A Gaze Data-based Comparative Study to Build a Trustworthy Human-AI Collaboration in Crash Anticipation

TL;DR

This study evaluates human drivers' crash anticipation ability using gaze data and compares it with AI models, highlighting the potential for collaborative human-AI systems to improve vehicle safety.

Contribution

It introduces a gaze-based evaluation method for human crash anticipation and demonstrates how human and AI predictions can be integrated for safer driving.

Findings

Drivers can anticipate crashes up to 2.61 seconds early.

AI models anticipate crashes 1.02 seconds earlier than humans.

Attention patterns influence crash anticipation, informing AI attention mechanisms.

Abstract

Vehicles with a safety function for anticipating crashes in advance can enhance drivers' ability to avoid crashes. As dashboard cameras have become a low-cost sensor device accessible to almost every vehicle, deep neural networks for crash anticipation from a dashboard camera are receiving growing interest. However, drivers' trust in the Artificial Intelligence (AI)-enabled safety function is built on the validation of its safety enhancement toward zero deaths. This paper is motivated to establish a method that uses gaze data and corresponding measures to evaluate human drivers' ability to anticipate crashes. A laboratory experiment is designed and performed, wherein a screen-based eye tracker collects the gaze data of six volunteers while watching 100 driving videos that include both normal and crash scenarios. Statistical analyses of the experimental data show that, on average, drivers can anticipate a crash up to 2.61 seconds before it occurs in this pilot study. The chance that drivers have successfully anticipated crashes before they occur is 92.8%. A state-of-the-art AI model can anticipate crashes 1.02 seconds earlier than drivers on average. The study finds that crash-involving traffic agents in the driving videos can vary drivers' instant attention level, average attention level, and spatial attention distribution. This finding supports the development of a spatial-temporal attention mechanism for AI models to strengthen their ability to anticipate crashes. Results from the comparison also suggest the development of collaborative intelligence that keeps human-in-the-loop of AI models to further enhance the reliability of AI-enabled safety functions.