7 citations
12 papers
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…
CASPFormer: Trajectory Prediction from BEV Images with Deformable Attention
Harsh Yadav, Maximilian Schaefer, Kun Zhao +1
Motion prediction is an important aspect for Autonomous Driving (AD) and Advance Driver Assistance Systems (ADAS). Current state-of-the-art motion prediction methods rely on High D…
Task Weighting through Gradient Projection for Multitask Learning
Christian Bohn, Ido Freeman, Hasan Tercan +1
In multitask learning, conflicts between task gradients are a frequent issue degrading a model's training performance. This is commonly addressed by using the Gradient Projection a…
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…