DrivCapsNet: A Driving Style Detection Algorithm Based on Capsule Network

Driver behavior plays a fundamental role in the driver-vehicle-environment system, where the driving style can significantly impact vehicle emissions, fuel consumption, insurance expenses, road safety, and advanced driver assistance systems (ADAS). Nonetheless, detecting driver behavior is a complex and challenging task, traditional methods require a lot of data pre-processing and there is still no research on discriminative driving behavior with capsule networks which can capture the spatial relationships of data. However, it has not been fully studied and applied for driver behavior detection. To tackle these challenges, we propose an methodology for detecting driving style using a capsule network, named DrivCapsNet, which is capable of detecting various driving styles using either inertial measurement unit (IMU) data or camera data. A crucial advantage of this method is that its dynamic routing mechanism can extract the relationships between the parts and the entity, thereby improving detection accuracy. We performed comprehensive experiments on two realistic driving datasets to substantiate the efficacy of our proposed DrivCapsNet approach. The outcomes validate that our approach performs well and achieves accurate driving style detection, highlighting its potential to contribute significantly to the field of driver behavior analysis.