%0 Conference Proceedings
%T Characterizing Perception Module Performance and Robustness in Production-Scale Autonomous Driving System
%+ Department of Computer Science and Engineering [Shanghai] (CSE)
%+ Chuxiong Normal University
%A Toschi, Alessandro
%A Sanic, Mustafa
%A Leng, Jingwen
%A Chen, Quan
%A Wang, Chunlin
%A Guo, Minyi
%Z Part 6: Emerging Topics
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 16th IFIP International Conference on Network and Parallel Computing (NPC)
%C Hohhot, China
%Y Xiaoxin Tang
%Y Quan Chen
%Y Pradip Bose
%Y Weiming Zheng
%Y Jean-Luc Gaudiot
%I Springer International Publishing
%3 Network and Parallel Computing
%V LNCS-11783
%P 235-247
%8 2019-08-23
%D 2019
%R 10.1007/978-3-030-30709-7_19
%K Autonomous driving
%K Robustness analysis
%K Performance profiling
%K Deep neural networks
%Z Computer Science [cs]Conference papers
%X Autonomous driving is a field that gathers many interests in academics and industry and represents one of the most important challenges of next years. Although individual algorithms of autonomous driving have been studied and well understood, there is still a lack of study for those tasks in a production-scale system. In this work, we profile and analyze the perception module of the open-source autonomous driving system Apollo, developed by Baidu, in terms of response time and robustness against sensor errors. The perception module is fundamental to the proper functioning and safety of autonomous driving, which relies on several sensors, such as LIDARs and cameras, for detecting obstacles and perceiving the surrounding environment. We identify the computation characteristics and potential bottlenecks in the perception module. Furthermore, we design multiple noise models for the camera frames and LIDAR cloud points to test the robustness of the whole module in terms of accuracy drop against a noise-free baseline. Our insights are useful for future performance and robustness optimization of autonomous driving system.
%G English
%Z TC 10
%Z WG 10.3
%2 https://inria.hal.science/hal-03770538/document
%2 https://inria.hal.science/hal-03770538/file/486810_1_En_19_Chapter.pdf
%L hal-03770538
%U https://inria.hal.science/hal-03770538
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC10
%~ IFIP-NPC
%~ IFIP-WG10-3
%~ IFIP-LNCS-11783