CPT:A Configurable Predictability Testbed for DNN Inference in AVs  

作　　者：Liangkai Liu Yanzhi Wang Weisong Shi 

机构地区：[1]Department of Computer and Information Science,University of Delaware,Newark,DE 19713,USA [2]Department of Electrical&Computer Engineering,Northeastern University,Boston,MA 02115,USA

出　　处：《Tsinghua Science and Technology》2025年第1期87-99,共13页清华大学学报自然科学版(英文版)

摘　　要：Predictability is an essential challenge for autonomous vehicles(AVs)’safety.Deep neural networks have been widely deployed in the AV’s perception pipeline.However,it is still an open question on how to guarantee the perception predictability for AV because there are millions of deep neural networks(DNNs)model combinations and system configurations when deploying DNNs in AVs.This paper proposes configurable predictability testbed(CPT),a configurable testbed for quantifying the predictability in AV’s perception pipeline.CPT provides flexible configurations of the perception pipeline on data,DNN models,fusion policy,scheduling policies,and predictability metrics.On top of CPT,the researchers can profile and optimize the predictability issue caused by different application and system configurations.CPT has been open-sourced at:https://github.com/Torreskai0722/CPT.

关 键 词：PREDICTABILITY deep neural network autonomous driving 

分 类 号：TN9[电子电信—信息与通信工程]