A model for naturalistic glance behavior around Tesla Autopilot disengagements

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A model for naturalistic glance behavior around Tesla Autopilot disengagements

Morando, Alberto / Gershon, Pnina / Mehler, Bruce / Reimer, Bryan

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.