A MULTI-HEAD ATTENTION APPROACH WITH COMPLEMENTARY MULTIMODAL FUSION FOR VEHICLE DETECTION

In the realm of autonomous vehicle technology, the Multimodal Vehicle Detection Network (MVDNet) represents a significant leap forward, particularly in the challenging context of weather conditions. This paper focuses on the enhancement of MVDNet through the integration of a multi-head attention layer, aimed at refining its performance. The integrated multi-head attention layer in the MVDNet model is a pivotal modification, advancing the network's ability to process and fuse multimodal sensor information more efficiently. The paper validates the improved performance of MVDNet with multi-head attention through comprehensive testing, which includes a training dataset derived from the Oxford Radar Robotcar. The results clearly demonstrate that the Multi-Head MVDNet outperforms the other related conventional models, particularly in the Average Precision (AP) estimation, under challenging environmental conditions. The proposed Multi-Head MVDNet not only contributes significantly to the field of autonomous vehicle detection but also underscores the potential of sophisticated sensor fusion techniques in overcoming environmental limitations.