Design Automation for Intelligent Automotive Systems

Shuyue Lan, Chao Huang, Zhilu Wang, Hengyi Liang, Wenhao Su, Qi Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


With rapid advancement of advanced driver assistance systems (ADAS) and autonomous driving functions, modern vehicles have become ever more intelligent than before. Sophisticated machine learning techniques have being developed for vehicle perception, planning and control. However, this also brings significant challenges to the design, implementation and validation of automotive systems, stemming from the fast-growing functional complexity, the adoption of advanced architectural components such as multicore CPUs and GPUs, the dynamic and uncertain physical environment, and the stringent requirements on various system metrics such as safety, security, reliability, performance, fault tolerance, extensibility, and cost. To address these challenges, new design methodologies, algorithms and tools are greatly needed. This paper will discuss the challenges in designing next-generation connected and autonomous vehicles, and the need of design automation techniques to tackle them.

Original languageEnglish (US)
Title of host publicationInternational Test Conference 2018, ITC 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538683828
StatePublished - Jul 2 2018
Event49th IEEE International Test Conference, ITC 2018 - Phoenix, United States
Duration: Oct 29 2018Nov 1 2018

Publication series

NameProceedings - International Test Conference
ISSN (Print)1089-3539


Conference49th IEEE International Test Conference, ITC 2018
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Applied Mathematics


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