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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 3·5 min read
RAPiD: Reward-Guided Consistency Distillation of Diffusion Planners for Real-Time Autonomous Driving
TL;DR
RAPiD is a new framework that distills a pretrained diffusion-based trajectory planner into a faster, real-time planner while maintaining safety and multi-modal trajectory generation. It achieves a 5.5x reduction in inference latency, making it suitable for autonomous driving applications.
✦ Why It Matters
Consider implementing RAPiD to enhance the efficiency of your autonomous driving systems today.
Key Takeaways
- Explore RAPiD for faster trajectory planning in autonomous vehicles.
- Utilize reward-guided consistency distillation for improved model efficiency.
- Implement safety-aware training methods in your AI systems.
- Test RAPiD on your datasets to evaluate performance improvements.
How It Works
RAPiD distills a pretrained diffusion planner into a student model that generates trajectories in fewer steps. It uses deterministic denoising steps from the teacher model and incorporates a low-noise data anchor to ensure the generated trajectories are realistic and grounded in expert behavior.
The framework also trains an Implicit Q-Learning critic to evaluate and select the best trajectories based on a modified reward system, enhancing safety and performance.