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

Abstract

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.

Effects of Driving Style and Vehicle Maintenance on Vehicle Roadworthiness

“…The limitations of the conducted research refer to the collection of data on the driving style and vehicle maintenance, which were collected using a questionnaire completed by respondents whose vehicles underwent PTI at a PTI station. Although such data were considered sufficiently relevant to support the hypotheses proposed in research papers [21,27], the authors in paper [8] warn of their subjective and indirect nature. Therefore, future research should be focused on providing a more relevant method for collecting data regarding the driving style recognition.…”

“…Objectively speaking, there are numerous elements in a person's driving behaviour that are used to determine their driving style. The authors in [8] differentiate between the terms driving style and driving behaviour. Driving style is influenced by the person's personality, as well as their physical and mental condition, and is manifested in the driving behaviour.…”

“…Accordingly, they highlighted that the weight of subjective driving style evaluation is equal to twice the objective evaluation. The authors of [8], however, used an Electroencephalography (EEG) device to collect data for their driving style recognition model. Collecting data through a questionnaire may be a somewhat subjective and indirect method for evaluating the driving style.…”

Intra‐individual association between C‐reactive protein and insulin administration in postoperative lumbar spinal canal stenosis patients: A retrospective cohort study

“…By calculating the Pearson’s correlation coefficient between the predicted and actual amounts of ΔInsulin using fivefold cross‐validation, we determined to add each variable or random effect. During cross‐validation, the model was evaluated using the data of patients not included in model development, similar to leave‐subject‐out cross‐validation. We also validated the model on the independent test dataset and finally calculated the effect size using the entire dataset.…”

Data Analysis Techniques in Vehicle Communication Networks: Systematic Mapping of Literature

“…A sensor fusion was also implemented with highway lanes images and steering wheel data regression, thereby achieving a 10% increase precision in the results comparing to the state-of-the-art. Another technique used for this application was the support vector machine, proposed in the papers of Fuwu et al [12] and Burton et al [18]. Fuwu developed an algorithm and compared the obtained CAN network data with the drivers' brain waves, aggrouping it into three main profiles: aggressive, moderate, and conservative.…”