Assessing Cognitive Demand during Natural Language Interactions with a Digital Driving Assistant
Year: 2016
Type: conference-paper
Abstract: Given the proliferation of digital assistants in everyday mobile technology, it appears inevitable that next generation vehicles will be embodied by similar agents, offering engaging, natural language interactions. However, speech can be cognitively captivating. It is therefore important to understand the demand that such interfaces may place on drivers. Twenty-five participants undertook four drives (counterbalanced), in a medium-fidelity driving simulator: 1. Interacting with a state-of-the-art digital driving assistant ('DDA') (presented using Wizard-of-Oz); 2. Engaged in a hands-free mobile phone conversation; 3. Undertaking the delayed-digit recall ('2-back') task and 4. With no secondary task (baseline). Physiological arousal, subjective workload assessment, tactile detection task (TDT) and driving performance measures consistently revealed the '2-back' drive as the most cognitively demanding (highest workload, poorest TDT performance). Mobile phone and DDA conditions were largely equivalent, attracting low/medium cognitive workload. Findings are discussed in the context of designing in-vehicle natural language interfaces to mitigate cognitive demand.
Institutions: University of Nottingham, Jaguar Land Rover (United Kingdom)
Language: English
Subfield: Human Factors and Ergonomics
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
Funder: Jaguar Land Rover
OpenAlex ID: W2569229936