Literature DB >> 29513611

Driver Vigilance in Automated Vehicles: Hazard Detection Failures Are a Matter of Time.

Eric T Greenlee1, Patricia R DeLucia1, David C Newton1.   

Abstract

OBJECTIVE: The primary aim of the current study was to determine whether monitoring the roadway for hazards during automated driving results in a vigilance decrement.
BACKGROUND: Although automated vehicles are relatively novel, the nature of human-automation interaction within them has the classic hallmarks of a vigilance task. Drivers must maintain attention for prolonged periods of time to detect and respond to rare and unpredictable events, for example, roadway hazards that automation may be ill equipped to detect. Given the similarity with traditional vigilance tasks, we predicted that drivers of a simulated automated vehicle would demonstrate a vigilance decrement in hazard detection performance.
METHOD: Participants "drove" a simulated automated vehicle for 40 minutes. During that time, their task was to monitor the roadway for roadway hazards.
RESULTS: As predicted, hazard detection rate declined precipitously, and reaction times slowed as the drive progressed. Further, subjective ratings of workload and task-related stress indicated that sustained monitoring is demanding and distressing and it is a challenge to maintain task engagement.
CONCLUSION: Monitoring the roadway for potential hazards during automated driving results in workload, stress, and performance decrements similar to those observed in traditional vigilance tasks. APPLICATION: To the degree that vigilance is required of automated vehicle drivers, performance errors and associated safety risks are likely to occur as a function of time on task. Vigilance should be a focal safety concern in the development of vehicle automation.

Entities:  

Keywords:  driver behavior; fatigue; human-automation interaction; vehicle automation; vigilance

Mesh:

Year:  2018        PMID: 29513611     DOI: 10.1177/0018720818761711

Source DB:  PubMed          Journal:  Hum Factors        ISSN: 0018-7208            Impact factor:   2.888


  5 in total

1.  Supervision of a self-driving vehicle unmasks latent sleepiness relative to manually controlled driving.

Authors:  Erin E Flynn-Evans; Lily R Wong; Yukiyo Kuriyagawa; Nikhil Gowda; Patrick F Cravalho; Sean Pradhan; Nathan H Feick; Nicholas G Bathurst; Zachary L Glaros; Theerawit Wilaiprasitporn; Kanika Bansal; Javier O Garcia; Cassie J Hilditch
Journal:  Sci Rep       Date:  2021-09-14       Impact factor: 4.996

2.  Redesigning Today's Driving Automation Toward Adaptive Backup Control With Context-Based and Invisible Interfaces.

Authors:  Christopher D D Cabrall; Jork C J Stapel; Riender Happee; Joost C F de Winter
Journal:  Hum Factors       Date:  2020-01-29       Impact factor: 2.888

Review 3.  Underload on the Road: Measuring Vigilance Decrements During Partially Automated Driving.

Authors:  Thomas McWilliams; Nathan Ward
Journal:  Front Psychol       Date:  2021-04-15

4.  Electrophysiological frequency domain analysis of driver passive fatigue under automated driving conditions.

Authors:  Yijing Zhang; Jinfei Ma; Chi Zhang; Ruosong Chang
Journal:  Sci Rep       Date:  2021-10-13       Impact factor: 4.379

5.  Neural Correlates Predicting Lane-Keeping and Hazard Detection: An fMRI Study Featuring a Pedestrian-Rich Simulator Environment.

Authors:  Kentaro Oba; Koji Hamada; Azumi Tanabe-Ishibashi; Fumihiko Murase; Masaaki Hirose; Ryuta Kawashima; Motoaki Sugiura
Journal:  Front Hum Neurosci       Date:  2022-02-09       Impact factor: 3.169

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.