A Machine Learning-Based Approach to Analyze Information Used for Steering Control

Abstract

Understanding the relationship between driving behavior and visual information is important for holistic understanding of driving behavior. However, the analysis of the cognitive behavior for steering/throttle control has been only conducted under a special simulator environment. Therefore, in this study, we aimed to develop a convolutional neural network (CNN) with human physical characteristics to analyze the driver's cognitive behavior and to validate that the machine learning methods can be an analytical method for understanding driver behavior. We obtained the driving data in a simulator experiment to train the proposed CNN model. The region where the visual field influences drivers' steering behavior was analyzed using the results of the feature maps generated by the trained CNN model and the driver's gaze behavior. The results indicate that the driver performs steering control using the information within 20 degrees from the gaze point. This shows that the results obtained from our proposed method can reproduce the same results as previous findings. We also validated that the results are not uniquely obtained depending on the proposed model and environment but are also influenced by the driving behavior such as the gaze point and the steering control. We analyzed the dataset generated by the mathematical control model, called the driver model, which performs different behaviors from the driver. The analysis results generated by the driver model were different from the results of the human data. Therefore, the results generated by the machine learning-based analysis are influenced by the driving behavior. Consequently, these results imply that machine learning methods have the potential to become analytical methods for understanding driver behavior.