1、End-to-end Autonomous Driving Industry Report,2024-2025D End-to-end intelligent driving research:How Li Auto becomes a leader from an intelligent driving followerThere are two types of end-to-end autonomous driving:global(one-stage)and segmented(two-stage)types.The former has a clear concept,and muc
2、h lower R&D cost than the latter,because it does not require any manually annotated data sets but relies on multimodal foundation models developed by Google,META,Alibaba and OpenAI.Standing on the shoulders of these technology giants,the performance of global end-to-end autonomous driving is much be
3、tter than segmented end-to-end autonomous driving,but at extremely high deployment cost.Segmented end-to-end autonomous driving still uses the traditional CNN backbone network to extract features for perception,and adopts end-to-end path planning.Although its performance is not as good as global end
4、-to-end autonomous driving,it has lower deployment cost.However,the deployment cost of segmented end-to-end autonomous driving is still very high compared with the current mainstream traditional“BEV+OCC+decision tree” UniADAs a representative of global end-to-end autonomous driving,Waymo EMMA direct
5、ly inputs videos without a backbone network but with a multimodal foundation model as the core.UniAD is a representative of segmented end-to-end autonomous End-to-end autonomous driving researches are mainly divided into two categoriesBased on whether feedback can be obtained,end-to-end autonomous d
6、riving researches are mainly divided into two categories:the research is conducted in simulators such as CARLA,and the next planned instructions can be actually performed;the research based on collected real data,mainly imitation learning,referring to UniAD.End-to-end autonomous driving currently fe
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