Search Results for author:
Found 25 papers, 9 papers with code
Causality-based Transfer of Driving Scenarios to Unseen Intersections
Scenario-based testing of automated driving functions has become a promising method to reduce time and cost compared to real-world testing.
MultiCorrupt: A Multi-Modal Robustness Dataset and Benchmark of LiDAR-Camera Fusion for 3D Object Detection
Multi-modal 3D object detection models for automated driving have demonstrated exceptional performance on computer vision benchmarks like nuScenes.
3D Point Cloud Compression with Recurrent Neural Network and Image Compression Methods
Transforming the raw point cloud data into a dense 2D matrix structure is a promising way for applying compression algorithms.
Enhancing Lidar-based Object Detection in Adverse Weather using Offset Sequences in Time
Our findings demonstrate that our novel method, involving temporal offset augmentation through randomized frame skipping in sequences, enhances object detection accuracy compared to both the baseline model (Pillar-based Object Detection) and no augmentation.
Explainable Multi-Camera 3D Object Detection with Transformer-Based Saliency Maps
Vision Transformers (ViTs) have achieved state-of-the-art results on various computer vision tasks, including 3D object detection.
Economic Analysis of Smart Roadside Infrastructure Sensors for Connected and Automated Mobility
Due to its modularity, the calculation model is suitable for diverse applications and outputs a distinctive evaluation of the underlying cost-benefit ratio of investigated setups.
Framework for Quality Evaluation of Smart Roadside Infrastructure Sensors for Automated Driving Applications
The use of smart roadside infrastructure sensors is highly relevant for future applications of connected and automated vehicles.
Robust Environment Perception for Automated Driving: A Unified Learning Pipeline for Visual-Infrared Object Detection
The RGB complementary metal-oxidesemiconductor (CMOS) sensor works within the visible light spectrum.
The exiD Dataset: A Real-World Trajectory Dataset of Highly Interactive Highway Scenarios in Germany
For system-level evaluation and safety validation of an automated driving system, real-world trajectory datasets are of great value for several tasks in the process, i. a.
An Automated Analysis Framework for Trajectory Datasets
Considering this amount of data, it is necessary to be able to compare these datasets in-depth with ease to get an overview.
Corridor for new mobility Aachen-Düsseldorf: Methods and concepts of the research project ACCorD
With the Corridor for New Mobility Aachen - D\"usseldorf, an integrated development environment is created, incorporating existing test capabilities, to systematically test and validate automated vehicles in interaction with connected Intelligent Transport Systems Stations (ITS-Ss).
Highly accurate digital traffic recording as a basis for future mobility research: Methods and concepts of the research project HDV-Mess
The research project HDV-Mess aims at a currently missing, but very crucial component for addressing important challenges in the field of connected and automated driving on public roads.
Systematic Categorization of Influencing Factors on Radar-Based Perception to Facilitate Complex Real-World Data Evaluation
On top of the literature review on environment factors influencing radar sensors, the paper introduces a modular structuring concept for such that can facilitate real-world data analysis by categorizing the factors possibly leading to performance limitations into different independent clusters in order to reduce the level of detail in complex real-world environments.
A Review of Testing Object-Based Environment Perception for Safe Automated Driving
Safety assurance of automated driving systems must consider uncertain environment perception.
6-Layer Model for a Structured Description and Categorization of Urban Traffic and Environment
To define those scenarios and operate in a complex real-world design domain, a structured description of the environment is needed.
Deep Inverse Sensor Models as Priors for evidential Occupancy Mapping
In this work, we describe a novel approach to integrate deep ISMs together with geometric ISMs into the evidential occupancy mapping framework.
The rounD Dataset: A Drone Dataset of Road User Trajectories at Roundabouts in Germany
We present a new dataset of road user trajectories at roundabouts in Germany.
Real-Time Point Cloud Fusion of Multi-LiDAR Infrastructure Sensor Setups with Unknown Spatial Location and Orientation
The use of infrastructure sensor technology for traffic detection has already been proven several times.
High-Precision Digital Traffic Recording with Multi-LiDAR Infrastructure Sensor Setups
We then analyze the properties of the resulting point clouds and perform MODT for all emerging traffic participants.
A Sim2Real Deep Learning Approach for the Transformation of Images from Multiple Vehicle-Mounted Cameras to a Semantically Segmented Image in Bird's Eye View
Accurate environment perception is essential for automated driving.
Ranked #1 on
Semantic Segmentation
on Cam2BEV
Bird View Synthesis
Cross-View Image-to-Image Translation
+2
Reducing Uncertainty by Fusing Dynamic Occupancy Grid Maps in a Cloud-based Collective Environment Model
This paper describes a method to combine perception data of automated and connected vehicles in the form of evidential Dynamic Occupany Grid Maps (DOGMas) in a cloud-based system.
The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersections
The dataset consists of 10 hours of measurement data from four intersections and is available online for non-commercial research at: http://www. inD-dataset. com
Deep, spatially coherent Occupancy Maps based on Radar Measurements
One essential step to realize modern driver assistance technology is the accurate knowledge about the location of static objects in the environment.
Deep, spatially coherent Inverse Sensor Models with Uncertainty Incorporation using the evidential Framework
To perform high speed tasks, sensors of autonomous cars have to provide as much information in as few time steps as possible.
The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems
Scenario-based testing for the safety validation of highly automated vehicles is a promising approach that is being examined in research and industry.
