Driving-With-LLMs

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Driving-with-LLMs is a PyTorch implementation for explainable autonomous driving, focusing on fusing object-level vector modality. It enables inference and training of the LLM-Driver.

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At a glance

Pricing

open-source

Free tier

Yes

API

—

Skill level

Technical

About

What is Driving-with-LLMs?

Driving-with-LLMs is a PyTorch-based tool that implements the research paper "Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving." Its core functionality revolves around integrating object-level vector data to enhance the explainability of autonomous driving systems. The tool supports both the inference and training processes of the LLM-Driver, providing a framework for researchers and developers working on advanced autonomous vehicle technologies. It is available as an open-source project on GitHub.

Best used for

Developing and researching explainable autonomous driving systems by fusing object-level vector modalities.

Common actions

Develop autonomous driving

Research AI explainability

Train LLM models

Implement deep learning

Process sensor data

workflowsdeepfakelow-code/no-codeopen-sourceautomated workflowcollaborationgithub copilotface swapping"AI Agents"

Capabilities

Key features

PyTorch implementation
Explainable autonomous driving
Object-level vector fusion
LLM-Driver inference
LLM-Driver training

Target Audience

ResearchersAI/ML DevelopersAutonomous Driving Engineers

Integrations

Not yet documented

Pricing & Plans

open-source

Free

FAQs

What are the hardware requirements for running Driving-with-LLMs, especially for training?

As a PyTorch-based tool for LLM-Driver training and inference, Driving-with-LLMs likely requires significant GPU resources, particularly for training. Specific VRAM and compute capabilities would depend on the model size and dataset used, but high-end NVIDIA GPUs are generally recommended.

What kind of datasets are compatible with Driving-with-LLMs for training the LLM-Driver?

Driving-with-LLMs is designed to work with datasets that include object-level vector modality, as described in the associated research paper. Users would need to prepare their autonomous driving datasets to incorporate this specific data format for effective training of the LLM-Driver.

Can Driving-with-LLMs be integrated with existing autonomous driving simulation environments?

While the tool provides the core LLM-Driver implementation, integration with specific simulation environments (e.g., CARLA, AirSim) would require custom development. Users would need to adapt the input/output interfaces to connect the LLM-Driver with their chosen simulator's data streams.

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Research & Education › Academic Research AI Agents & Automation › AI Frameworks & Infra

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