what is uniad fr?

UniAD is a research framework for self-driving cars that won the Best Paper Award at CVPR 2023, one of the top computer vision conferences. The central idea is that the many separate tasks a self-driving system needs to do, including detecting other vehicles, predicting where they will go, understanding the layout of the road, anticipating what parts of space nearby cars might occupy, and deciding where your car should drive next, work better when trained together in a coordinated way rather than as isolated modules. Most autonomous driving systems are built with separate components that pass information to each other in sequence. UniAD treats all of these tasks as part of one unified model where the output of perception tasks flows directly into prediction tasks, and prediction flows into planning. The reasoning is that if the system learns with planning as the final goal from the start, every intermediate step is optimized in service of that goal rather than for its own score in isolation. The model takes camera images from around the vehicle as input. It processes them into a bird's-eye view representation of the surrounding scene, which the task-specific parts of the network then interpret for tracking, mapping, motion prediction, occupancy forecasting, and path planning. The model is trained in two stages: first the perception parts are trained alone to reach stable starting weights, then all parts are trained together end-to-end. Pre-trained model checkpoints are provided for download so researchers can reproduce the reported numbers or use the weights as a starting point for their own experiments. The repository includes configuration files for training and evaluation, and documentation for installation and dataset preparation. The dataset used is nuScenes, a public benchmark for autonomous driving research. The code is released under the Apache 2.0 license.