Automation Expectation Mismatch: Incorrect Prediction Despite Eyes on Threat and Hands on Wheel
Artikel i vetenskaplig tidskrift, 2018

Objective: The aim of this study was to understand how to secure driver supervision engagement and conflict intervention performance while using highly reliable (but not perfect) automation.
Background: Securing driver engagement—by mitigating irony of automation (i.e., the better the automation, the less attention drivers will pay to traffic and the system, and the less capable they will be to resume control) and by communicating system limitations to avoid mental model misconceptions—is a major challenge in the human factors literature.
Method: One hundred six drivers participated in three test-track experiments in which we studied driver intervention response to conflicts after driving highly reliable but supervised automation. After 30 min, a conflict occurred wherein the lead vehicle cut out of lane to reveal a conflict object in the form of either a stationary car or a garbage bag.
Results: Supervision reminders effectively maintained drivers’ eyes on path and hands on wheel. However, neither these reminders nor explicit instructions on system limitations and supervision responsibilities prevented 28% (21/76) of drivers from crashing with their eyes on the conflict object (car or bag).
Conclusion: The results uncover the important role of expectation mismatches, showing that a key component of driver engagement is cognitive (understanding the need for action), rather than purely visual (looking at the threat), or having hands on wheel.
Application: Automation needs to be designed either so that it does not rely on the driver or so that the driver unmistakably understands that it is an assistance system that needs an active driver to lead and share control.

accident analysis

autonomous driving

attentional processes

shared mental models

human–automation interaction

mental models

Författare

Trent Victor

Chalmers, Mekanik och maritima vetenskaper, Fordonssäkerhet

Emma Tivesten

Chalmers, Mekanik och maritima vetenskaper, Fordonssäkerhet

Volvo Cars

Pär Gustavsson

Volvo Cars

Joel Johansson

Volvo Cars

Fredrik Sangberg

Volvo Cars

Mikael Ljung Aust

Volvo Cars

Human Factors

0018-7208 (ISSN) 1547-8181 (eISSN)

Vol. 60 8 1095-1116

Drivkrafter

Hållbar utveckling

Innovation och entreprenörskap

Styrkeområden

Transport

Ämneskategorier

Psykologi

Övrig annan teknik

Miljömedicin och yrkesmedicin

Annan naturvetenskap

DOI

10.1177/0018720818788164

Mer information

Senast uppdaterat

2022-04-19