  Autonomous Driving vehicles (ADV) are on road with large scales. For safe and
efficient operations, ADVs must be able to predict the future states and
iterative with road entities in complex, real-world driving scenarios. How to
migrate a well-trained prediction model from one geo-fenced area to another is
essential in scaling the ADV operation and is difficult most of the time since
the terrains, traffic rules, entities distributions, driving/walking patterns
would be largely different in different geo-fenced operation areas. In this
paper, we introduce a highly automated learning-based prediction model
pipeline, which has been deployed on Baidu Apollo self-driving platform, to
support different prediction learning sub-modules' data annotation, feature
extraction, model training/tuning and deployment. This pipeline is completely
automatic without any human intervention and shows an up to 400\% efficiency
increase in parameter tuning, when deployed at scale in different scenarios
across nations.
