Object Detection and Heading Forecasting by fusing Raw Radar Data using Cross Attention
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Radar has been believed to be an inevitable sensor for advanced driver assistance systems (ADAS) for decades. Along with providing robust range, angle and velocity measurements, it is also cost-effective. Hence, radar is expected to play a big role in the next generation ADAS. In this paper, we propose a neural network for object detection and heading forecasting based on radar by fusing three raw radar channels with a cross-attention mechanism. We also introduce an improved ground truth augmentation method based on Bivariate norm, which represents the object labels in a more realistic form for radar measurements. Our results show 5 methods. To the best of our knowledge, this is the first attempt in the radar field, where cross-attention is utilized for object detection and heading forecasting without the use of object tracking and association.
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