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

Autor:	Xu, Kecheng, Xiao, Xiangquan, Miao, Jinghao, Luo, Qi
Rok vydání:	2020
Předmět:	Computer Science - Robotics Computer Science - Computer Vision and Pattern Recognition Computer Science - Software Engineering
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted by the 31st IEEE Intelligent Vehicles Symposium (2020)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2006.06715 Zobrazit plný text záznamu View this record from Arxiv