Curve safe speed model considering driving style based on driver behaviour questionnaire

Deng, Zejian; Chu, Delin; Wu, Chaozhong; He, Yi; Cui, Jian

doi:10.1016/j.trf.2018.02.007

Cited by 56 publications

(40 citation statements)

References 40 publications

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“…The complexity of the scenario would affect the degree of driving difficulty. Specifically the performance of turning was related to different driving styles (Ly et al, 2013; Choi et al, 2017; Deng et al, 2018) and brain dynamics (Garcia et al, 2017). Hence in this study, a curved mountainous road was chosen as the scenario.…”

Section: Discussionmentioning

confidence: 99%

“…The driving scenario was designed based on Unity 3D (Unity Technologies, USA). Previous studies demonstrated the coupling between turning and driving styles (Ly et al, 2013; Choi et al, 2017; Deng et al, 2018), brain dynamics (Garcia et al, 2017). Hence a seven-kilometer circular road containing two consecutive S-shaped curves, two curved roads with a radius of 20 m and seven other curves in a montanic scenery was applied (Figure 1A).…”

Section: Methodsmentioning

confidence: 97%

“…A hybrid model based on DBQ and DSI was proposed to classify drivers into sub-groups based on their driving styles and driving skills (Martinussen et al, 2014). Deng et al (2018) applied DBQ-based driving styles to curve safety speed model to determine the theoretical curve safe speed, and the results indicated the new model could not only prevent the risks of rollover and sideslip during turning, but also could adapt to the driver's driving style. Although promising results were obtained, the questionnaire investigation was prone to the subjective factors of the researchers and the participants.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Driving Style Recognition Based on Electroencephalography Data From a Simulated Driving Experiment

et al. 2019

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Driving style is a very important indicator and a crucial measurement of a driver's performance and ability to drive in a safe and protective manner. A dangerous driving style would possibly result in dangerous behaviors. If the driving styles can be recognized by some appropriate classification methods, much attention could be paid to the drivers with dangerous driving styles. The driving style recognition module can be integrated into the advanced driving assistance system (ADAS), which integrates different modules to improve driving automation, safety and comfort, and then the driving safety could be enhanced by pre-warning the drivers or adjusting the vehicle's controlling parameters when the dangerous driving style is detected. In most previous studies, driver's questionnaire data and vehicle's objective driving data were utilized to recognize driving styles. And promising results were obtained. However, these methods were indirect or subjective in driving style evaluation. In this paper a method based on objective driving data and electroencephalography (EEG) data was presented to classify driving styles. A simulated driving system was constructed and the EEG data and the objective driving data were collected synchronously during the simulated driving. The driving style of each participant was classified by clustering the driving data via K-means. Then the EEG data was denoised and the amplitude and the Power Spectral Density (PSD) of four frequency bands were extracted as the EEG features by Fast Fourier transform and Welch. Finally, the EEG features, combined with the classification results of the driving data were used to train a Support Vector Machine (SVM) model and a leave-one-subject-out cross validation was utilized to evaluate the performance. The SVM classification accuracy was about 80.0%. Conservative drivers showed higher PSDs in the parietal and occipital areas in the alpha and beta bands, aggressive drivers showed higher PSD in the temporal area in the delta and theta bands. These results imply that different driving styles were related with different driving strategies and mental states and suggest the feasibility of driving style recognition from EEG patterns.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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Driving Style Recognition Based on Electroencephalography Data From a Simulated Driving Experiment

et al. 2019

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“…Regarding to the terrain, previous studies observed the strong association between adverse geometric terrain and location with high accident rate [ 34 ]. In the survey conducted by Chinese government, the fatality rate in curved road was as high as 0.8, while the average fatality rate was 0.3 among all crashes in 2014 [ 35 ]. The finding that higher accident rate was associated with smaller radius of curvature corroborated the effect of curved pathway on ESRAs [ 36 ].…”

Section: Discussionmentioning

confidence: 99%

Risk factors for extremely serious road accidents: Results from national Road Accident Statistical Annual Report of China

et al. 2018

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BackgroundIn the past decades, extremely serious road accidents with a death toll over ten in each have become a severe public health problem in China. This study investigates risk factors contributing to extremely serious road accidents, which will be crucial for accident prevention.MethodsCollecting data from The Road Accident Statistical Annual Report openly issued by China’s Traffic Management Bureau of the Public Security Ministry for the time period 2004–2015, we used the monthly case number of extreme serious road accidents as the dependent variable. We then selected ten risk factors as primary independent variables: professional driver, driving under influence (alcohol or drug), fatigue, vehicle type, overload, brake problem, weather, road classification, terrain, and region. The method of negative binominal regression was implemented to investigate the association between these risk factors and extremely serious road accidents.ResultsA total of 346 extremely serious road accidents were included in our analysis. On a national scale, we found that professional driver [incidence rate ratio (IRR): 1.10, 95% CI: 1.02–1.19], fatigue (IRR: 1.15, 95% CI: 1.03–1.29), large vehicle type (IRR: 1.11, 95% CI: 1.03–1.21), overload (IRR: 1.09, 95% CI: 1.03–1.16), and terrain (IRR: 1.09, 95% CI: 1.01–1.18) were significantly associated with extremely serious road accidents. Besides, separate analyses on western and non-western region indicated that both regions had shared risk factors as well as distinct factors.ConclusionsOur study identifies professional driver, fatigue, large vehicle type, overload, and terrain as significant risk factors of extremely serious road accidents in China, and targeted and preventative measures could be taken based on our findings.

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“…In order to evaluate whether a driving behavior is fuel-efficient, Hao et al [26] mined the traffic operating data and fuel consumption data and finally built a corresponding analysis model. Deng et al [27] established a curve safe speed model on the basis of a thorough analysis of driving behavior data. e use of empirical data to investigate driving behaviors with VSL control was well-studied [10,12,17].…”

Section: Introductionmentioning

confidence: 99%

Traffic Flow Modeling of Freeway Variable Speed Limit Control Based on the Big Data of Driving Behavior

et al. 2020

Journal of Advanced Transportation

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Variable speed limit (VSL) control is a flexible restriction on the rate at which motorists can drive on a given stretch of road. Effective VSL control can increase safety and provide clear guidance for motorists. Previous traffic flow models of VSL control were mostly based on the influence of VSL on average speed (macro) or driver’s expected speed (micro). Few models considered the influence of VSL on driver’s actual driving behavior. In this paper, we first briefly introduce the big traffic data involved in this study and explain the mapping relationship between the data and driving behavior. Then, we analyze the driver’s actual driving behavior under the VSL control. Then, an improved single-lane cellular automaton model is established based on the driving behavior characteristics under VSL control. After that, we calibrate the parameters of the single-lane cellular automaton model with the left lane as the calibration object. Finally, this paper uses the proposed single-lane cellular automaton model to simulate the traffic flow characteristics under VSL control. The numerical simulation results show that the simulation of the variable speed limit in different density intervals presents different results, but these results are consistent with the actual situation of variable speed limit control, which verifies the validity of the proposed model.

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Curve safe speed model considering driving style based on driver behaviour questionnaire

Cited by 56 publications

References 40 publications

Driving Style Recognition Based on Electroencephalography Data From a Simulated Driving Experiment

Driving Style Recognition Based on Electroencephalography Data From a Simulated Driving Experiment

Risk factors for extremely serious road accidents: Results from national Road Accident Statistical Annual Report of China

Traffic Flow Modeling of Freeway Variable Speed Limit Control Based on the Big Data of Driving Behavior

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