Enhance Human Driver Situation Awareness of Adversarial Attacks in Automated Driving Systems: A Driving Simulator Study

As automated driving systems (ADS) become increasingly integrated into real-world transportation, understanding how human drivers perceive and respond to system failures under adversarial conditions is critical for safety. Prior research has shown that human drivers often overestimate ADS’ capabilities and lack awareness of the systems’ vulnerabilities to adversarial attacks. In this study, we investigate the effectiveness of explanations on enhancing human drivers’ situation awareness (SA) of different adversarial attacks and their takeover performance during SAE Level 3 automated driving using a driving simulator. We varied ADS reliability within-subjects and attack type and explanation between-subjects. Our preliminary results show that the benefit of explanations appeared to vary by attack type, suggesting that certain adversarial scenarios may elicit greater SA improvement when accompanied by system-generated explanations.