A Study on a 3D Convolution-Based Video Recognition System for Driving Aggressiveness Recognition

Sangin Lee, Jihun Park

http://doi.org/10.5626/JOK.2024.51.12.1094

This study aims to develop and test a model for classifying driving styles and recognizing driving aggressiveness using video data collected from a vehicle's front camera. To achieve this, the CARLA simulator was employed to simulate aggressive and cautious driving behaviors across various road environments, while a 3D convolution-based VideoResNet model was utilized for analyzing the video data. The results showed that the trained model achieved high accuracy in classifying driving styles during urban driving scenarios, demonstrating the effectiveness of front camera data in recognizing driving aggressiveness. Furthermore, experiments confirmed the model's capability to classify driving styles in an online manner, highlighting its potential as an on-the-spot tool for recognizing driving aggressiveness. Additionally, this study investigated the effect of road environments and speed variations on aggressiveness scores, demonstrating that the model can effectively consider the interplay between road complexity and speed when makingin its predictions.