Impact of Road Event Recognition Reliability in Autonomous Vehicles on Driver Trust and Takeover Performance

The reliability of an autonomous vehicle's (AV) event recognition is vital for building driver trust. Drivers' responses to AV event alerts directly influence driving safety. Therefore, understanding how AV recognition reliability affects driver trust and subsequent takeover behavior is crucial for designing safer and more effective autonomous driving systems.This study used a driving simulator to examine the effects of AV’s event recognition reliability on driver trust and takeover performance. Sixty volunteers without prior autonomous vehicle experience were divided into two groups: one performed a non-driving-related task (NDRT) in autonomous mode, while the other passively observed the roadside. The experiment utilized a mixed factorial design with three levels of recognition reliability (93%, 80%, 60%) and two error types (false alarm vs. miss), alongside a secondary task condition (NDRT required vs. not required). Six similar road scenarios were created to minimize environmental variability, and participants drove for about 18 minutes at 70 km/h. During the drive, 15 autonomous vehicle recognition events were randomly introduced, prompting the vehicle to issue or withhold takeover requests based on reliability. Participants could decide whether to assume manual control in response to these alerts.The experimental data collection comprised two parts: (1) objective data, including takeover performance (takeover time, control duration), driving behavior (accidents, steering wheel angle variation, lateral acceleration variation, lane position variation), and NDRT performance (total score, completion time, number of questions answered, and errors); and (2) subjective data, assessing participants' initial trust in the autonomous vehicle, their trust and acceptance levels after each event, and overall acceptance following the driving session.The study revealed that miss errors significantly impacted driver confidence, which declined sharply with increased error frequency and decreased system reliability. Miss errors also prolonged takeover time, hindering drivers' ability to respond effectively to sudden road incidents. Conversely, false alarms increased reaction time by about 0.14 seconds, while miss errors reduced takeover time by approximately 0.41 seconds, risking premature responses. Additionally, miss errors increased cognitive load due to unexpected incidents, leading to greater steering variability and impulsive lane changes, which caused more significant deviations from the intended path. After encountering multiple errors—especially at 80% and 60% reliability—drivers remained skeptical of system alerts. Although trust improved slightly with accurate warnings, it did not return to the initial levels seen with high reliability. These findings underscore the challenge of restoring driver trust in autonomous vehicles and highlight the importance of human factors design in maintaining user confidence.

Authors

Publication Details

Journal
AHFE international
Published
2025-01-01
DOI
https://doi.org/10.54941/ahfe1006732
Primary Topic
Human-Automation Interaction and Safety
Type
article
Field-Weighted Citation Impact
0.00
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article

Impact of Road Event Recognition Reliability in Autonomous Vehicles on Driver Trust and Takeover Performance

Yung‐Ching Liu, Shokatsu CHEN
AHFE international
Human-Automation Interaction and Safety
article

Impact of Road Event Recognition Reliability in Autonomous Vehicles on Driver Trust and Takeover Performance

Yung‐Ching Liu, Shokatsu CHEN
article en

Abstract

The reliability of an autonomous vehicle's (AV) event recognition is vital for building driver trust. Drivers' responses to AV event alerts directly influence driving safety. Therefore, understanding how AV recognition reliability affects driver trust and subsequent takeover behavior is crucial for designing safer and more effective autonomous driving systems.This study used a driving simulator to examine the effects of AV’s event recognition reliability on driver trust and takeover performance. Sixty volunteers without prior autonomous vehicle experience were divided into two groups: one performed a non-driving-related task (NDRT) in autonomous mode, while the other passively observed the roadside. The experiment utilized a mixed factorial design with three levels of recognition reliability (93%, 80%, 60%) and two error types (false alarm vs. miss), alongside a secondary task condition (NDRT required vs. not required). Six similar road scenarios were created to minimize environmental variability, and participants drove for about 18 minutes at 70 km/h. During the drive, 15 autonomous vehicle recognition events were randomly introduced, prompting the vehicle to issue or withhold takeover requests based on reliability. Participants could decide whether to assume manual control in response to these alerts.The experimental data collection comprised two parts: (1) objective data, including takeover performance (takeover time, control duration), driving behavior (accidents, steering wheel angle variation, lateral acceleration variation, lane position variation), and NDRT performance (total score, completion time, number of questions answered, and errors); and (2) subjective data, assessing participants' initial trust in the autonomous vehicle, their trust and acceptance levels after each event, and overall acceptance following the driving session.The study revealed that miss errors significantly impacted driver confidence, which declined sharply with increased error frequency and decreased system reliability. Miss errors also prolonged takeover time, hindering drivers' ability to respond effectively to sudden road incidents. Conversely, false alarms increased reaction time by about 0.14 seconds, while miss errors reduced takeover time by approximately 0.41 seconds, risking premature responses. Additionally, miss errors increased cognitive load due to unexpected incidents, leading to greater steering variability and impulsive lane changes, which caused more significant deviations from the intended path. After encountering multiple errors—especially at 80% and 60% reliability—drivers remained skeptical of system alerts. Although trust improved slightly with accurate warnings, it did not return to the initial levels seen with high reliability. These findings underscore the challenge of restoring driver trust in autonomous vehicles and highlight the importance of human factors design in maintaining user confidence.

AHFE internationalVol. -5
Openalex Percentile: Top 83%
Human-Automation Interaction and Safety
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