Vehicle Detection in Adverse Weather: A Multi-Head Attention Approach with Multimodal Fusion

Abstract

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) of 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.