Uncertainty-Aware for Robust Object Detection in Autonomous Vehicles

This paper proposes an uncertainty modeling method based on the YOLOv11 object detection algorithm to address the challenges posed by rainy, snowy, and foggy weather to the accuracy and reliability of autonomous driving visual perception systems. The method uses a Laplace distribution to model the uncertainty of predicted bounding boxes and employs information entropy to quantify the uncertainty of classification tasks. These quantified uncertainties are incorporated into redesigned bounding box loss functions and classification loss functions, enabling the model to adaptively adjust its focus on bounding box prediction accuracy and classification confidence. Experimental validation was conducted on the CADC and BDD100K dataset, demonstrating that the proposed method achieves improvements of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9. 3 \%}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{5. 2 \%}$</tex> in mAP compared to baseline models. The experimental results demonstrate that the proposed method significantly improves the robustness and accuracy of algorithms under adverse weather conditions, reduces the probability of missed or false detections, and enhances detection precision and prediction reliability.