Analysis of the Correlation between Driver's Visual Features and Driver Intention

Driver behaviors provide abundant information and feedback for future Advanced Driver Assistance Systems (ADAS). Driver’s eye and head may present some typical movement patterns before executing driving maneuvers. It is possible to use driver’s head and eye movement information for predicting driver intention. Therefore, to determine the most important features related to driver intention has attracted widespread research interests. In this paper, a method to analyze the correlation between driver’s visual features and driver intention is proposed, aiming to determine the most representative features for driver intention prediction. Firstly, naturalistic driving experiment is conducted to collect driver’s videos during executing lane keeping and lane change maneuvers. Then, driver’s head and face visual features are extracted from those videos. By using boxplot and independent samples T-test, features which have significant correlation with driver intention are found. Finally, the random forest algorithm is used to sequence the correlation importance between driver’s visual features and driver intention. The results show that driver’s eye horizontal movement and head yaw movement play the most important role in driver intention estimation among all related visual features. This research can be used to support features selection during driver intention estimation and leads to improvement of traffic safety.