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Friday, June 23, 2023 – 12:00PM to 1:00PM
Dr. Xi Zheng. Macquarie University, Australia
Autonomous driving has shown great potential to reform modern transportation. Yet its reliability and safety have drawn a lot of attention and concerns. Compared with traditional software systems, autonomous driving systems (ADSs) often use deep neural networks in tandem with logic-based modules. This new paradigm poses unique challenges for software testing. Despite the recent development of new ADS testing techniques, it is not clear to what extent those techniques have addressed the needs of ADS practitioners. To fill this gap, we have published a series of works and I will present some of them. The first work is to conduct a comprehensive study to identify the current practices, needs and gaps in testing autonomous driving systems (FSE'22). The second work is to reduce and prioritize test for multi-module autonomous driving systems (FSE'22). The third work is to generate test cases from traffic rules for autonomous driving models (TSE'22). I will also cover some ongoing and future work in autonomous driving systems based on our prior work analysing the robustness issues in the deep learning driving models (PerCom'20) and tackling challenges in distributed learning of connected intelligent vehicles (PerCom'21).
Dr. Xi Zheng received the Ph.D. in Software Engineering from the University of Texas at Austin in 2015. From 2005 to 2012, he was the Chief Solution Architect for Menulog Australia. He is currently the Director of Intelligent Systems Research Group (ITSEG.ORG), Director of International engagement in the School of Computing, Senior Lecturer (equivalent to Associate Professor US) and Deputy Program Leader in Software Engineering, Macquarie University, Australia. His research interests include Cyber-Physical Systems Testing and Verification, Safety Analysis, Distributed Learning, Internet of Things, and Software Engineering in general. He has secured more than $1.2 million competitive funding in Australian Research Council (Linkage and Discovery) and Data61 (CRP) projects on safety analysis, model testing and verification, and trustworthy AI on autonomous vehicles. He also won a few awards including Deakin Industry Researcher (2016) and MQ Earlier Career Researcher (Runner-up 2020). He has a number of highly cited papers and best conference papers. He served as PC members for Software and System flagship conferences including FSE (2022) and PerCom (2017-2023).