Robust Automated Driving in Extreme Weather

Información del proyecto

ROADVIEW

Identificador del acuerdo de subvención: 101069576

DOI

10.3030/101069576

Fecha de la firma de la CE 9 Junio 2022

Fecha de inicio 1 Septiembre 2022

Fecha de finalización 31 Agosto 2026

Financiado con arreglo a

• Climate, Energy and Mobility

Coste total

€ 8 014 105,55

Aportación de la UE

€ 6 652 915,80

Coordinado por

HOGSKOLAN I HALMSTAD
Sweden

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

Documents, reports (11)

Test plan regarding the most appropriate test method

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

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

Interim impact report

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

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

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

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

Vehicle dynamics modelling methodology

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

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

SW on Collaborative Perception Solutions - First report

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

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

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

Data sets, microdata, etc (1)

Reference dataset of measured weather characteristics

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

Websites, patent fillings, videos etc. (1)

ROADVIEW website

ROADVIEW website with dedicated areas for different stakeholder groups.

Demonstrators, pilots, prototypes (1)

ROADVIEW Demonstration 1

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

Other (1)

Library of validated statistical noise models

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.

Publicaciones

Conference proceedings (8)

Synthetic Extreme Weather for AI training: Concept and Validation

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

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

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

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

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

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

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

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

Peer reviewed articles (10)

A Noise Analysis of 4D RADAR: Robust Sensing for Automotive?

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

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

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

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

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

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

Depth- and semantics-aware multi-modal domain translation: Generating 3D panoramic color images from LiDAR point clouds

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

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

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

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

Other (6)

3D-OutDet: A Fast and Memory Efficient Outlier Detector for 3D LiDAR Point Clouds in Adverse Weather

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

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

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

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

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

A noise analysis of 4D RADAR: robust sensing for automotive?

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

Thesis and dissertations (1)

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

Book chapters (2)

Road Grip Uncertainty Estimation Through Surface State Segmentation

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

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