UBTransformer: Uncertainty-Based Transformer Model for Complex Scenarios Detection in Autonomous Driving

This is the authors' abstract. We don't add key points for this paper.

The traditional object detection algorithm in the intelligent vehicle perception system cannot maintain stable recognition performance in the unknown and changing road environment. We find that uncertainty quantification is of great significance in detecting unknown complex environments and helps to improve the robustness and safety of autonomous driving systems. Therefore, this paper proposes an Uncertaintybased Transformer (UBT) object detection algorithm. Firstly, the double Gaussian feature map network (DGF) is designed to quantify and utilize the uncertainty of the features derived from the backbone network. Secondly, we propose a RBFbased query filtering model(RBQF), which takes uncertainty sum as the index of query vector screening. At the same time, this paper proposes an uncertainty detection head (UDH); the final model output results are quantitative uncertainty, improved detection performance and enhanced algorithm reliability. To further prove the detection performance of the proposed method in real driving scenes, we use COCO, Cityscapes, FoggyCi-tyscapes, RainCityscapes and self-made traffic scene datasets for verification, which shows that our algorithm is well applicable to large datasets and complex road scenes. Our codebase and pre-trained models can be accessed at https://github.com/sotifma/UBT/tree/master