  Directly producing planning results from raw sensors has been a long-desired
solution for autonomous driving and has attracted increasing attention
recently. Most existing end-to-end autonomous driving methods factorize this
problem into perception, motion prediction, and planning. However, we argue
that the conventional progressive pipeline still cannot comprehensively model
the entire traffic evolution process, e.g., the future interaction between the
ego car and other traffic participants and the structural trajectory prior. In
this paper, we explore a new paradigm for end-to-end autonomous driving, where
the key is to predict how the ego car and the surroundings evolve given past
scenes. We propose GenAD, a generative framework that casts autonomous driving
into a generative modeling problem. We propose an instance-centric scene
tokenizer that first transforms the surrounding scenes into map-aware instance
tokens. We then employ a variational autoencoder to learn the future trajectory
distribution in a structural latent space for trajectory prior modeling. We
further adopt a temporal model to capture the agent and ego movements in the
latent space to generate more effective future trajectories. GenAD finally
simultaneously performs motion prediction and planning by sampling
distributions in the learned structural latent space conditioned on the
instance tokens and using the learned temporal model to generate futures.
Extensive experiments on the widely used nuScenes benchmark show that the
proposed GenAD achieves state-of-the-art performance on vision-centric
end-to-end autonomous driving with high efficiency. Code:
https://github.com/wzzheng/GenAD.
