REVIEW 1 cited by
Drive as You Speak: Enabling Human-Like Interaction with Large Language Models in Autonomous Vehicles
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
The future of autonomous vehicles lies in the convergence of human-centric design and advanced AI capabilities. Autonomous vehicles of the future will not only transport passengers but also interact and adapt to their desires, making the journey comfortable, efficient, and pleasant. In this paper, we present a novel framework that leverages Large Language Models (LLMs) to enhance autonomous vehicles' decision-making processes. By integrating LLMs' natural language capabilities and contextual understanding, specialized tools usage, synergizing reasoning, and acting with various modules on autonomous vehicles, this framework aims to seamlessly integrate the advanced language and reasoning capabilities of LLMs into autonomous vehicles. The proposed framework holds the potential to revolutionize the way autonomous vehicles operate, offering personalized assistance, continuous learning, and transparent decision-making, ultimately contributing to safer and more efficient autonomous driving technologies.
Forward citations
Cited by 1 Pith paper
-
DriveQA: Passing the Driving Knowledge Test
DriveQA is a new multimodal driving-knowledge benchmark showing that LLMs and MLLMs struggle with right-of-way, numerical traffic rules, and sign variations, with modest transfer gains to nuScenes and BDD.
Discussion (0). Continue with ORCID to comment.