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Towards Adaptive Autonomous Vehicle Systems: Considering Trust and Risk Perception during Failures
Cherin Lim1, Prashanth Rajivan1
1University of Washington, USA.
Accident; Analysis and Prevention
|October 24, 2025
Summary
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.
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.
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