How Will Humans Cut Through Automated Vehicle Platoons in Mixed Traffic Environments? A Simulation Study of Drivers’ Gaze Behaviors Based on the Dynamic Areas of Interest

Abstract

With higher levels of automation (LOA) in vehicles, mixed traffic environments are expected to emerge and last, until the transition into fully autonomous traffic environments. So far, there is limited research surrounding this transitional state of mixed traffic environments regarding how humans in semi-automatic vehicles interact with autonomous vehicles on the road. Knowledge behind the interactive behavior of humans in this situation can be useful to resolve the uncertainty in mixed traffic environments. This study investigated the manual driver's gaze behaviors during their targeted action of changing lanes and cutting through a platoon of fully automated vehicles, with the goal of safely exiting a highway. This scenario of manually cutting through a platoon, which was run by cooperative adaptive cruise control (CACC), was developed using a model-based simulation software (PreScan, TASS International) and tested in a driving simulator with a controlled experimental scheme. The resultant gaze behaviors captured during the experiments using the eye-tracking glass (ETG2, SMI), were analyzed by mapping the gaze vectors with the specified area of interests (AOIs) in the visual field of view. This paper focuses on applying a deep-learning algorithm for automated detection and tracking of the two, dynamic AOIs, (i) the leading vehicle from the platoon in the middle lane pertaining to the perceived distance and amount of time the driver has to make a lane change, and (ii) the road center outlined by the detection of lane and road boundaries, which serves as a frame of reference for visual attention in a primary driving task. By training over a thousand images for deep learning, the accuracy of the two AOIs' detection was obtained 99.85% and 71.95%, respectively. The mapping of gaze vectors with the AOIs showed that, with shorter time headway (THW) of a platoon, drivers spent a longer time fixating on the leading vehicle from the platoon, and that the average fixation time on the road center became longer, while the percentage of road center remain unchanged. These findings imply there are significant changes in cognitive workload with various platoon time headway during a mixed traffic environment.