The application of modern series production automotive radar sensors to
pedestrian recognition is an important topic in research on future driver
assistance systems. The aim of this paper is to understand the potential and
limits of such sensors in pedestrian recognition. This knowledge could be
used to develop next generation radar sensors with improved pedestrian
recognition capabilities. A new raw radar data signal processing algorithm is
proposed that allows deep insights into the object classification process.
The impact of raw radar data properties can be directly observed in every
layer of the classification system by avoiding machine learning and tracking.
This gives information on the limiting factors of raw radar data in terms of
classification decision making. To accomplish the very challenging
distinction between pedestrians and static objects, five significant and
stable object features from the spatial distribution and Doppler information
are found. Experimental results with data from a 77 GHz automotive radar
sensor show that over 95% of pedestrians can be classified correctly under
optimal conditions, which is compareable to modern machine learning systems.
The impact of the pedestrian's direction of movement, occlusion, antenna beam
elevation angle, linear vehicle movement, and other factors are investigated
and discussed. The results show that under real life conditions, radar only
based pedestrian recognition is limited due to insufficient Doppler frequency
and spatial resolution as well as antenna side lobe effects
