GenAD: Three core components that revolutionize autonomous driving technology

The success of the GenAD project relies on three key components: instance-centric scene representation, trajectory prior modeling, and generation of future motion. First, the project uses a bird's-eye view processing method to extract agent-centric scene markers and fuse map features. Second, a variational autoencoder is used to map the ground truth trajectory to the latent space, based on which trajectory prior modeling is performed. Finally, the gated recurrent unit is used to gradually predict the next future in the latent space, achieving accurate prediction and planning of future motion.