Driver Expectations of a Partial Driving Automation System in Relation to Branding and Training

• Published in: Human factors Vol. 66; no. 5; pp. 1531 - 1544
• Main Authors: Singer, Jeremiah, Tefft, Brian C, Benson, Aaron, Jenness, James W, Horrey, William J
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01.05.2024; Human Factors and Ergonomics Society
• Subjects: Accidents, Traffic - prevention & control; Automation; Automobile Driving; Collision avoidance; Humans; Motivation; Reaction Time; Safety critical; Surface Transportation; Technical information; Training; mental models; surface transportation; vehicle automation; training evaluation; driver behavior
• ISSN: 0018-7208; 1547-8181; 1547-8181
• DOI: 10.1177/00187208221143024

Objective The current study examined whether differences in the branding and description or mode of training materials influence drivers’ understanding and expectations of a partial driving automation system. Background How technology is described might influence consumers’ understanding and expectations, even if all information is accurate. Method Ninety drivers received training about a real partial driving automation system with a fictitious name. Participants were randomly assigned to a branding condition (system named AutonoDrive, training emphasized capabilities; or system named DriveAssist, training emphasized limitations) and training mode (quick-start brochure; video; or in-person demonstration). No safety-critical information was withheld nor deliberately misleading information provided. After training, participants drove a vehicle equipped with the system. Associations of drivers’ expectations with branding condition and training mode were assessed using between-subjects comparisons of questionnaire responses obtained pre- and post-drive. Results Immediately after training, those who received information emphasizing the system’s capabilities had greater expectations of the system’s function and crash avoidance capability in a variety of driving scenarios, including many in which the system would not work, as well as greater willingness to utilize the system’s workload reduction benefits to take more risks. Most but not all differences persisted after driving the vehicle. Expectations about collision avoidance differed by training mode pre-drive but not post-drive. Conclusion Training that emphasizes a partial driving automation system’s capabilities and downplays its limitations can foster overconfidence. Application Accuracy of technical information does not guarantee understanding; training should provide a balanced view of a system’s limitations as well as capabilities.