistic driving study.
    Wang X; Xu R; Asmelash A; Xing Y; Lee C
    Accid Anal Prev; 2020 Dec; 148():105845. PubMed ID: 33120181
    [TBL] [Abstract][Full Text] [Related]  

  • 36. An XGBoost approach to detect driver visual distraction based on vehicle dynamics.
    Guo Y; Ding H; ShangGuan X
    Traffic Inj Prev; 2023; 24(6):458-465. PubMed ID: 37272712
    [TBL] [Abstract][Full Text] [Related]  

  • 37. How is the duration of distraction related to safety-critical events? Harnessing naturalistic driving data to explore the role of driving instability.
    Ahmad N; Arvin R; Khattak AJ
    J Safety Res; 2023 Jun; 85():15-30. PubMed ID: 37330865
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Driving behaviour while self-regulating mobile phone interactions: A human-machine system approach.
    Oviedo-Trespalacios O; Haque MM; King M; Demmel S
    Accid Anal Prev; 2018 Sep; 118():253-262. PubMed ID: 29653674
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Effects of automation trust in drivers' visual distraction during automation.
    Zhang Y; Ma J; Pan C; Chang R
    PLoS One; 2021; 16(9):e0257201. PubMed ID: 34520500
    [TBL] [Abstract][Full Text] [Related]  

  • 40. The prevalence of distraction among passenger vehicle drivers: a roadside observational approach.
    Huisingh C; Griffin R; McGwin G
    Traffic Inj Prev; 2015; 16(2):140-6. PubMed ID: 24761827
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 8.