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A model for naturalistic glance behavior around Tesla Autopilot disengagements
Alberto Morando1, Pnina Gershon1, Bruce Mehler1
1MIT Agelab, Massachusetts Institute of Technology, 1 Amherst Street, Cambridge, MA 02142, USA.
Accident; Analysis and Prevention
|September 7, 2021
Summary
Drivers using Autopilot (AP) shift visual attention away from the road, with longer off-road glances before disengagement. This visual behavior model aids in assessing automated driving safety.
Area of Science:
- Human-computer interaction
- Automotive safety
- Cognitive science
Background:
- Driver inattention is a concern with partially automated driving systems.
- Understanding visual behavior changes during automation is crucial for safety assessment.
Purpose of the Study:
- To model driver visual behavior during non-critical disengagements of Tesla's Autopilot (AP).
- To simulate glance patterns in naturalistic highway driving scenarios.
Main Methods:
- Utilized glance data from 290 human-initiated AP disengagement epochs.
- Employed Bayesian Generalized Linear Mixed models to analyze glance duration and transitions.
Main Results:
- The model accurately replicates observed driver glance patterns.
- Off-road glances were longer with AP active; non-driving related glances were most frequent and longest (22% > 2s).
- Driving-related off-road glances decreased with AP active compared to manual driving.
Conclusions:
- Visual behavior significantly changes before and after AP disengagement.
- Drivers focus more on non-driving related areas before disengaging AP.
- The model serves as a reference for safety assessments and driver management system design.
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