Work

Toward Integrated Takeover Performance Measurement: Validation of Fréchet Distance as a Takeover Performance Metric

Year: 2024

Type: article

Abstract: This study introduces and validates a new metric for assessing takeover performance in conditionally automated driving, using Fréchet Distance. Fréchet Distance is a measurement that measures the similarity between two separate curves. Thirty-two participants took part in a simulated driving experiment. Employing a 2 × 2 within-subjects design, the study compared traditional takeover performance metrics, including takeover time, time to collision, and resulting acceleration, with Fréchet Distance. Analysis results revealed similar trends between traditional metrics and Fréchet Distance. These findings suggest that Fréchet Distance can effectively measure takeover performance by integrating spatial and temporal aspects.

Source: Proceedings of the Human Factors and Ergonomics Society Annual Meeting

DOI: 10.1177/10711813241266831

Authors: Doo Won Han, Jundi Liu, X. Jessie Yang, Alicia Romo, William Horrey, Dawn Tilbury, Feng Zhou, Lionel Robert, Lisa Molnar

Institutions: University of Michigan, AAA Foundation for Traffic Safety, University of Michigan–Dearborn

Language: English

Topic: Human-Automation Interaction and Safety

Subfield: Social Psychology

Field: Psychology

Domain: Social Sciences

Open Access status: bronze

Funders: AAA Foundation for Traffic Safety, American Academy of Audiology Foundation

OpenAlex ID: W4402070425

FWCI: 0.474

Cites: 23

Cited by: 1

Related to: 10