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 *

182 related articles for article (PubMed ID: 31200499)

  • 1. A Driver's Physiology Sensor-Based Driving Risk Prediction Method for Lane-Changing Process Using Hidden Markov Model.
    Li Y; Wang F; Ke H; Wang LL; Xu CC
    Sensors (Basel); 2019 Jun; 19(12):. PubMed ID: 31200499
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A proactive lane-changing risk prediction framework considering driving intention recognition and different lane-changing patterns.
    Shangguan Q; Fu T; Wang J; Fang S; Fu L
    Accid Anal Prev; 2022 Jan; 164():106500. PubMed ID: 34823098
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Effect of driver's age and side of impact on crash severity along urban freeways: a mixed logit approach.
    Haleem K; Gan A
    J Safety Res; 2013 Sep; 46():67-76. PubMed ID: 23932687
    [TBL] [Abstract][Full Text] [Related]  

  • 4. A proactive crash risk prediction framework for lane-changing behavior incorporating individual driving styles.
    Zhang Y; Chen Y; Gu X; Sze NN; Huang J
    Accid Anal Prev; 2023 Aug; 188():107072. PubMed ID: 37137214
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. Quantifying visual road environment to establish a speeding prediction model: An examination using naturalistic driving data.
    Yu B; Chen Y; Bao S
    Accid Anal Prev; 2019 Aug; 129():289-298. PubMed ID: 31177040
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A Novel Intelligent Approach to Lane-Change Behavior Prediction for Intelligent and Connected Vehicles.
    Du L; Chen W; Ji J; Pei Z; Tong B; Zheng H
    Comput Intell Neurosci; 2022; 2022():9516218. PubMed ID: 35082845
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Multi-parameter prediction of drivers' lane-changing behaviour with neural network model.
    Peng J; Guo Y; Fu R; Yuan W; Wang C
    Appl Ergon; 2015 Sep; 50():207-17. PubMed ID: 25959336
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Lane Departure Warning Mechanism of Limited False Alarm Rate using Extreme Learning Residual Network and ϵ-greedy LSTM.
    Gao Q; Yin H; Zhang W
    Sensors (Basel); 2020 Jan; 20(3):. PubMed ID: 31979330
    [TBL] [Abstract][Full Text] [Related]  

  • 10. The influence of curbs on driver behaviors in four-lane rural highways--A driving simulator based study.
    Yang Q; Overton R; Han LD; Yan X; Richards SH
    Accid Anal Prev; 2013 Jan; 50():1289-97. PubMed ID: 23084096
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Driving Intention Recognition of Surrounding Vehicles Based on a Time-Sequenced Weights Hidden Markov Model for Autonomous Driving.
    Liu P; Qu T; Gao H; Gong X
    Sensors (Basel); 2023 Oct; 23(21):. PubMed ID: 37960461
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Aggressive driving behavior prediction considering driver's intention based on multivariate-temporal feature data.
    Xu W; Wang J; Fu T; Gong H; Sobhani A
    Accid Anal Prev; 2022 Jan; 164():106477. PubMed ID: 34813934
    [TBL] [Abstract][Full Text] [Related]  

  • 13. How eye movement and driving performance vary before, during, and after entering a long expressway tunnel: considering the differences of novice and experienced drivers under daytime and nighttime conditions.
    Wang Y; Wang L; Wang C; Zhao Y
    Springerplus; 2016; 5():538. PubMed ID: 27186502
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Portable System for Monitoring and Controlling Driver Behavior and the Use of a Mobile Phone While Driving.
    Khandakar A; Chowdhury MEH; Ahmed R; Dhib A; Mohammed M; Al-Emadi NAMA; Michelson D
    Sensors (Basel); 2019 Mar; 19(7):. PubMed ID: 30935150
    [TBL] [Abstract][Full Text] [Related]  

  • 15. The effect of varying levels of vehicle automation on drivers' lane changing behaviour.
    Madigan R; Louw T; Merat N
    PLoS One; 2018; 13(2):e0192190. PubMed ID: 29466402
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Effect of traffic density on drivers' lane change and overtaking maneuvers in freeway situation-A driving simulator-based study.
    Yang L; Li X; Guan W; Zhang HM; Fan L
    Traffic Inj Prev; 2018; 19(6):594-600. PubMed ID: 29757689
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Quantifying drivers' visual perception to analyze accident-prone locations on two-lane mountain highways.
    Yu B; Chen Y; Bao S; Xu D
    Accid Anal Prev; 2018 Oct; 119():122-130. PubMed ID: 30025353
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Utilizing UAV video data for in-depth analysis of drivers' crash risk at interchange merging areas.
    Gu X; Abdel-Aty M; Xiang Q; Cai Q; Yuan J
    Accid Anal Prev; 2019 Feb; 123():159-169. PubMed ID: 30513457
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Prediction of Driver's Intention of Lane Change by Augmenting Sensor Information Using Machine Learning Techniques.
    Kim IH; Bong JH; Park J; Park S
    Sensors (Basel); 2017 Jun; 17(6):. PubMed ID: 28604582
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A driving simulator study of driver performance on deceleration lanes.
    Calvi A; Benedetto A; De Blasiis MR
    Accid Anal Prev; 2012 Mar; 45():195-203. PubMed ID: 22269501
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.