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Autonomous Driving vehicles (ADV) are on road with large scales. For safe andefficient operations, ADVs must be able to predict the future states anditerative with road entities in complex, real-world driving scenarios. How tomigrate a well-trained prediction model from one geo-fenced area to another isessential in scaling the ADV operation and is difficult most of the time sincethe terrains, traffic rules, entities distributions, driving/walking patternswould be largely different in different geo-fenced operation areas. In thispaper, we introduce a highly automated learning-based prediction modelpipeline, which has been deployed on Baidu Apollo self-driving platform, tosupport different prediction learning sub-modules' data annotation, featureextraction, model training/tuning and deployment. This pipeline is completelyautomatic without any human intervention and shows an up to 400\% efficiencyincrease in parameter tuning, when deployed at scale in different scenariosacross nations.

Data Driven Prediction Architecture for Autonomous Driving and its Application on Apollo Platform