Your browser doesn't support javascript.
loading
The Impact of Cybersecurity Attacks on Human Trust in Autonomous Vehicle Operations.
Lim, Cherin; Prendez, David; Boyle, Linda Ng; Rajivan, Prashanth.
Afiliación
  • Lim C; University of Washington, USA.
  • Prendez D; University of Washington, USA.
  • Boyle LN; New York University, USA.
  • Rajivan P; University of Washington, USA.
Hum Factors ; : 187208241283321, 2024 Sep 18.
Article en En | MEDLINE | ID: mdl-39293023
ABSTRACT

OBJECTIVE:

This study examines the extent to which cybersecurity attacks on autonomous vehicles (AVs) affect human trust dynamics and driver behavior.

BACKGROUND:

Human trust is critical for the adoption and continued use of AVs. A pressing concern in this context is the persistent threat of cyberattacks, which pose a formidable threat to the secure operations of AVs and consequently, human trust.

METHOD:

A driving simulator experiment was conducted with 40 participants who were randomly assigned to one of two groups (1) Experience and Feedback and (2) Experience-Only. All participants experienced three drives Baseline, Attack, and Post-Attack Drive. The Attack Drive prevented participants from properly operating the vehicle in multiple incidences. Only the "Experience and Feedback" group received a security update in the Post-Attack drive, which was related to the mitigation of the vehicle's vulnerability. Trust and foot positions were recorded for each drive.

RESULTS:

Findings suggest that attacks on AVs significantly degrade human trust, and remains degraded even after an error-less drive. Providing an update about the mitigation of the vulnerability did not significantly affect trust repair.

CONCLUSION:

Trust toward AVs should be analyzed as an emergent and dynamic construct that requires autonomous systems capable of calibrating trust after malicious attacks through appropriate experience and interaction design. APPLICATION The results of this study can be applied when building driver and situation-adaptive AI systems within AVs.
Palabras clave

Texto completo: 1 Bases de datos: MEDLINE Idioma: En Revista: Hum Factors Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Bases de datos: MEDLINE Idioma: En Revista: Hum Factors Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos