Driving style recognition method using braking characteristics based on hidden Markov model

Deng, Chao; Wu, Chaozhong; Lyu, Nengchao; Huang, Zhen

doi:10.1371/journal.pone.0182419

Cited by 46 publications

(28 citation statements)

References 27 publications

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“…The Calinski-Harabasz score was utilized to determine the optimal number of clusters, which was 3 for our dataset (Figure 3). In addition, previous studies have suggested that driving style can be classified into Aggressive type, Moderate type, and Conservative type (Chu et al, 2017; Deng et al, 2017; Li et al, 2017; Palat et al, 2019), accordingly in this paper K is 3. Three random samples were selected as the initial clustering centroids and the samples were clustered into three driving style groups via the K-means algorithm (Figure 4).…”

Section: Resultsmentioning

confidence: 88%

“…However, the driving style does have some relationship with the driving behaviors. Previous studies have suggested that the driving style can be classified into three types: Aggressive type, Moderate type, and Conservative type (Chu et al, 2017; Deng et al, 2017; Li et al, 2017; Palat et al, 2019). Different driving styles can result in different kinds of behaviors and actions of the drivers and vehicles.…”

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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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: Resultsmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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“…The research shows that HMMs are accurate and reliable for driving events recognition. Deng et al proposed driving style classification using braking characteristics based on HMM [ 26 ].…”

Section: Survey Of Related Workmentioning

confidence: 99%

IoT On-Board System for Driving Style Assessment

Jachimczyk

Dziak²,

Czapla

et al. 2018

Sensors

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The assessment of skills is essential and desirable in areas such as medicine, security, and other professions where mental, physical, and manual skills are crucial. However, often such assessments are performed by people called “experts” who may be subjective and are able to consider a limited number of factors and indicators. This article addresses the problem of the objective assessment of driving style independent of circumstances. The proposed objective assessment of driving style is based on eight indicators, which are associated with the vehicle’s speed, acceleration, jerk, engine rotational speed and driving time. These indicators are used to estimate three driving style criteria: safety, economy, and comfort. The presented solution is based on the embedded system designed according to the Internet of Things concept. The useful data are acquired from the car diagnostic port—OBD-II—and from an additional accelerometer sensor and GPS module. The proposed driving skills assessment method has been implemented and experimentally validated on a group of drivers. The obtained results prove the system’s ability to quantitatively distinguish different driving styles. The system was verified on long-route tests for analysis and could then improve the driver’s behavior behind the wheel. Moreover, the spider diagram approach that was used established a convenient visualization platform for multidimensional comparison of the result and comprehensive assessment in an intelligible manner.

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“…This scheme also analyzes driving behavior patterns and provides feedback to the driver for the vehicle status. Deng et al [ 12 ] proposed a scheme to classify driving styles into one of three styles (aggressive, moderate, or mild) by analyzing driver braking characteristics using a hidden Markov model.…”

Section: Related Workmentioning

confidence: 99%

“…Advanced driver assistance systems for driver safety and convenience are growing rapidly and are widely used in the vehicular domain [ 1 ]. In particular, various smart phone-based driving assistance systems, including the detection of front vehicles and obstacles, the headway distance, and time to a collision estimation [ 2 , 3 , 4 ]; the recognition of various driver behaviors and road conditions [ 5 , 6 , 7 , 8 ]; the identification of driving styles [ 9 , 10 , 11 , 12 ], the detection of emergent braking [ 13 ], and an accident alert for preventing a secondary accident [ 14 , 15 ] have been actively proposed. The smart phone-based driving assistant systems are still attractive, since they can be directly used for ordinary vehicles without additional sensor equipment for monitoring driving situations.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Traffic Risk Detection Model Using Smart Mobile Device

Park

Han

Kim

et al. 2018

Sensors

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Automatically recognizing dangerous situations for a vehicle and quickly sharing this information with nearby vehicles is the most essential technology for road safety. In this paper, we propose a real-time deceleration pattern-based traffic risk detection system using smart mobile devices. Our system detects a dangerous situation through machine learning on the deceleration patterns of a driver by considering the vehicle’s headway distance. In order to estimate the vehicle’s headway distance, we introduce a practical vehicle detection method that exploits the shadows on the road and the taillights of the vehicle. For deceleration pattern analysis, the proposed system leverages three machine learning models: neural network, random forest, and clustering. Based on these learning models, we propose two types of decision models to make the final decisions on dangerous situations, and suggest three types of improvements to continuously enhance the traffic risk detection model. Finally, we analyze the accuracy of the proposed model based on actual driving data collected by driving on Seoul city roadways and the Gyeongbu expressway. We also propose an optimal solution for traffic risk detection by analyzing the performance between the proposed decision models and the improvement techniques.

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Driving style recognition method using braking characteristics based on hidden Markov model

Cited by 46 publications

References 27 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

IoT On-Board System for Driving Style Assessment

Real-Time Traffic Risk Detection Model Using Smart Mobile Device

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