Finding windows of opportunity: how drivers adapt to partial automation safeguards over time

September 2024

Introduction: Most partially automated systems have safeguards to counteract driver disengagement, but little is known about how they affect driver behavior over time. This naturalistic observation study investigated how the behavior of 14 drivers who had no partial automation experience evolved over a month of exposure to the Tesla Autopilot system in a model year 2020 Model 3. Method: Behavior was analyzed leading up to, during, and immediately after attention reminders and emergency-slowdown-leading-to-lockout events. Results: We found that drivers learn to internalize safeguard sequences and discover windows of opportunity to do non-driving-related activities. People learned to respond quicker to alerts, leading to fewer escalated sequences in the latter half of the study. However, drivers also spent more time engaging in non-driving-related activities and glancing off-road, which corresponded with more initial alerts of the attention reminder sequence as time went on. Prolonged disengagement culminated in 16 lockouts across the sample, although in general, drivers responded faster and had fewer lockouts over time. Conclusion: Our findings demonstrate the human ability to learn system constraints and thus illustrate that it is possible to shape safer driving behavior with robust safeguards. User-centric design considerations for driver support strategies are presented in this paper.