Diffusion-Planner and Hyper-Diffusion-Planner
These are ecosystem siblings where Hyper-Diffusion-Planner builds upon and extends the foundational diffusion-based planning approach of Diffusion-Planner, progressing from flexible guidance mechanisms to end-to-end autonomous driving capabilities.
About Diffusion-Planner
ZhengYinan-AIR/Diffusion-Planner
[ICLR 2025 Oral] The official implementation of "Diffusion-Based Planning for Autonomous Driving with Flexible Guidance"
This project helps autonomous vehicle engineers create more advanced and reliable self-driving systems. It takes in sensor data and environmental information to generate optimal, safe driving trajectories for a self-driving car. The output is a planned path for the vehicle, which can then be used to control its movement in complex scenarios like navigating busy intersections or avoiding pedestrians.
About Hyper-Diffusion-Planner
ZhengYinan-AIR/Hyper-Diffusion-Planner
The official implementation of "Unleashing the Potential of Diffusion Models for End-to-End Autonomous Driving"
This project helps autonomous vehicle researchers and developers design better end-to-end autonomous driving systems. It takes sensor data or simulation inputs and outputs a planned driving trajectory, demonstrating how diffusion models can create effective and scalable planning solutions for complex real-world scenarios. This is for professionals working on advanced AI for self-driving cars.
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