41 citations · 71 across the 11 of their papers we have counts for
15 papers
Accurate Training Data for Occupancy Map Prediction in Automated Driving Using Evidence Theory
Jonas Kälble, Sascha Wirges, Maxim Tatarchenko +1
Automated driving fundamentally requires knowledge about the surrounding geometry of the scene. Modern approaches use only captured images to predict occupancy maps that represent…
Mapping LiDAR and Camera Measurements in a Dual Top-View Grid Representation Tailored for Automated Vehicles
Sven Richter, Frank Bieder, Sascha Wirges +1
We present a generic evidential grid mapping pipeline designed for imaging sensors such as LiDARs and cameras. Our grid-based evidential model contains semantic estimates for cell…
Sensor Data Fusion in Top-View Grid Maps using Evidential Reasoning with Advanced Conflict Resolution
Sven Richter, Frank Bieder, Sascha Wirges +1
We present a new method to combine evidential top-view grid maps estimated based on heterogeneous sensor sources. Dempster's combination rule that is usually applied in this contex…
Fast and Robust Ground Surface Estimation from LIDAR Measurements using Uniform B-Splines
Sascha Wirges, Kevin Rösch, Frank Bieder +1
We propose a fast and robust method to estimate the ground surface from LIDAR measurements on an automated vehicle. The ground surface is modeled as a UBS which is robust towards v…
SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation
Juncong Fei, Wenbo Chen, Philipp Heidenreich +2
3D pedestrian detection is a challenging task in automated driving because pedestrians are relatively small, frequently occluded and easily confused with narrow vertical objects. L…
Exploiting Multi-Layer Grid Maps for Surround-View Semantic Segmentation of Sparse LiDAR Data
Frank Bieder, Sascha Wirges, Johannes Janosovits +3
In this paper, we consider the transformation of laser range measurements into a top-view grid map representation to approach the task of LiDAR-only semantic segmentation. Since th…
Learned Enrichment of Top-View Grid Maps Improves Object Detection
Sascha Wirges, Ye Yang, Sven Richter +2
We propose an object detector for top-view grid maps which is additionally trained to generate an enriched version of its input. Our goal in the joint model is to improve generaliz…
Single-Stage Object Detection from Top-View Grid Maps on Custom Sensor Setups
Sascha Wirges, Shuxiao Ding, Christoph Stiller
We present our approach to unsupervised domain adaptation for single-stage object detectors on top-view grid maps in automated driving scenarios. Our goal is to train a robust obje…
Localization in Aerial Imagery with Grid Maps using LocGAN
Haohao Hu, Junyi Zhu, Sascha Wirges +1
In this work, we present LocGAN, our localization approach based on a geo-referenced aerial imagery and LiDAR grid maps. Currently, most self-localization approaches relate the cur…
Self-Supervised Flow Estimation using Geometric Regularization with Applications to Camera Image and Grid Map Sequences
Sascha Wirges, Johannes Gräter, Qiuhao Zhang +1
We present a self-supervised approach to estimate flow in camera image and top-view grid map sequences using fully convolutional neural networks in the domain of automated driving.…
Capturing Object Detection Uncertainty in Multi-Layer Grid Maps
Sascha Wirges, Marcel Reith-Braun, Martin Lauer +1
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…
Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks
Sascha Wirges, Tom Fischer, Jesus Balado Frias +1
A detailed environment perception is a crucial component of automated vehicles. However, to deal with the amount of perceived information, we also require segmentation strategies.…
An Approach to Vehicle Trajectory Prediction Using Automatically Generated Traffic Maps
Jannik Quehl, Haohao Hu, Sascha Wirges +1
Trajectory and intention prediction of traffic participants is an important task in automated driving and crucial for safe interaction with the environment. In this paper, we prese…
Evidential Occupancy Grid Map Augmentation using Deep Learning
Sascha Wirges, Felix Hartenbach, Christoph Stiller
A detailed environment representation is a crucial component of automated vehicles. Using single range sensor scans, data is often too sparse and subject to occlusions. Therefore,…
Guided Depth Upsampling for Precise Mapping of Urban Environments
Sascha Wirges, Björn Roxin, Eike Rehder +2
We present an improved model for MRF-based depth upsampling, guided by image- as well as 3D surface normal features. By exploiting the underlying camera model we define a novel reg…