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CORDIS - Resultados de investigaciones de la UE
CORDIS

Robust Automated Driving in Extreme Weather

CORDIS proporciona enlaces a los documentos públicos y las publicaciones de los proyectos de los programas marco HORIZONTE.

Los enlaces a los documentos y las publicaciones de los proyectos del Séptimo Programa Marco, así como los enlaces a algunos tipos de resultados específicos, como conjuntos de datos y «software», se obtienen dinámicamente de OpenAIRE .

Resultado final

SW on Improved Localization Using High-density Map Updating - Final report (se abrirá en una nueva ventana)

A final 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.

Final KPI specifications (se abrirá en una nueva ventana)

Update on the KPI specifications.

Evaluation of sensors in-situ (se abrirá en una nueva ventana)

Report on robustness of shielded sensors based on data while driving in real-world harsh weather conditions, including possible mitigations such as design choices, filtering, and calibration.

Test plan regarding the most appropriate test method (se abrirá en una nueva ventana)

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.

Report on data privacy and storage (se abrirá en una nueva ventana)

Data privacy and storage of the logged data.

Final readiness assessment of specific datasets (se abrirá en una nueva ventana)

Final report on the quantitative evaluation of the ROADVIEW DRL method on public datasets and on the ROADVIEW dataset using enlarged feature sets.

Initial readiness assessment of specific datasets (se abrirá en una nueva ventana)

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

Interim impact report (se abrirá en una nueva ventana)

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 (se abrirá en una nueva ventana)

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 Collaborative Perception Solutions - Final report (se abrirá en una nueva ventana)

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

SW on Adaptive Sensor Fusion and Perception Solutions - Final report (se abrirá en una nueva ventana)

A final 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.

SW on Adaptive Sensor Fusion and Perception Solutions - First report (se abrirá en una nueva ventana)

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 (se abrirá en una nueva ventana)

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

Environmental and weather condition state estimates (se abrirá en una nueva ventana)

Report on the online state estimation method providing the reliable visibility range and weather conditions using camera, LiDAR, and RADAR sensor data.

Vehicle dynamics modelling methodology (se abrirá en una nueva ventana)

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 (se abrirá en una nueva ventana)

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

XAI design of perception solutions (se abrirá en una nueva ventana)

Explainability requirement specifications, report and implementation of XAI design for AI models developed in Tasks 5.2 and 5.3.

SW on Collaborative Perception Solutions - First report (se abrirá en una nueva ventana)

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

Report on data annotation (se abrirá en una nueva ventana)

Report on the categorisation and labelling of data.

Use cases and scenarios (se abrirá en una nueva ventana)

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 (se abrirá en una nueva ventana)

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

Initial KPI specifications (se abrirá en una nueva ventana)

Report on the KPI specifications.

Relevant metrics to assert the validity of the simulation-assisted test methods (se abrirá en una nueva ventana)

This report describes the relevant metrics for the validation of simulation-assisted methods in comparison to controlled environment/proving ground reference values.

Library of internal sensor noise models and compound noise modelling methodology (se abrirá en una nueva ventana)

A library of internal sensor noise models for the perception sensors selected for the project will be delivered. This will be accompanied by a report on noise modelling, the methodologies to combine multiple noise factor effects on the sensor outputs for different sensor technologies, and any significant corner cases or scenarios to test where compound noise could create significant degradation of sensor performance.

Library of validated physics-based parameterised noise models (se abrirá en una nueva ventana)

A library of validated physics-based parameterised noise models for the selected perception sensor technologies with detailed report on implementation, validation methods and comparison of the generated models based on the agreed evaluation metrics.

Library of validated statistical noise models (se abrirá en una nueva ventana)

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.

GitHub release of ROADVIEW Readiness levels (se abrirá en una nueva ventana)

Release of the source code for DRL.

Reference dataset of measured weather characteristics (se abrirá en una nueva ventana)

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 (se abrirá en una nueva ventana)

ROADVIEW website with dedicated areas for different stakeholder groups.

ROADVIEW Demonstration 1 (se abrirá en una nueva ventana)

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

Publicaciones

Synthetic Extreme Weather for AI training: Concept and Validation (se abrirá en una nueva ventana)

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

Creation of digital models for accelerated and reliable testing of automated systems in adverse weather (se abrirá en una nueva ventana)

Autores: Tuomas Herranen, Erik Henriksson, Pak Hung Chan, Yuri Poledna, Pierre Duthon, Amine Ben-Daoued, Maikol Drechsler, Valentina Donzella
Publicado en: Autonomous Systems for Security and Defence, 2024
Editor: SPIE
DOI: 10.1117/12.3031473

Semantics-aware LiDAR-Only Pseudo Point Cloud Generation for 3D Object Detection (se abrirá en una nueva ventana)

Autores: Tiago Cortinhal, Idriss Gouigah, Eren Erdal Aksoy
Publicado en: 2024 IEEE Intelligent Vehicles Symposium (IV), 2024
Editor: IEEE
DOI: 10.1109/IV55156.2024.10588782

Vehicle Dynamics Parameter Estimation Methodology for Virtual Automated Driving Testing (se abrirá en una nueva ventana)

Autores: Maikol Funk Drechsler, Yuri Poledna, Mattias Hjort, Sogol Kharrazi, Werner Huber
Publicado en: 2024 IEEE International Automated Vehicle Validation Conference (IAVVC), 2024
Editor: IEEE
DOI: 10.1109/IAVVC63304.2024.10786416

REHEARSE: adveRse wEatHEr datAset for sensoRy noiSe modEls (se abrirá en una nueva ventana)

Autores: Yuri Poledna, Maikol Funk Drechsler, Valentina Donzella, Pak Hung Chan, Pierre Duthon, Werner Huber
Publicado en: 2024 IEEE Intelligent Vehicles Symposium (IV), 2024
Editor: IEEE
DOI: 10.1109/IV55156.2024.10588491

3D-OutDet: A Fast and Memory Efficient Outlier Detector for 3D LiDAR Point Clouds in Adverse Weather (se abrirá en una nueva ventana)

Autores: Raisuddin, Abu Mohammed; Cortinhal, Tiago; Holmblad, Jesper; Aksoy, Eren Erdal
Publicado en: 2024 IEEE Intelligent Vehicles Symposium (IV), 2024
Editor: IEEE
DOI: 10.36227/TECHRXIV.24297166.V1

Simulation numérique de capteurs perceptifs du véhicule autonome sous conditions météorologiques dégradées

Autores: Amine Ben-Daoued; Frédéric Bernardin; Pierre Duthon
Publicado en: ATEC ITS Congress, 2023
Editor: HAL

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

Autores: Wang, Yiting, Zhao, Haonan, Debattista, Kurt and Donzella, Valentina
Publicado en: 26th IEEE International Conference on Intelligent Transportation Systems (ITSC 2023), 2023
Editor: WRAP Warwick

Parametric Physics-Based Snow Model for Automotive Cameras (se abrirá en una nueva ventana)

Autores: Pak Hung Chan, Kurt Debattista, Valentina Donzella
Publicado en: 2024 IEEE SENSORS, 2024
Editor: IEEE
DOI: 10.1109/SENSORS60989.2024.10784836

A Noise Analysis of 4D RADAR: Robust Sensing for Automotive? (se abrirá en una nueva ventana)

Autores: Pak Hung Chan; Sepeedeh Shahbeigi Roudposhti; Xinyi Ye; Valentina Donzella
Publicado en: IEEE Sensors Journal, 2025, ISSN 1558-1748
Editor: IEEE
DOI: 10.36227/TECHRXIV.24517249

Raw Camera Data Object Detectors: An Optimisation for Automotive Video Processing and Transmission (se abrirá en una nueva ventana)

Autores: Pak Hung Chan, Chuheng Wei, Anthony Huggett, Valentina Donzella
Publicado en: IEEE Access, Edición 13, 2025, ISSN 2169-3536
Editor: IEEE
DOI: 10.36227/TECHRXIV.23807499.V1

The inconvenient truth of ground truth errors in automotive datasets and DNN-based detection (se abrirá en una nueva ventana)

Autores: Chan, Pak Hung; Li, Boda; Baris, Gabriele; Sadiq, Qasim; Donzella, Valentina
Publicado en: Data-Centric Engineering, 2024, ISSN 2632-6736
Editor: Cambridge University Press
DOI: 10.36227/TECHRXIV.170630689.97107789/V1

A Novel Score-based LiDAR Point Cloud degradation Analysis Method (se abrirá en una nueva ventana)

Autores: Sepeedeh Shahbeigi, Honahan Robinson, Valentina Donzella
Publicado en: IEEE Transactions and Journals, 2024, ISSN 1803-7232
Editor: IEEE Xplore
DOI: 10.1109/ACCESS.2024.3359300

Automotive DNN-Based Object Detection in the Presence of Lens Obstruction and Video Compression (se abrirá en una nueva ventana)

Autores: Gabriele Baris, Boda Li, Pak Hung Chan, Carlo Alberto Avizzano, Valentina Donzella
Publicado en: IEEE Access, Edición 13, 2025, ISSN 2169-3536
Editor: Institute of Electrical and Electronics Engineers (IEEE)
DOI: 10.1109/ACCESS.2025.3544773

A Comparative Review of the SWEET Simulator: Theoretical Verification Against Other Simulators (se abrirá en una nueva ventana)

Autores: Amine Ben-Daoued; Frédéric Bernardin; Pierre Duthon
Publicado en: Journal of Imaging, 2024, ISSN 3064-6936
Editor: MDPI
DOI: 10.3390/JIMAGING10120306

LiDAR De-Snow Score (DSS): Combining Quality and Perception Metrics for Optimized De-Noising (se abrirá en una nueva ventana)

Autores: Valentina Donzella, Pak Hung Chan, Daniel Gummadi, Abu Mohammed Raisuddin, Eren Erdal Aksoy
Publicado en: IEEE Sensors Journal, Edición 25, 2025, ISSN 1530-437X
Editor: Institute of Electrical and Electronics Engineers (IEEE)
DOI: 10.1109/JSEN.2025.3570478

Depth- and semantics-aware multi-modal domain translation: Generating 3D panoramic color images from LiDAR point clouds (se abrirá en una nueva ventana)

Autores: Tiago Cortinhal, Eren Erdal Aksoy
Publicado en: Robotics and Autonomous Systems, 2023, ISSN 0921-8890
Editor: Science Direct | Elsevier
DOI: 10.1016/j.robot.2023.104583

SWEET: A Realistic Multiwavelength 3D Simulator for Automotive Perceptive Sensors in Foggy Conditions (se abrirá en una nueva ventana)

Autores: Amine Ben-Daoued; Pierre Duthon; Frédéric Bernardin
Publicado en: Journal of Imaging, 2023, ISSN 2313-433X
Editor: MDPI
DOI: 10.3390/jimaging9020054

Influence of AVC and HEVC Compression on Detection of Vehicles Through Faster R-CNN (se abrirá en una nueva ventana)

Autores: Pak Hung Chan, Anthony Huggett, Georgina Souvalioti, Paul Jennings, Valentina Donzella
Publicado en: IEEE Transactions on Intelligent Transportation Systems, Edición 25, 2025, ISSN 1524-9050
Editor: Institute of Electrical and Electronics Engineers (IEEE)
DOI: 10.36227/TECHRXIV.19808566

From operational design domain to test cases: A methodology to include harsh weather (se abrirá en una nueva ventana)

Autores: Fredrik Warg; Valentina Donzella; Pak Hung Chan; Jonathan Robinson; Yuri Poledna; Sebastien Liandrat; Umut Cihan; Maytheewat Aramrattana; Graham Lee; Eren Erdal Aksoy
Publicado en: Open Research Europe, 2024, ISSN 2732-5121
Editor: European Commission
DOI: 10.12688/OPENRESEUROPE.18592.1

3D-OutDet: A Fast and Memory Efficient Outlier Detector for 3D LiDAR Point Clouds in Adverse Weather (se abrirá en una nueva ventana)

Autores: Abu Mohammed Raisuddin, Tiago Cortinhal, Jesper Holmblad, Eren Erdal Aksoy
Publicado en: TechRxiv, 2023
Editor: TechRxiv
DOI: 10.36227/techrxiv.24297166.v1

Semantics-aware LiDAR-Only Pseudo Point Cloud Generation for 3D Object Detection (se abrirá en una nueva ventana)

Autores: Tiago Cortinhal, Idriss Gouigah, Eren Erdal Aksoy
Publicado en: arXiv, 2023
Editor: arXiv
DOI: 10.48550/arXiv.2309.08932

Raw camera data object detectors: an optimisation for automotive processing and transmission (se abrirá en una nueva ventana)

Autores: Pak Hung Chan, Chuheng Wei, Anthony Huggett, Valentina Donzella
Publicado en: TechRxiv, 2023
Editor: TechRxiv
DOI: 10.36227/techrxiv.23807499.v1

Correlating traditional image quality metrics and DNN-based object detection: a case study with compressed camera data (se abrirá en una nueva ventana)

Autores: Daniel Gummadi, Pak Hung Chan, Hetian Wang, Valentina Donzella
Publicado en: TechRxiv, 2023
Editor: TechRxiv
DOI: 10.36227/techrxiv.24566371.v1

Analysis of Faster R-CNN network prediction in the presence of lens occlusion and video compression (se abrirá en una nueva ventana)

Autores: Gabriele Baris, Boda Li, Pak Hung Chan, Carlo Alberto Avizzano, Valentina Donzella
Publicado en: TechRxiv, 2023
Editor: TechRxiv
DOI: 10.36227/techrxiv.23047412.v1

An Experimental Study on ObjectTracking

Autores: Mahmoud Alshaikh
Publicado en: 2025
Editor: n/a

A noise analysis of 4D RADAR: robust sensing for automotive? (se abrirá en una nueva ventana)

Autores: Pak Hung Chan, Sepeedeh Shahbeigi Roudposhti, Xinyi Ye, Valentina Donzella
Publicado en: TechRxiv, 2023
Editor: TechRxiv
DOI: 10.36227/techrxiv.24517249.v1

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

Autores: Julius Pesonen
Publicado en: Machine Learning, Data Science and Artificial Intelligence, 2023
Editor: Aaltodoc publication archive

Road Grip Uncertainty Estimation Through Surface State Segmentation (se abrirá en una nueva ventana)

Autores: Jyri Maanpää, Julius Pesonen, Iaroslav Melekhov, Heikki Hyyti, Juha Hyyppä
Publicado en: Lecture Notes in Computer Science, Image Analysis, 2025
Editor: Springer Nature Switzerland
DOI: 10.1007/978-3-031-95911-0_17

Dense Road Surface Grip Map Prediction from Multimodal Image Data (se abrirá en una nueva ventana)

Autores: Jyri Maanpää, Julius Pesonen, Heikki Hyyti, Iaroslav Melekhov, Juho Kannala, Petri Manninen, Antero Kukko, Juha Hyyppä
Publicado en: Lecture Notes in Computer Science, Pattern Recognition, 2024
Editor: Springer Nature Switzerland
DOI: 10.1007/978-3-031-78447-7_26

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