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.
DRIVETRUTH: Automated Autonomous Driving Dataset Generation for Security Applications
Raymond Muller (Purdue University), Yanmao Man (University of Arizona), Z. Berkay Celik (Purdue University), Ming Li (University of Arizona) and Ryan Gerdes (Virginia Tech)
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