# Leveraging Uncertainties for Deep Multi-modal Object Detection in Autonomous Driving Source: [Leveraging Uncertainties for Deep Multi-modal Object Detection in Autonomous Driving]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:03:19.943Z Authors: Di Feng, Yifan Cao, Lars Rosenbaum, Fabian Timm, Klaus Dietmayer Published: 2020-02-01T14:24:51.000Z ## Abstract This work presents a probabilistic deep neural network that combines LiDAR point clouds and RGB camera images for robust, accurate 3D object detection. We explicitly model uncertainties in the classification and regression tasks, and leverage uncertainties to train the fusion network via a sampling mechanism. We validate our method on three datasets with challenging real-world driving scenarios. Experimental results show that the predicted uncertainties reflect complex environmental uncertainty like difficulties of a human expert to label objects. The results also show that our method consistently improves the Average Precision by up to 7% compared to the baseline method. When sensors are temporally misaligned, the sampling method improves the Average Precision by up to 20%, showing its high robustness against noisy sensor inputs. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()