Driving Style Recognition Model Based on NEV High-Frequency Big Data and Joint Distribution Feature Parameters

Abstract

With the promotion and financial subsidies of the new energy vehicle (NEV), the NEV industry of China has developed rapidly in recent years. However, compared with traditional fuel vehicles, the technological maturity of the NEV is still insufficient, and there are still many problems that need to be solved in the R&D and operation stages. Among them, energy consumption and driving range are particularly concerning, and are closely related to the driving style of the driver. Therefore, the accurate identification of the driving style can provide support for the research of energy consumption. Based on the NEV high-frequency big data collected by the vehicle-mounted terminal, we extract the feature parameter set that can reflect the precise spatiotemporal changes in driving behavior, use the principal component analysis method (PCA) to optimize the feature parameter set, realize the automatic driving style classification using a K-means algorithm, and build a driving style recognition model through a neural network algorithm. The result of this paper shows that the model can automatically classify driving styles based on the actual driving data of NEV users, and that the recognition accuracy can reach 96.8%. The research on driving style recognition in this paper has a certain reference value for the development and upgrade of NEV products and the improvement of safety.

From Human to Autonomous Driving: A Method to Identify and Draw Up the Driving Behaviour of Connected Autonomous Vehicles

“…pedal use, as described in [40][41][42], but how to translate this data into an autonomous driver model is still an open issue.…”

“…However, it is still challenging to understand each model parameter's influence on the resultant driving style to make it as suitable as possible for others. The classification and recognition of driving style are usually obtained by using statistical and machine learning algorithms based on vehicular signals such as speed, acceleration, trajectory, and gas pedal use, as described in [40][41][42], but how to translate this data into an autonomous driver model is still an open issue.…”