End-to-end autonomous driving (E2E AD) has become a popular hit recently. Compared to conventional modular based design, the main advantages of E2E AD descend from the global optimization across sub-modules in the system, and the unanimous objective towards planning and control task. Some leading corporations from industry have also adopted such an end-to-end philosophy and rolled out to customers on the product end. In this talk, we will first walkthrough the key milestones and roadmap of E2E AD, including the first prototypical work UniAD, proposed by OpenDriveLab and recognized as IEEE CVPR 2023 Best Paper. Then the pivotal challenges (generalization, world model, etc.) that the community currently are confronted with are discussed. With the prevalence of foundation models, how to utilize LLM/VLM techniques and distill common knowledge from experienced human experts are covered as well.The talk would be concluded with some future perspectives on building towards a general and intelligent system in a wide span of downstream applications for the next couple years.