  The potentials of automotive radar for autonomous driving have not been fully
exploited. We present a multi-input multi-output (MIMO) radar transmit and
receive signal processing chain, a knowledge-aided approach exploiting the
radar domain knowledge and signal structure, to generate high resolution radar
range-azimuth spectra for object detection and classification using deep neural
networks. To achieve waveform orthogonality among a large number of transmit
antennas cascaded by four automotive radar transceivers, we propose a staggered
time division multiplexing (TDM) scheme and velocity unfolding algorithm using
both Chinese remainder theorem and overlapped array. Field experiments with
multi-modal sensors were conducted at The University of Alabama. High
resolution radar spectra were obtained and labeled using the camera and LiDAR
recordings. Initial experiments show promising performance of object detection
using an image-oriented deep neural network with an average precision of 96.1%
at an intersection of union (IoU) of typically 0.5 on 2,000 radar frames.
