  The development of high-resolution imaging radars introduce a plethora of
useful applications, particularly in the automotive sector. With increasing
attention on active transport safety and autonomous driving, these imaging
radars are set to form the core of an autonomous engine. One of the most
important tasks of such high-resolution radars is to estimate the instantaneous
velocities and heading angles of the detected objects (vehicles, pedestrians,
etc.). Feasible estimation methods should be fast enough in real-time
scenarios, bias-free and robust against micro-Dopplers, noise and other
systemic variations. This work proposes a parallel-computing scheme that
achieves a real-time and accurate implementation of vector velocity
determination using frequency modulated continuous wave (FMCW) radars. The
proposed scheme is tested against traffic data collected using an FMCW radar at
a center frequency of 78.6 GHz and a bandwidth of 4 GHz. Experiments show that
the parallel algorithm presented performs much faster than its conventional
counterparts without any loss in precision.
