Assessing Cognitive Demand during Natural Language Interactions with a Digital Driving Assistant

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.