  Multi-modality fusion is the guarantee of the stability of autonomous driving
systems. In this paper, we propose a general multi-modality cascaded fusion
framework, exploiting the advantages of decision-level and feature-level
fusion, utilizing target position, size, velocity, appearance and confidence to
achieve accurate fusion results. In the fusion process, dynamic coordinate
alignment(DCA) is conducted to reduce the error between sensors from different
modalities. In addition, the calculation of affinity matrix is the core module
of sensor fusion, we propose an affinity loss that improves the performance of
deep affinity network(DAN). Last, the proposed step-by-step cascaded fusion
framework is more interpretable and flexible compared to the end-toend fusion
methods. Extensive experiments on Nuscenes [2] dataset show that our approach
achieves the state-of-theart performance.dataset show that our approach
achieves the state-of-the-art performance.
