GenAD: Innovative research leading to a new era of autonomous driving

The latest paper "GenAD: Generative End-to-End Autonomous Driving" jointly released by Berkeley, Huituo and the Institute of Automation of the Chinese Academy of Sciences proposes a new end-to-end autonomous driving paradigm. The key to this research is to predict the evolution of the ego vehicle and the surrounding environment in past scenarios, and based on this, use variational autoencoders to learn the future trajectory distribution in the structural latent space to achieve trajectory prior modeling. This instance-centric scene representation method, combined with high-order map-self-agent interactions, can comprehensively and compactly describe autonomous driving scenarios.