  Emergency vehicles in service have right-of-way over all other vehicles.
Hence, all other vehicles are supposed to take proper actions to yield
emergency vehicles with active sirens. As this task requires the cooperation
between ears and eyes for human drivers, it also needs audio detection as a
supplement to vision-based algorithms for fully autonomous driving vehicles. In
urban driving scenarios, we need to know both the existence of emergency
vehicles and their relative positions to us to decide the proper actions. We
present a novel system from collecting the real-world siren data to the
deployment of models using only two cost-efficient microphones. We are able to
achieve promising performance for each task separately, especially within the
crucial 10m to 50m distance range to react (the size of our ego vehicle is
around 5m in length and 2m in width). The recall rate to determine the
existence of sirens is 99.16% , the median and mean angle absolute error is
9.64{\deg} and 19.18{\deg} respectively, and the median and mean distance
absolute error of 9.30m and 10.58m respectively within that range. We also
benchmark various machine learning approaches that can determine the siren
existence and sound source localization which includes direction and distance
simultaneously within 50ms of latency.
