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Publications

DELIVERABLES

Here we publish the various deliverables of the DENSE project.

Scientific Publications

Here we list all the published scientific publications in the context of the DENSE project.

 

2017

 

Aho, Antti & Viheriälä, Jukka & Mäkelä, Jaakko & Virtanen, Heikki & Ranta, Sanna & Dumitrescu, Mihail & Guina, Mircea. (2017). High-power 1550 nm tapered DBR laser diodes for LIDAR applications. 1-1.
10.1109/CLEOE-EQEC.2017.8086373

 

 Viheriälä, Jukka & Aho, Antti & Virtanen, Heikki & Koskinen, Mervi & Dumitrescu, Mihail & Guina, Mircea. (2017). 1180 nm GaInNAs quantum well based high power DBR laser diodes. 100860K.
10.1117/12.2251317

 

2018

 

Jukka Viheriälä, Antti T. Aho, Heikki Virtanen, Sanna Ranta, Mihail Dumitrescu, Mircea Guina. (2018). High-Power 1550 nm DBR Laser Diodes for LIDAR Applications.
https://ieeexplore.ieee.org/document/8086373

 

M. Bijelic, T. Gruber, W. Ritter. (2018). A Benchmark for Lidar Sensors in Fog: Is Detection Breaking Down?
https://www.researchgate.net/publication/328454883_A_Benchmark_for_Lidar_Sensors_in_Fog_Is_Detection_Breaking_Down

 

 M. Bijelic, T. Gruber, W. Ritter. (2018). Benchmarking Image Sensors Under Adverse Weather Conditions for Autonomous Driving
https://ieeexplore.ieee.org/document/8500659

 

N. Scheiner, N. Appenrodt, J. Dickmann, and B. Sick. (2018). Radar-based Feature Design and Multiclass Classification for Road User Recognition.
https://www.researchgate.net/publication/328458670_Radar-based_Feature_Design_and_Multiclass_Classification_for_Road_User_Recognition

 

M. Bijelic, C. Munch und e. Al. (2018). Robustness Against Unknown Noise for Raw Data Fusing Neural Networks.
https://www.researchgate.net/publication/329620148_Robustness_Against_Unknown_Noise_for_Raw_Data_Fusing_Neural_Networks

 

T. Gruber, M. Kokhova, W. Ritter, N. Haala, K. Dietmayer. (2018). Learning super-resolved Depth from Active Gated Imaging.
https://www.researchgate.net/publication/329619351_Learning_Super-resolved_Depth_from_Active_Gated_Imaging

 

Henna Paaso. (2018). Direction of Arrival Estimation Algorithms for Leaky-Wave Antennas and Antenna Arrays
http://jultika.oulu.fi/Record/isbn978-952-62-2074-1

 

K. Dietmayer, A. Pfeuffer. (2018). Optimal Sensor Data Fusion Architecture for Object Detection in Adverse Weather Conditions.
https://arxiv.org/abs/1807.02323

 

Alexander Hunt. (2018). Vision Enhancement for Autonomous Driving under Adverse Weather Conditions using Generative Adversial Nets.
http://dbis.eprints.uni-ulm.de/1677/

 

2019

 

N. Scheiner, N. Appenrodt, J. Dickmann, B. Sick. (2019). Automated Ground Truth Estimation of Vulnerable Road Users in Automotive Radar Data Using GNSS.
https://arxiv.org/abs/1905.11219

 

N. Scheiner, N. Appenrodt, J. Dickmann, B. Sick. (2019). Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles.
https://arxiv.org/abs/1905.11703

 

Nicolas Scheiner, Stefan Haag, Nils Appenrodt, Bharanidhar Duraisamy, Jürgen Dickmann, Martin Fritzsche, Bernhard Sick. (2019). Automated Ground Truth Estimation For Automotive Radar Tracking Applications With Portable GNSS And IMU Devices.
https://arxiv.org/abs/1905.11987

 

Andreas Pfeuffer, Klaus Dietmayer. (2019). Robust Semantic Segmentation in Adverse Weather Conditions by means of Sensor Data Fusion.
https://arxiv.org/abs/1905.10117

 

Maria Jokela, Matti Kutila, Pasi Pyykönen. (2019). Testing and Validation of Automotive Point-Cloud Sensors in Adverse Weather Conditions.
https://doi.org/10.3390/app9112341

 

 

Presentations

Here we publish public presentations held by the DENSE partners.

 

Presentation in the Pecha Kucha format by Dr. Werner Ritter at the congress "ECSEL in Germany"
Dresden, Germany, 05. + 06.09.2018: A new sensor suite for automated driving in all weather conditions.