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CORDIS - Risultati della ricerca dell’UE
CORDIS

Robust Automated Driving in Extreme Weather

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

Test plan regarding the most appropriate test method (si apre in una nuova finestra)

Report defining how to test single components and integrated automated vehicle systems in virtual and real domains. It will consider specific uses-cases under extreme weather conditions.

Initial readiness assessment of specific datasets (si apre in una nuova finestra)

Report on the quantitative evaluation of the ROADVIEW DRL method on public datasets using a limited feature size.

Interim impact report (si apre in una nuova finestra)

Interim impact report on communication and dissemination activities, including those at the international level. An update of the DCP plan will be considered.

SW on Improved Localization Using High-density Map Updating - First report (si apre in una nuova finestra)

A first report related SW of a compressed HD map representation that can cope with the seasonal changes and can be kept updated while excluding the dynamic objects.

SW on Adaptive Sensor Fusion and Perception Solutions - First report (si apre in una nuova finestra)

A first report on algorithms and SW developed to detect weather-related physical conditions (e.g., heavy rain, fog, slush and snow), free space detection, object detection and tracking, and adaptive sensor fusion.

Stakeholder report and strategy (si apre in una nuova finestra)

Stakeholder report and strategy including detailed stakeholder mapping and strategies to reach key target audiences.

Vehicle dynamics modelling methodology (si apre in una nuova finestra)

A public report will be published along with any relevant publications on vehicle dynamics modelling methodologies. In addition, a representative library of vehicle dynamics models for a selection of vehicles available within the consortium will be delivered.

Definition of the complex environment conditions (si apre in una nuova finestra)

An extended ODD taxonomy will be reported considering harsh weather conditions and complex urban/rural environments.

SW on Collaborative Perception Solutions - First report (si apre in una nuova finestra)

A first report on improving vehicle perception with sharing information provided by sensors from other connected vehicles or VRUs and roadside infrastructure.

Use cases and scenarios (si apre in una nuova finestra)

A set of use cases with multiple scenarios will be reported based on the ODD taxonomy defined in Task 2.1.

Plan for the dissemination and communication activities (si apre in una nuova finestra)

Plan for the dissemination and communication activities including defined strategy, tools, channels and (international) activities.

Reference dataset of measured weather characteristics (si apre in una nuova finestra)

Reference datasets for the controlled environment and real-world site weather characteristics will be delivered, with a detailed report on data collection methodology.

ROADVIEW website (si apre in una nuova finestra)

ROADVIEW website with dedicated areas for different stakeholder groups.

ROADVIEW Demonstration 1 (si apre in una nuova finestra)

Report on the demonstration of early ROADVIEW system components implementations to be used in the subsequent demonstrations.

Library of validated statistical noise models (si apre in una nuova finestra)

A library of validated statistical noise models for the selected perception sensor technologies will be delivered with a detailed report on implementation and validation methods used.

Pubblicazioni

Synthetic Extreme Weather for AI training: Concept and Validation (si apre in una nuova finestra)

Autori: Letícia Cristófoli Duarte Silva, Maikol Funk Drechsler, Yuri Poledna, Werner Huber, Thiago Antonio Fiorentin
Pubblicato in: 2023 Third International Conference on Digital Data Processing (DDP), 2024, ISSN 2473-2001
Editore: IEEExplore
DOI: 10.1109/DDP60485.2023.00044

The effect of camera data degradation factors on panoptic segmentation for automated driving

Autori: Wang, Yiting, Zhao, Haonan, Debattista, Kurt and Donzella, Valentina
Pubblicato in: 26th IEEE International Conference on Intelligent Transportation Systems (ITSC 2023), 2023
Editore: WRAP Warwick

3D-OutDet: A Fast and Memory Efficient Outlier Detector for 3D LiDAR Point Clouds in Adverse Weather (si apre in una nuova finestra)

Autori: Abu Mohammed Raisuddin, Tiago Cortinhal, Jesper Holmblad, Eren Erdal Aksoy
Pubblicato in: TechRxiv, 2023
Editore: TechRxiv
DOI: 10.36227/techrxiv.24297166.v1

Semantics-aware LiDAR-Only Pseudo Point Cloud Generation for 3D Object Detection (si apre in una nuova finestra)

Autori: Tiago Cortinhal, Idriss Gouigah, Eren Erdal Aksoy
Pubblicato in: arXiv, 2023
Editore: arXiv
DOI: 10.48550/arXiv.2309.08932

Raw camera data object detectors: an optimisation for automotive processing and transmission (si apre in una nuova finestra)

Autori: Pak Hung Chan, Chuheng Wei, Anthony Huggett, Valentina Donzella
Pubblicato in: TechRxiv, 2023
Editore: TechRxiv
DOI: 10.36227/techrxiv.23807499.v1

Correlating traditional image quality metrics and DNN-based object detection: a case study with compressed camera data (si apre in una nuova finestra)

Autori: Daniel Gummadi, Pak Hung Chan, Hetian Wang, Valentina Donzella
Pubblicato in: TechRxiv, 2023
Editore: TechRxiv
DOI: 10.36227/techrxiv.24566371.v1

Analysis of Faster R-CNN network prediction in the presence of lens occlusion and video compression (si apre in una nuova finestra)

Autori: Gabriele Baris, Boda Li, Pak Hung Chan, Carlo Alberto Avizzano, Valentina Donzella
Pubblicato in: TechRxiv, 2023
Editore: TechRxiv
DOI: 10.36227/techrxiv.23047412.v1

A noise analysis of 4D RADAR: robust sensing for automotive? (si apre in una nuova finestra)

Autori: Pak Hung Chan, Sepeedeh Shahbeigi Roudposhti, Xinyi Ye, Valentina Donzella
Pubblicato in: TechRxiv, 2023
Editore: TechRxiv
DOI: 10.36227/techrxiv.24517249.v1

Pixelwise Road Surface Slipperiness Estimation for Autonomous Driving with Weakly Supervised Learning

Autori: Julius Pesonen
Pubblicato in: Machine Learning, Data Science and Artificial Intelligence, 2023
Editore: Aaltodoc publication archive

A Novel Score-based LiDAR Point Cloud degradation Analysis Method (si apre in una nuova finestra)

Autori: Sepeedeh Shahbeigi, Honahan Robinson, Valentina Donzella
Pubblicato in: IEEE Transactions and Journals, 2024, ISSN 1803-7232
Editore: IEEE Xplore
DOI: 10.1109/ACCESS.2024.3359300

Depth- and semantics-aware multi-modal domain translation: Generating 3D panoramic color images from LiDAR point clouds (si apre in una nuova finestra)

Autori: Tiago Cortinhal, Eren Erdal Aksoy
Pubblicato in: Robotics and Autonomous Systems, 2023, ISSN 0921-8890
Editore: Science Direct | Elsevier
DOI: 10.1016/j.robot.2023.104583

SWEET: A Realistic Multiwavelength 3D Simulator for Automotive Perceptive Sensors in Foggy Conditions (si apre in una nuova finestra)

Autori: Amine Ben-Daoued; Pierre Duthon; Frédéric Bernardin
Pubblicato in: Journal of Imaging, 2023, ISSN 2313-433X
Editore: MDPI
DOI: 10.3390/jimaging9020054

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