Driver Assistant: Persuading Drivers to Adjust Secondary Tasks Using Large Language Models
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
How Do Drivers Allocate Their Potential Attention? Driving Fixation Prediction via Convolutional Neural Networks
The traffic driving environment is a complex and dynamic changing scene in which drivers have to pay close attention to salient and important targets or regions for safe driving. Modeling drivers’ eye movements and atten…
object-detectionObject DetectionMining Personalized Climate Preferences for Assistant Driving
Both assistant driving and self-driving have attracted a great amount of attention in the last few years. However, the majority of research efforts focus on safe driving; few research has been conducted on in-vehicle cli…
Optimal Passenger-Seeking Policies on E-hailing Platforms Using Markov Decision Process and Imitation Learning
Vacant taxi drivers' passenger seeking process in a road network generates additional vehicle miles traveled, adding congestion and pollution into the road network and the environment. This paper aims to employ a Markov …
Imitation LearningReinforcement LearningBlaming humans in autonomous vehicle accidents: Shared responsibility across levels of automation
When a semi-autonomous car crashes and harms someone, how are blame and causal responsibility distributed across the human and machine drivers? In this article, we consider cases in which a pedestrian was hit and killed …
Improving Driver Satisfaction with a Driving Function Learning from Implicit Human Feedback -- a Test Group Study
During the use of advanced driver assistance systems, drivers frequently intervene into the active driving function and adjust the system's behavior to their personal wishes. These active driver-initiated takeovers conta…