Incorporating Gaze Behavior Using Joint Embedding With Scene Context for Driver Takeover Detection.

Despite the recent advancement in driver assistance systems, most existing solutions and partial automation systems such as SAE Level 2 driving automation systems assume that the driver is in the loop; the human driver must continuously monitor the driving environment. Frequent transition of maneuver control is expected between the driver and the car while using such automation in difficult traffic conditions. In this work, we aim to predict driver takeover timing in order for the system to prepare transition from automation to driver control. While previous studies indicated that eye gaze is an important cue to predict driver takeover, we hypothesize that traffic condition as well as the reliability of the driving automation also have a strong impact. Therefore, we propose an algorithm that jointly consider the driver's gaze information and contextual driving environment, which is complemented with the vehicle operational and driver physiological signals. Specifically, we consider joint embedding of traffic scene information and gaze behavior using 3D-Convolutional Neural Network (3D-CNN). We demonstrate that our algorithm is successfully able to predict driver takeover intent, using user study data from 28 participants collected in simulated driving environments.