FSDrive

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FSDrive is a Research & Education tool that implements "FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Driving." It enables visual reasoning for autonomous driving systems using spatio-temporal CoT.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is FSDrive?

FSDrive is the official implementation for the research paper "FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Driving," which was recognized as a NeurIPS 2025 spotlight. This tool introduces a novel spatio-temporal Chain-of-Thought (CoT) approach, allowing end-to-end autonomous driving Visual Language Agents (VLA) to visually process and plan trajectories. It uniquely unifies visual generation and understanding with minimal data, marking a significant advancement in applying visual reasoning to autonomous driving. FSDrive provides comprehensive instructions for installation, data preparation, training, inference, evaluation, and visualization, making it a valuable resource for researchers and developers in the autonomous driving domain.

Best used for

Ideal for developers and professors who need to implement and research advanced visual reasoning for autonomous driving, evaluate trajectory planning models, and develop AI models for autonomous systems. Especially valuable for those working with spatio-temporal Chain-of-Thought and Visual Language Agents.

Common actions

implement autonomous driving

research visual reasoning

evaluate trajectory planning

develop AI models

collaborationworkflowsopen-sourcelow-code/no-code"AI Agents"github copilotface swappingautomated workflowdeepfake

Capabilities

Key features

Spatio-temporal CoT
Visual trajectory planning
Visual generation
Visual understanding
Minimal data requirement
Autonomous driving research

Target Audience

developerprofessor

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What is the core innovation of FSDrive?

FSDrive introduces a spatio-temporal Chain-of-Thought (CoT) approach that enables Visual Language Agents (VLA) to think visually for trajectory planning in autonomous driving. This method unifies visual generation and understanding with minimal data, advancing visual reasoning in the field.

What are the primary steps to get started with FSDrive?

To get started, you need to clone the repository, set up the conda environment, install dependencies, and prepare the nuScenes dataset. After that, you can proceed with extracting visual tokens, constructing data, and then training and inferring models.

Can FSDrive be used for visualizing autonomous driving trajectories?

Yes, FSDrive includes tools for visualizing both the predicted trajectories and the Chain-of-Thought (CoT) in the pixel space. This allows researchers to gain insights into how the model processes visual information and plans its movements.

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Study Assistants Knowledge Management Course Creation Scientific Computing Summarization Language Learning

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