A model for naturalistic glance behavior around Tesla Autopilot disengagements. (October 2021) - Morando, Alberto; Gershon, Pnina; Mehler, Bruce; Reimer, Bryan
Abstract : Objective: We present a model for visual behavior that can simulate the glance pattern observed around driver-initiated, non-critical disengagements of Tesla's Autopilot (AP) in naturalistic highway driving. Background: Drivers may become inattentive when using partially-automated driving systems. The safety effects associated with inattention are unknown until we have a quantitative reference on how visual behavior changes with automation. Methods: The model is based on glance data from 290 human initiated AP disengagement epochs. Glance duration and transition were modelled with Bayesian Generalized Linear Mixed models. Results: The model replicates the observed glance pattern across drivers. The model's components show that off-road glances were longer with AP active than without and that their frequency characteristics changed. Driving-related off-road glances were less frequent with AP active than in manual driving, while non-driving related glances to the down/center-stack areas were the most frequent and the longest (22% of the glances exceeded 2 s). Little difference was found in on-road glance duration. Conclusion: Visual behavior patterns change before and after AP disengagement. Before disengagement, drivers looked less on road and focused more on non-driving related areas compared to after the transition to manual driving. The higher proportion of off-road glances before disengagement to manual driving were not compensated by longer glances ahead. Application: The model can be used as a reference for safety assessment or to formulate design targets for driver management systems.