7 citations
9 papers · 1 filter
AttentiveGRU: Recurrent Spatio-Temporal Modeling for Advanced Radar-Based BEV Object Detection
Loveneet Saini, Mirko Meuter, Hasan Tercan +1
Bird's-eye view (BEV) object detection has become important for advanced automotive 3D radar-based perception systems. However, the inherently sparse and non-deterministic nature o…
Adverse Weather Conditions Augmentation of LiDAR Scenes with Latent Diffusion Models
Andrea Matteazzi, Pascal Colling, Michael Arnold +1
LiDAR scenes constitute a fundamental source for several autonomous driving applications. Despite the existence of several datasets, scenes from adverse weather conditions are rare…
SparseRadNet: Sparse Perception Neural Network on Subsampled Radar Data
Jialong Wu, Mirko Meuter, Markus Schoeler +1
Radar-based perception has gained increasing attention in autonomous driving, yet the inherent sparsity of radars poses challenges. Radar raw data often contains excessive noise, w…
A Preprocessing and Postprocessing Voxel-based Method for LiDAR Semantic Segmentation Improvement in Long Distance
Andrea Matteazzi, Pascal Colling, Michael Arnold +1
In recent years considerable research in LiDAR semantic segmentation was conducted, introducing several new state of the art models. However, most research focuses on single-scan p…
Deep Learning Method for Cell-Wise Object Tracking, Velocity Estimation and Projection of Sensor Data over Time
Marco Braun, Moritz Luszek, Mirko Meuter +3
Current Deep Learning methods for environment segmentation and velocity estimation rely on Convolutional Recurrent Neural Networks to exploit spatio-temporal relationships within o…
Semantic Segmentation of Radar Detections using Convolutions on Point Clouds
Marco Braun, Alessandro Cennamo, Markus Schoeler +2
For autonomous driving, radar sensors provide superior reliability regardless of weather conditions as well as a significantly high detection range. State-of-the-art algorithms for…