Exploring Micro Accidents and Driver Responses in Automated Driving: Insights from Real-world Videos

TL;DR

This paper investigates micro accidents in automated driving using real-world videos, analyzing their causes, driver responses, and implications for system safety to improve autonomous vehicle design.

Contribution

It introduces a comprehensive dataset of micro accidents, applies machine learning for analysis, and explores human perceptions to enhance safety in automated driving systems.

Findings

Micro accidents are common and can lead to severe incidents.

Driver responses vary based on environment and accident type.

Insights can inform safer autonomous vehicle design.

Abstract

Automated driving in level 3 autonomy has been adopted by multiple companies such as Tesla and BMW, alleviating the burden on drivers while unveiling new complexities. This article focused on the under-explored territory of micro accidents during automated driving, characterized as not fatal but abnormal aberrations such as abrupt deceleration and snake driving. These micro accidents are basic yet pervasive events that might results in more severe accidents. Through collecting a comprehensive dataset of user generated video recording such micro accidents in natural driving scenarios, this article locates key variables pertaining to environments and autonomous agents using machine learning methods. Subsequently, crowdsourcing method provides insights into human risk perceptions and reactions to these micro accidents. This article thus describes features of safety critical scenarios other than crashes and fatal accidents, informing and potentially advancing the design of automated driving systems.