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Driver trust in autonomous vehicles (AVs) significantly decreases after any failure, regardless of type. Understanding failure context is crucial for improving human-machine teaming and AV safety.

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Area of Science:

  • Human-Computer Interaction
  • Autonomous Systems Safety
  • Transportation Engineering

Background:

  • Autonomous vehicles (AVs) are evolving from transport to collaborative partners.
  • Driver-vehicle interaction is critical for safety in unpredictable environments.
  • Real-world AV testing reveals vulnerabilities in safety-critical situations.

Purpose of the Study:

  • To investigate how vehicle failures influence driver trust and risk perception.
  • To differentiate the impact of security vs. mechanical failures on driver behavior.
  • To analyze the role of scenario context in driver responses to AV failures.

Main Methods:

  • Identified six failure scenario categories from DMV disengagement reports.
  • Conducted an online experiment with baseline and failure drives.
  • Assessed driver trust and risk perception after experiencing specific failure types and scenarios.

Main Results:

  • Driver trust substantially decreased across all failure conditions.
  • Higher perceived risk correlated with reduced trust and risk-taking.
  • Control-related issues most significantly increased perceived risk.
  • No significant difference in trust or risk perception between security and mechanical failures.

Conclusions:

  • AV design must include mechanisms for assessing and responding to varying risk levels.
  • Addressing failure impacts is key to strengthening human-AV collaboration.
  • Improving human-machine teaming enhances overall AV safety and reliability.