论文标题
数据驱动的预测架构用于自动驾驶及其在阿波罗平台上的应用
Data Driven Prediction Architecture for Autonomous Driving and its Application on Apollo Platform
论文作者
论文摘要
自动驾驶车辆（ADV）处于大尺度的道路上。为了安全有效的运营，ADV必须能够通过复杂的，现实世界中的驾驶场景中的道路实体来预测未来的状态和迭代。如何将训练有素的预测模型从一个地理束缚区域迁移到另一个地理区域对于扩展ADV操作至关重要，并且在大多数情况下很难迁移，因为在不同的地理运营区域中，地形，交通规则，实体分布，驾驶/步行模式将在很大程度上有所不同。在本文中，我们介绍了一条高度自动化的基于学习的预测模型管道，该管道已在Baidu Apollo自动驾驶平台上部署，以支持不同的预测学习子模块的数据注释，功能提取，模型培训/调整和部署。该管道是完全自动的，没有任何人类干预，并且在各个国家的不同情况下进行大规模部署时，参数调整的效率最高为400％。
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.
