  While inferring common actor states (such as position or velocity) is an
important and well-explored task of the perception system aboard a self-driving
vehicle (SDV), it may not always provide sufficient information to the SDV.
This is especially true in the case of active emergency vehicles (EVs), where
light-based signals also need to be captured to provide a full context. We
consider this problem and propose a sequential methodology for the detection of
active EVs, using an off-the-shelf CNN model operating at a frame level and a
downstream smoother that accounts for the temporal aspect of flashing EV
lights. We also explore model improvements through data augmentation and
training with additional hard samples.
