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

Area of Science:

• Human-Computer Interaction
• Automotive Engineering
• Cognitive Psychology

Background:

• Consumer understanding of complex technologies like partial driving automation systems is heavily influenced by how information is presented.
• Accurate technical details do not inherently ensure correct user expectations or safe usage patterns.

Purpose of the Study:

• To investigate how branding and training methods for partial driving automation systems affect driver understanding and expectations.
• To determine if emphasizing system capabilities versus limitations impacts driver overconfidence and risk-taking behavior.

Main Methods:

• Ninety drivers were trained on a simulated partial driving automation system using fictitious branding (AutonoDrive vs. DriveAssist) and varied training modes (brochure, video, in-person).
• Participants' expectations of system performance, crash avoidance, and willingness to take risks were assessed via questionnaires before and after a driving session.

Main Results:

• Training that highlighted system capabilities led to inflated expectations of function and safety, and increased risk-taking, compared to training emphasizing limitations.
• While some expectation differences diminished post-driving, the initial impact of capability-focused training on perceived safety and risk tolerance was significant.

Conclusions:

• Overemphasis on capabilities without acknowledging limitations in training can create overconfidence in drivers using partial driving automation.
• Effective training for automated driving systems requires a balanced presentation of both capabilities and limitations to ensure accurate user understanding and safe operation.