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Paper Number
2025
Paper Type
Complete
Abstract
This study investigates the impact of automated driving systems on traffic safety. We design a natural experiment by combining Tesla’s rollout of “Navigate on Autopilot” in the U.S. with regional variations in the intensity of Tesla vehicles. Using granular data on traffic accidents and Tesla registrations in the State of Washington during the period of 2011–2022, we show that the intensity of Tesla vehicles is significantly associated with a reduction in traffic accidents following the rollout of its advanced autopilot feature, particularly in collisions between Tesla and non-Tesla vehicles. We identify the conditions where autonomous driving is most effective, such as when driver uncertainty is low (e.g., non-distracted) and the road environment is less complex (e.g., highways). Surprisingly, it has the unintended consequences of increased accidents in situations involving drowsy driving or under unconventional conditions, such as work zones. These results underscore the importance of situational factors, offering insights for the practical and policy implications of AI-driven systems.
Recommended Citation
Jung, Miyeon; Park, Jiyong; and Pang, Min-Seok, "Safety on Autopilot: An Empirical Investigation of Autonomous Driving and Traffic Safety" (2024). ICIS 2024 Proceedings. 27.
https://aisel.aisnet.org/icis2024/soc_impactIS/soc_impactIS/27
Safety on Autopilot: An Empirical Investigation of Autonomous Driving and Traffic Safety
This study investigates the impact of automated driving systems on traffic safety. We design a natural experiment by combining Tesla’s rollout of “Navigate on Autopilot” in the U.S. with regional variations in the intensity of Tesla vehicles. Using granular data on traffic accidents and Tesla registrations in the State of Washington during the period of 2011–2022, we show that the intensity of Tesla vehicles is significantly associated with a reduction in traffic accidents following the rollout of its advanced autopilot feature, particularly in collisions between Tesla and non-Tesla vehicles. We identify the conditions where autonomous driving is most effective, such as when driver uncertainty is low (e.g., non-distracted) and the road environment is less complex (e.g., highways). Surprisingly, it has the unintended consequences of increased accidents in situations involving drowsy driving or under unconventional conditions, such as work zones. These results underscore the importance of situational factors, offering insights for the practical and policy implications of AI-driven systems.
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05-SocImpact