Literature DB >> 28166888

How much time do drivers need to obtain situation awareness? A laboratory-based study of automated driving.

Zhenji Lu1, Xander Coster2, Joost de Winter2.   

Abstract

Drivers of automated cars may occasionally need to take back manual control after a period of inattentiveness. At present, it is unknown how long it takes to build up situation awareness of a traffic situation. In this study, 34 participants were presented with animated video clips of traffic situations on a three-lane road, from an egocentric viewpoint on a monitor equipped with eye tracker. Each participant viewed 24 videos of different durations (1, 3, 7, 9, 12, or 20 s). After each video, participants reproduced the end of the video by placing cars in a top-down view, and indicated the relative speeds of the placed cars with respect to the ego-vehicle. Results showed that the longer the video length, the lower the absolute error of the number of placed cars, the lower the total distance error between the placed cars and actual cars, and the lower the geometric difference between the placed cars and the actual cars. These effects appeared to be saturated at video lengths of 7-12 s. The total speed error between placed and actual cars also reduced with video length, but showed no saturation up to 20 s. Glance frequencies to the mirrors decreased with observation time, which is consistent with the notion that participants first estimated the spatial pattern of cars after which they directed their attention to individual cars. In conclusion, observers are able to reproduce the layout of a situation quickly, but the assessment of relative speeds takes 20 s or more.
Copyright © 2016 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Eye tracking; Hazard perception; Scene perception; Spatial memory

Mesh:

Year:  2016        PMID: 28166888     DOI: 10.1016/j.apergo.2016.12.003

Source DB:  PubMed          Journal:  Appl Ergon        ISSN: 0003-6870            Impact factor:   3.661


  4 in total

1.  Transitions Between Highly Automated and Longitudinally Assisted Driving: The Role of the Initiator in the Fight for Authority.

Authors:  Davide Maggi; Richard Romano; Oliver Carsten
Journal:  Hum Factors       Date:  2020-08-31       Impact factor: 2.888

2.  Identifying the Causes of Drivers' Hazardous States Using Driver Characteristics, Vehicle Kinematics, and Physiological Measurements.

Authors:  Ali Darzi; Sherif M Gaweesh; Mohamed M Ahmed; Domen Novak
Journal:  Front Neurosci       Date:  2018-08-14       Impact factor: 4.677

Review 3.  Updating our understanding of situation awareness in relation to remote operators of autonomous vehicles.

Authors:  Clare Mutzenich; Szonya Durant; Shaun Helman; Polly Dalton
Journal:  Cogn Res Princ Implic       Date:  2021-02-19

4.  Toward a Theory of Visual Information Acquisition in Driving.

Authors:  Benjamin Wolfe; Ben D Sawyer; Ruth Rosenholtz
Journal:  Hum Factors       Date:  2020-07-17       Impact factor: 3.598

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.