# Capturing Object Detection Uncertainty in Multi-Layer Grid Maps Source: [Capturing Object Detection Uncertainty in Multi-Layer Grid Maps]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:24:52.728Z Authors: Sascha Wirges, Marcel Reith-Braun, Martin Lauer, Christoph Stiller Published: 2019-01-31T09:33:26.000Z ## Abstract We propose a deep convolutional object detector for automated driving applications that also estimates classification, pose and shape uncertainty of each detected object. The input consists of a multi-layer grid map which is well-suited for sensor fusion, free-space estimation and machine learning. Based on the estimated pose and shape uncertainty we approximate object hulls with bounded collision probability which we find helpful for subsequent trajectory planning tasks. We train our models based on the KITTI object detection data set. In a quantitative and qualitative evaluation some models show a similar performance and superior robustness compared to previously developed object detectors. However, our evaluation also points to undesired data set properties which should be addressed when training data-driven models or creating new data sets. ## 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]()