  For high resolution scene mapping and object recognition, optical
technologies such as cameras and LiDAR are the sensors of choice. However, for
robust future vehicle autonomy and driver assistance in adverse weather
conditions, improvements in automotive radar technology, and the development of
algorithms and machine learning for robust mapping and recognition are
essential. In this paper, we describe a methodology based on deep neural
networks to recognise objects in 300GHz radar images, investigating robustness
to changes in range, orientation and different receivers in a laboratory
environment. As the training data is limited, we have also investigated the
effects of transfer learning. As a necessary first step before road trials, we
have also considered detection and classification in multiple object scenes.
