Raymond Muller (Purdue University), Yanmao Man (University of Arizona), Z. Berkay Celik (Purdue University), Ming Li (University of Arizona) and Ryan Gerdes (Virginia Tech)

With emerging vision-based autonomous driving (AD) systems, it becomes increasingly important to have datasets to evaluate their correct operation and identify potential security flaws. However, when collecting a large amount of data, either human experts manually label potentially hundreds of thousands of image frames or systems use machine learning algorithms to label the data, with the hope that the accuracy is good enough for the application. This can become especially problematic when tracking the context information, such as the location and velocity of surrounding objects, useful to evaluate the correctness and improve stability and robustness of the AD systems.

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Yulong Cao, Jiaxiang Ma, Kevin Fu (University of Michigan), Sara Rampazzi (University of Florida), and Z. Morley Mao (University of Michigan) Best Demo Award Runner-up ($200 cash prize)!

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Hsun Lee (National Taiwan University), Yuming Hsu (National Taiwan University), Jing-Jie Wang (National Taiwan University), Hao Cheng Yang (National Taiwan University), Yu-Heng Chen (National Taiwan University), Yih-Chun Hu (University of Illinois at Urbana-Champaign), Hsu-Chun Hsiao (National Taiwan University)

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SynthCT: Towards Portable Constant-Time Code

Sushant Dinesh (University of Illinois at Urbana Champaign), Grant Garrett-Grossman (University of Illinois at Urbana Champaign), Christopher W. Fletcher (University of Illinois at Urbana Champaign)

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