Driver Assistant: Persuading Drivers to Adjust Secondary Tasks Using Large Language Models
This addresses safety risks for drivers in automated vehicles, but it is incremental as it applies an existing LLM method to a new domain-specific scenario.
The study tackled the problem of drivers in Level 3 automated vehicles becoming distracted by secondary tasks, which can hinder emergency interventions, by using a Large Language Model to provide persuasive advice that maintains driver attention with reduced cognitive load.
Level 3 automated driving systems allows drivers to engage in secondary tasks while diminishing their perception of risk. In the event of an emergency necessitating driver intervention, the system will alert the driver with a limited window for reaction and imposing a substantial cognitive burden. To address this challenge, this study employs a Large Language Model (LLM) to assist drivers in maintaining an appropriate attention on road conditions through a "humanized" persuasive advice. Our tool leverages the road conditions encountered by Level 3 systems as triggers, proactively steering driver behavior via both visual and auditory routes. Empirical study indicates that our tool is effective in sustaining driver attention with reduced cognitive load and coordinating secondary tasks with takeover behavior. Our work provides insights into the potential of using LLMs to support drivers during multi-task automated driving.