Total runs:
356
Run Growth:
309
Growth Rate:
86.80%
Sponsored by
APIMart
DriveFusion / DriveFusion-V0.2
Model's Last Updated: February 11 2026
robotics
Introduction of DriveFusion-V0.2
Model Details of DriveFusion-V0.2
DriveFusion-V0.2 Model Card
DriveFusion-V0.2
A Multimodal Autonomous Driving Model: Vision + Language + GPS + Speed.
🚀 Model Overview
DriveFusion-V0.2 is a multimodal model designed for autonomous vehicle applications. Unlike standard Vision-Language models, V0.2 integrates telemetry data (GPS and Speed) directly into the transformer architecture to perform dual tasks:
- Natural Language Reasoning : Describing scenes and explaining driving decisions.
- Trajectory & Speed Prediction : Outputting coordinates for future waypoints and target velocity profiles.
Built on the Qwen2.5-VL foundation, DriveFusion-V0.2 adds specialized MLP heads to fuse physical context with visual features, enabling a comprehensive "world model" for driving.
🔗 GitHub Repository
Find the full implementation, training scripts, and preprocessing logic here:
- Main Model Code: DriveFusion/drivefusion
- Data Collection: DriveFusion/data-collection
Core Features
- Vision Processing : Handles images and videos via a 32-layer Vision Transformer.
-
Context Fusion
: Custom
SpeedMLPandGPSTargetPointsMLPintegrate vehicle telemetry. - Predictive Heads : Generates 20 trajectory waypoints and 10 target speed values .
- Reasoning : Full natural language generation for "Chain of Thought" driving explanations.
🏗 Architecture
DriveFusion-V0.2 extends the Qwen2.5-VL architecture with a modular "Driving Intelligence" layer.
The DriveFusion-V0.2 Architecture: Integrating visual tokens with telemetry-encoded tokens for dual-head output.
Technical Specifications
- Text Encoder : Qwen2.5-VL (36 Transformer layers).
- Vision Encoder : 32-layer ViT with configurable patch sizes.
-
Driving MLPs
:
-
TrajectoryMLP: Generates batch × 20 × 2 coordinates. -
TargetSpeedMLP: Generates batch × 10 × 2 velocity values.
-
- Context Window : 128k tokens.
🚀 Quick Start
Using DriveFusion-V0.2 requires the custom
DriveFusionProcessor
to handle the fusion of text, images, and telemetry.
Installation
pip install transformers accelerate qwen-vl-utils torch
Inference Example
import torch
from drivefusion import DriveFusionForConditionalGeneration, DriveFusionProcessor
# Load Model
model_id = "DriveFusion/DriveFusion-V0.2"
model = DriveFusionForConditionalGeneration.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
processor = DriveFusionProcessor.from_pretrained(model_id)
# Define Input: Image + Prompt + Telemetry
gps_context = [[40.7128, -74.0060], [40.7130, -74.0058]] # Lat/Lon history
speed_context = [[30.5]] # Current speed in m/s
message = [{
"role": "user",
"content": [
{"type": "image", "image": "highway_scene.jpg"},
{"type": "text", "text": "Analyze the scene and predict the next trajectory based on our current speed."}
]
}]
# Generate
inputs = processor(text=message, images="highway_scene.jpg", gps=gps_context, speed=speed_context, return_tensors="pt").to("cuda")
output = model.generate(**inputs, max_new_tokens=512)
# Results
print("Reasoning:", output["text"])
print("Predicted Trajectory (20 pts):", output["trajectory"])
print("Target Speeds:", output["target_speeds"])
🛠 Intended Use
- End-to-End Autonomous Driving : Acting as a primary planner or a redundant safety checker.
- Explainable AI (XAI) : Providing human-readable justifications for automated maneuvers.
- Sim-to-Real Transfer : Using the model as a sophisticated "expert" driver in simulated environments.
Runs of DriveFusion DriveFusion-V0.2 on huggingface.co
365
Total runs
1
24-hour runs
2
3-day runs
159
7-day runs
355
30-day runs
More Information About DriveFusion-V0.2 huggingface.co Model
Url of DriveFusion-V0.2
DriveFusion-V0.2 huggingface.co Url
Provider of DriveFusion-V0.2 huggingface.co
Other API from DriveFusion
Updated:February 11 2026