Pith. sign in

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

arxiv 2309.10228 v1 pith:6IE3ANJF submitted 2023-09-19 cs.HC cs.AI

classification cs.HCcs.AI
keywords autonomousvehicleslanguagecapabilitiesframeworkllmsadvanceddecision-making
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DriveQA: Passing the Driving Knowledge Test

    cs.CV 2025-08 conditional novelty 6.0 of 10

    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.

Pith tools