Characterizing Driving Styles with Deep Learning

Abstract

Characterizing driving styles of human drivers using vehicle sensor data, e.g., GPS, is an interesting research problem and an important real-world requirement from automotive industries. A good representation of driving features can be highly valuable for autonomous driving, auto insurance, and many other application scenarios. However, traditional methods mainly rely on handcrafted features, which limit machine learning algorithms to achieve a better performance. In this paper, we propose a novel deep learning solution to this problem, which could be the first attempt of extending deep learning to driving behavior analysis based on GPS data. The proposed approach can effectively extract high level and interpretable features describing complex driving patterns. It also requires significantly less human experience and work. The power of the learned driving style representations are validated through the driver identification problem using a large real dataset.

References in corpus (3)

• On the difficulty of training Recurrent Neural Networks
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Cited by in corpus (7)

• Deep Learning Techniques for Geospatial Data Analysis
• Sequential Interpretability: Methods, Applications, and Future Direction for Understanding Deep Learning Models in the Context of Sequential Data
• Vehicle Telematics Via Exteroceptive Sensors: A Survey
• Driving Style Representation in Convolutional Recurrent Neural Network Model of Driver Identification
• A Choquet Fuzzy Integral Vertical Bagging Classifier for Mobile Telematics Data Analysis
• Deep Learning Approach for Aggressive Driving Behaviour Detection
• Dynamic and Systematic Survey of Deep Learning Approaches for Driving Behavior Analysis