Dangerous driving behavior detection using smartphone sensors

2016 ◽  
Author(s):  
Fu Li ◽  
Hai Zhang ◽  
Huan Che ◽  
Xiaochen Qiu
Keyword(s):  
Driving Behavior ◽  
Behavior Detection ◽  
Dangerous Driving ◽  
Smartphone Sensors

Dangerous driving behavior detection using video-extracted vehicle trajectory histograms

2017 ◽  
Vol 21 (5) ◽  
pp. 409-421 ◽  
Author(s):  
Zhijun Chen ◽  
Chaozhong Wu ◽  
Zhen Huang ◽  
Nengchao Lyu ◽  
Zhaozheng Hu ◽  
...  
Keyword(s):  
Driving Behavior ◽  
Behavior Detection ◽  
Dangerous Driving ◽  
Vehicle Trajectory

SafeDriving: An Effective Abnormal Driving Behavior Detection System based on EMG Signals

2021 ◽  
pp. 1-1
Author(s):  
Yuanzhao Fan ◽  
Fei Gu ◽  
Jin Wang ◽  
Jianping Wang ◽  
Kejie Lu ◽  
...  
Keyword(s):  
Detection System ◽  
Driving Behavior ◽  
Behavior Detection

scholarly journals Effects of Robots Teaching Driving Behavior Using Dangerous Driving Videos

2021 ◽  
Vol 33 (4) ◽  
pp. 819-826
Author(s):  
Felix JIMENEZ ◽  
Masayoshi KANOH ◽  
Mitsuhiro HAYASE ◽  
Tomohiro YOSHIKAWA ◽  
Takahiro TANAKA ◽  
...  
Keyword(s):  
Driving Behavior ◽  
Dangerous Driving

scholarly journals INDIVIDUAL VALUE SYSTEM CHARACTERISTICS AND DRIVING BEHAVIOR RELATIONSHIPS

2017 ◽  
Vol 1 ◽  
pp. 570
Author(s):  
Krists Jānis Lazdiņš ◽  
Kristīne Mārtinsone
Keyword(s):  
Driving Behavior ◽  
Distracted Driving ◽  
Value System ◽  
System Integrity ◽  
Dangerous Driving ◽  
The Future ◽  
And Gender ◽  
The Individual ◽  
Individual Value ◽  
System Characteristics

The aim of research was to examine characteristics of individual value system prediction for driving behavior. It raised fundamental question for the research: 1. which of the individual value system characteristics predict driving behavior controlling gender and age. In the study participated 108 respondents, 40 (37.0%) men and 68 (63.0%) women who filled the questionnaire on the internet. There was used two questionnaires – „Latvian driving behavior survey”, The value and levels of availability relations in different spheres of life” The results showed that the value system integrity / disintegrity indicator predicts distracted driving, explains 18% of variation and is statistically significantly. Internal vacuum and age statistically significantly negatively predicts risky driving explaining 17% of variation. Age statistically significantly predicts safe and courteous driving, explains 12% of variation. Value system integrity / disintegrity indicator and gender, statistically significantly negatively predicts summary indicator of dangerous driving, explains 22% of variation. Age statistically significantly negatively predicts distracted driving, explains 30% of variation. Limitations of the research are related to the size of the sample, alignment of participants and use of new instruments, as well as data collection method. If the study would be repeated in the future, it would be desirable to increase the sample size and use approbated instrument. It would be interesting to find out how the value of individual factors predicts objective size of accidents and violations caused by driving. The results can serve as the basis to create new driving behavior interventions and also applicable to psychologist's professional work, when counseling individuals of this group, as well as can be used in the future development of the field, science and research.

scholarly journals Estimating the Necessary Amount of Driving Data for Assessing Driving Behavior

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2600 ◽  
Author(s):  
Anna-Maria Stavrakaki ◽  
Dimitrios I. Tselentis ◽  
Emmanouil Barmpounakis ◽  
Eleni I. Vlahogianni ◽  
George Yannis
Keyword(s):  
Discrete Time ◽  
Driving Behavior ◽  
Methodological Framework ◽  
Detailed Data ◽  
Specific Time ◽  
Speed Limits ◽  
Specific Time Point ◽  
Smartphone Sensors ◽  
Over Time ◽  
Time Point

The aim of this paper was to provide a methodological framework for estimating the amount of driving data that should be collected for each driver in order to acquire a clear picture regarding their driving behavior. We examined whether there is a specific discrete time point for each driver, in the form of total driving duration and/or the number of trips, beyond which the characteristics of driving behavior are stabilized over time. Various mathematical and statistical methods were employed to process the data collected and determine the time point at which behavior converges. Detailed data collected from smartphone sensors are used to test the proposed methodology. The driving metrics used in the analysis are the number of harsh acceleration and braking events, the duration of mobile usage while driving and the percentage of time driving over the speed limits. Convergence was tested in terms of both the magnitude and volatility of each metric for different trips and analysis is performed for several trip durations. Results indicated that there is no specific time point or number of trips after which driving behavior stabilizes for all drivers and/or all metrics examined. The driving behavior stabilization is mostly affected by the duration of the trips examined and the aggressiveness of the driver.

scholarly journals Inattentive Driving Behavior Detection Based on Portable FMCW Radar

2019 ◽  
Vol 67 (10) ◽  
pp. 4031-4041 ◽  
Author(s):  
Chuanwei Ding ◽  
Rachel Chae ◽  
Jing Wang ◽  
Li Zhang ◽  
Hong Hong ◽  
...  
Keyword(s):  
Driving Behavior ◽  
Fmcw Radar ◽  
Behavior Detection
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