Curve speed model for driver assistance based on driving style classification

Chu, Delin; Deng, Zejian; He, Yi; Wu, Chaozhong; Sun, Chuan; Lu, Zhenji

doi:10.1049/iet-its.2016.0294

Cited by 47 publications

(37 citation statements)

References 22 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: 87%

“…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: 87%

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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“…Ma [29] summarized the driving style characteristic parameters that affect fuel consumption from the mechanism, mainly including speed, throttle opening, throttle opening change rate, etc. Some researchers took the statistical values (maximum, average, and standard deviation) of vehicle speed, throttle opening, and acceleration as characteristic parameters to classify driving styles [30,31].…”

Section: Selection Of Driving Style Characteristic Parameters For Minmentioning

confidence: 99%

Machine Learning-Based Driving Style Identification of Truck Drivers in Open-Pit Mines

et al. 2019

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The significance in constructing a driving style identification model for open-pit mine truck drivers is to reduce diesel consumption and improve training. First, we developed a driving behavior and mining truck condition monitoring system for an open-pit mine. Under heavy-load and no-load conditions of a mining truck, based on the same experimental truck and haulage road, the data of driving behavior and truck status of different drivers were collected. The driving style characteristic parameters of mining trucks under heavy-load and no-load conditions were constructed through Pearson correlation analysis. Using a k-means clustering algorithm, driving style can be divided into three types: normal type, soft type, and aggressive type, and we verified the validity of this driving style classification with a box plot. On this basis, the parameters of random forest, k-nearest neighbor, support vector machine, and neural network models were optimized and the accuracy was compared through a cross-validation grid search, and then a driving style identification model based on the random forest method was finally proposed. Driving style parameter weight values were obtained based on the Gini coefficient. Last, the fuel consumption characteristics of different driving styles were calculated. The results show that the driving style identification models based on random forest can effectively identify different driving styles when the mining truck is operating under heavy load and no load, and the overall accuracy of the model is 95.39% and 90.74% respectively. The fuel consumption of the aggressive driving style was the largest and was 10% higher than the average fuel consumption. The research results provide data support and new ideas for operation training and fuel-saving driving of mining trucks in open-pit mines.

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“…In addition, the learning‐based methodologies such as Bayesian non‐parametric techniques have also been directly applied to analyse driving style [23, 24]. For example, fuzzy logic (FL) methods [25] were selected to classify driving style for improving the cure speed model accuracy. Though the above mentioned works have made a great progress in driver behaviour modelling and driving style recognition, they did not consider the driver behaviour uncertainty which is usually caused by psychological/physical factors and driving environments.…”

Section: Introductionmentioning

confidence: 99%

Statistical‐based approach for driving style recognition using Bayesian probability with kernel density estimation

Han

Wang

et al. 2018

IET Intelligent Transport Systems

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Curve speed model for driver assistance based on driving style classification

Cited by 47 publications

References 22 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

Machine Learning-Based Driving Style Identification of Truck Drivers in Open-Pit Mines

Statistical‐based approach for driving style recognition using Bayesian probability with kernel density estimation

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