Towards exploring adversarial learning for anomaly detection in complex
  driving scenes
Nour Habib Yunsu Cho Abhishek Buragohain Andreas Rausch
One of the many Autonomous Systems (ASs), such as autonomous driving cars,
performs various safety-critical functions. Many of these autonomous systems
take advantage of Artificial Intelligence (AI) techniques to perceive their
environment. But these perceiving components could not be formally verified,
since, the accuracy of such AI-based components has a high dependency on the
quality of training data. So Machine learning (ML) based anomaly detection, a
technique to identify data that does not belong to the training data could be
used as a safety measuring indicator during the development and operational
time of such AI-based components. Adversarial learning, a sub-field of machine
learning has proven its ability to detect anomalies in images and videos with
impressive results on simple data sets. Therefore, in this work, we investigate
and provide insight into the performance of such techniques on a highly complex
driving scenes dataset called Berkeley DeepDrive.