Autonomous driving holds the keys to a future where driving time could be reallocated to other, more productive activities, where accidents due to human errors are minimized and where traffic could be optimized to reduce energy consumption. However, much progress still needs to be made for this to happen. In particular, handling of anomalous, hazardous objects, rarely seen by the vehicle during normal operation, is particularly difficult for the current state-of-the-art autonomous cars; this poses safety risks that sometimes lead to dangerous accidents, including pedestrian death. Furthermore, most deep learning models deployed on these cars are so called “black-box”, because it’s difficult to predict their behavior reliably and to identify what caused an accident after it happened.