Driver Assistant: Persuading Drivers to Adjust Secondary Tasks Using Large Language Models

TL;DR

This paper presents a novel use of Large Language Models to persuade drivers to stay attentive during Level 3 automated driving, reducing cognitive load and improving safety during secondary tasks.

Contribution

It introduces a humanized persuasive system leveraging LLMs to maintain driver attention and coordinate secondary tasks in automated driving scenarios.

Findings

Effective in sustaining driver attention

Reduces cognitive load during secondary tasks

Improves coordination during takeovers

Abstract

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.