Скачать с ютуб [04] Motion Forecasting (Waabi CVPR 24 Tutorial on Self-Driving Cars) в хорошем качестве

[04] Motion Forecasting (Waabi CVPR 24 Tutorial on Self-Driving Cars)

Learn about how and why the future state of the world is forecasted in autonomous driving. We will look into the challenges of this task, different input and output representations, as well as different architectures to tackle this problem. Speaker: Sergio Casas --- A full day tutorial from Waabi covering all aspects of autonomous driving. Delivered at the top international computer vision conference, CVPR, this tutorial will provide the necessary background for understanding the different tasks and associated challenges, the different sensors and data sources one can use and how to exploit them, as well as how to formulate the relevant algorithmic problems such that efficient learning and inference is possible. We begin by introducing the self-driving problem setting and a broad range of existing solutions, both top-down from a high-level perspective, as well as bottom-up from technological and algorithmic points of view. We will then extrapolate from the state of the art and discuss where the challenges and open problems are, and where we need to head towards to provide a scalable, safe and affordable self-driving solution for the future. Since last year’s instance (https://waabi.ai/cvpr-2023/), countless new and promising avenues of research have started gaining traction, and we have updated our tutorial accordingly. To name a few example, this includes topics like occupancy forecasting, self-supervised learning, foundation models, the rise of Gaussian Splatting and diffusion models for simulation as well as the study of closed-loop vs. open-loop evaluation. See the tutorial website for more information: https://waabi.ai/cvpr-2024/