Drive-KD: Multi-Teacher Distillation for VLMs in Autonomous Driving
Drive-KD: Multi-Teacher Distillation for VLMs in Autonomous Driving explores Drive-KD optimizes autonomous driving VLMs through multi-teacher distillation to enhance efficiency and performance.. Commercial viability score: 2/10 in Autonomous Driving AI.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Higher ViabilityDAIT: Distillation from Vision-Language Models to Lightweight Classifiers with Adaptive Intermediate Teacher TransferViability: 6.0
- Higher ViabilityKDFlow: A User-Friendly and Efficient Knowledge Distillation Framework for Large Language ModelsViability: 8.0
- Higher ViabilityEvoDriveVLA: Evolving Autonomous Driving Vision-Language-Action Model via Collaborative Perception-Planning DistillationViability: 8.0
- Higher ViabilityFrom Images to Words: Efficient Cross-Modal Knowledge Distillation to Language Models from Black-box TeachersViability: 7.0