1.4k citations · 1.5k across the 15 of their papers we have counts for
15 papers
ALOOD: Exploiting Language Representations for LiDAR-based Out-of-Distribution Object Detection
Michael Kösel, Marcel Schreiber, Michael Ulrich +2
LiDAR-based 3D object detection plays a critical role for reliable and safe autonomous driving systems. However, existing detectors often produce overly confident predictions for o…
Bosch Street Dataset: A Multi-Modal Dataset with Imaging Radar for Automated Driving
Karim Armanious, Maurice Quach, Michael Ulrich +25
This paper introduces the Bosch street dataset (BSD), a novel multi-modal large-scale dataset aimed at promoting highly automated driving (HAD) and advanced driver-assistance syste…
Group Regression for Query Based Object Detection and Tracking
Felicia Ruppel, Florian Faion, Claudius Gläser +1
Group regression is commonly used in 3D object detection to predict box parameters of similar classes in a joint head, aiming to benefit from similarities while separating highly d…
Sensor Visibility Estimation: Metrics and Methods for Systematic Performance Evaluation and Improvement
Joachim Börger, Marc Patrick Zapf, Marat Kopytjuk +2
Sensor visibility is crucial for safety-critical applications in automotive, robotics, smart infrastructure and others: In addition to object detection and occupancy mapping, visib…
Can Transformer Attention Spread Give Insights Into Uncertainty of Detected and Tracked Objects?
Felicia Ruppel, Florian Faion, Claudius Gläser +1
Transformers have recently been utilized to perform object detection and tracking in the context of autonomous driving. One unique characteristic of these models is that attention…
Transformers for Object Detection in Large Point Clouds
Felicia Ruppel, Florian Faion, Claudius Gläser +1
We present TransLPC, a novel detection model for large point clouds that is based on a transformer architecture. While object detection with transformers has been an active field o…
Self-Supervised Velocity Estimation for Automotive Radar Object Detection Networks
Daniel Niederlöhner, Michael Ulrich, Sascha Braun +5
This paper presents a method to learn the Cartesian velocity of objects using an object detection network on automotive radar data. The proposed method is self-supervised in terms…
Transformers for Multi-Object Tracking on Point Clouds
Felicia Ruppel, Florian Faion, Claudius Gläser +1
We present TransMOT, a novel transformer-based end-to-end trainable online tracker and detector for point cloud data. The model utilizes a cross- and a self-attention mechanism and…
Improved Orientation Estimation and Detection with Hybrid Object Detection Networks for Automotive Radar
Michael Ulrich, Sascha Braun, Daniel Köhler +4
This paper presents novel hybrid architectures that combine grid- and point-based processing to improve the detection performance and orientation estimation of radar-based object d…
A Multi-Task Recurrent Neural Network for End-to-End Dynamic Occupancy Grid Mapping
Marcel Schreiber, Vasileios Belagiannis, Claudius Gläser +1
A common approach for modeling the environment of an autonomous vehicle are dynamic occupancy grid maps, in which the surrounding is divided into cells, each containing the occupan…
Pedestrian Behavior Prediction for Automated Driving: Requirements, Metrics, and Relevant Features
Michael Herman, Jörg Wagner, Vishnu Prabhakaran +6
Automated vehicles require a comprehensive understanding of traffic situations to ensure safe and anticipatory driving. In this context, the prediction of pedestrians is particular…
DeepReflecs: Deep Learning for Automotive Object Classification with Radar Reflections
Michael Ulrich, Claudius Gläser, Fabian Timm
This paper presents an novel object type classification method for automotive applications which uses deep learning with radar reflections. The method provides object class informa…
Where can I drive? A System Approach: Deep Ego Corridor Estimation for Robust Automated Driving
Thomas Michalke, Di Feng, Claudius Gläser +1
Lane detection is an essential part of the perception sub-architecture of any automated driving (AD) or advanced driver assistance system (ADAS). When focusing on low-cost, large s…
Motion Estimation in Occupancy Grid Maps in Stationary Settings Using Recurrent Neural Networks
Marcel Schreiber, Vasileios Belagiannis, Claudius Glaeser +1
In this work, we tackle the problem of modeling the vehicle environment as dynamic occupancy grid map in complex urban scenarios using recurrent neural networks. Dynamic occupancy…
Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges
Di Feng, Christian Haase-Schütz, Lars Rosenbaum +5
Recent advancements in perception for autonomous driving are driven by deep learning. In order to achieve robust and accurate scene understanding, autonomous vehicles are usually e…