Robust Automated Driving in Extreme Weather

Informations projet

ROADVIEW

N° de convention de subvention: 101069576

DOI

10.3030/101069576

Projet clôturé

Date de signature de la CE 9 Juin 2022

Date de début 1 Septembre 2022

Date de fin 31 Août 2026

Financé au titre de

• Climate, Energy and Mobility

Coût total

€ 8 014 105,55

Contribution de l’UE

€ 6 652 915,80

Coordonné par

HOGSKOLAN I HALMSTAD
Sweden

CORDIS fournit des liens vers les livrables publics et les publications des projets HORIZON.

Les liens vers les livrables et les publications des projets du 7e PC, ainsi que les liens vers certains types de résultats spécifiques tels que les jeux de données et les logiciels, sont récupérés dynamiquement sur OpenAIRE .

Livrables

Documents, reports (29)

SW on Improved Localization Using High-density Map Updating - Final report

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.

Validation of the simulation-assisted test methods and proposed test plan

This report describes to what extent simulation can be used to test automated vehicle systems.

Final KPI specifications

Update on the KPI specifications.

Evaluation of sensors in-situ

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

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 performance assessment according to KPIs

Final report on the system performance based on the KPIs.

Report on data privacy and storage

Data privacy and storage of the logged data.

Final readiness assessment of specific datasets

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

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 Collaborative Perception Solutions - Final report

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

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

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.

Environmental and weather condition state estimates

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

Report evaluating compliance with OpenX standards

A report evaluating compliance with OpenX standards will be delivered. This will include recommendations to modify ROADVIEW models to enhance interoperability and advance the OpenX standards.

Final impact report

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

Report of final ROADVIEW Summit

Final ROADVIEW Summit event open to externals to present results and outcomes.

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.

XAI design of perception solutions

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

Report on the ROADVIEW standards

The fundamental standards for the ROADVIEW perception and control systems will be reported by referring to different levels of autonomy in different weather and traffic conditions.

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.

Report on data annotation

Report on the categorisation and labelling of data.

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.

Initial KPI specifications

Report on the KPI specifications.

Relevant metrics to assert the validity of the simulation-assisted test methods

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

Other (3)

Library of internal sensor noise models and compound noise modelling methodology

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

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

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.

Data sets, microdata, etc (2)

GitHub release of ROADVIEW Readiness levels

Release of the source code for DRL.

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.

Publications

Conference proceedings (21)

Synthetic Extreme Weather for AI training: Concept and Validation

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

Quantifying Blockage Detection in 4D Automotive Radars

Auteurs: Henrik Toss, Martin Sanfridson
Publié dans: 2026 20th European Conference on Antennas and Propagation (EuCAP), 2026
Éditeur: IEEE
DOI: 10.23919/EUCAP68105.2026.11612756

Creation of digital models for accelerated and reliable testing of automated systems in adverse weather

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

Validating Camera Sensor Models for Virtual Testing of Vision Systems in Automated Driving

Auteurs: Fabian Ulreich, Maikol Funk Drechsler, Yuri Poledna, Pak Hung Chan, Tuomas Herranen, Martin Ebert, André Kaup, Werner Huber
Publié dans: 2025 IEEE International Conference on Vehicular Electronics and Safety (ICVES), 2026
Éditeur: IEEE
DOI: 10.1109/ICVES65691.2025.11376043

Mitigating Systematic Errors in Roadside Camera-Based V2X Perception

Auteurs: Maxime Cancouet, Hervé Ruellan, Romain Bellessort, Isabelle Morvan, Eric Nassor, Heikki Hyyti, Jean-Marie Bonnin
Publié dans: 2026 IEEE Intelligent Vehicles Symposium (IV), 2026
Éditeur: IEEE
DOI: 10.5281/ZENODO.18935750

Semantics-aware LiDAR-Only Pseudo Point Cloud Generation for 3D Object Detection

Auteurs: Tiago Cortinhal, Idriss Gouigah, Eren Erdal Aksoy
Publié dans: 2024 IEEE Intelligent Vehicles Symposium (IV), 2024
Éditeur: IEEE
DOI: 10.1109/IV55156.2024.10588782

LRC-WeatherNet: LiDAR, RADAR, and Camera Fusion Network for Real-time Weather-type Classification in Autonomous Driving

Auteurs: Nour Alhuda Albashir, Lars Pernickel, Danial Hamoud, Idriss Gouigah, Eren Erdal Aksoy
Publié dans: 2026 IEEE Intelligent Vehicles Symposium (IV), 2026
Éditeur: IEEE
DOI: 10.1109/IV66570.2026.11624018

PAVIN Extreme Weather Facility: A Cutting-Edge Testing Environment for Agricultural Robotics

Auteurs: Pierre Duthon, Sébastien Liandrat, Amine Ben-Daoued, Jean-Luc Bicard, Mickaël Ferreira-Fernandes, David Bicard, Frédéric Bernardin
Publié dans: Agritech Day - 8th Edition 2025, 2025
Éditeur: Axema

Vehicle Dynamics Parameter Estimation Methodology for Virtual Automated Driving Testing

Auteurs: Maikol Funk Drechsler, Yuri Poledna, Mattias Hjort, Sogol Kharrazi, Werner Huber
Publié dans: 2024 IEEE International Automated Vehicle Validation Conference (IAVVC), 2024
Éditeur: IEEE
DOI: 10.1109/IAVVC63304.2024.10786416

REHEARSE: adveRse wEatHEr datAset for sensoRy noiSe modEls

Auteurs: Yuri Poledna, Maikol Funk Drechsler, Valentina Donzella, Pak Hung Chan, Pierre Duthon, Werner Huber
Publié dans: 2024 IEEE Intelligent Vehicles Symposium (IV), 2024
Éditeur: IEEE
DOI: 10.1109/IV55156.2024.10588491

Ensuring Human Safety and Accountability Through Data Management in Automated Driving

Auteurs: Ian Marsh, Thanh Hai Bui
Publié dans: 2026 1st International Conference on Human Centric Artificial Intelligence (ICHCAI), 2026
Éditeur: IEEE
DOI: 10.1109/ICHCAI70183.2026.11607538

Darwick: A Paired Dataset in Low-Light Driving Scenarios for Advanced Perceptual Enhancement and Benchmarking Assessment

Auteurs: Zixiang Wei, Yunruo Jiang, Pak Hung Chan, Yiting Wang, Yuri Poledna, Valentina Donzella
Publié dans: 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), 2026
Éditeur: IEEE
DOI: 10.1109/ITSC58415.2024.11414063

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

Auteurs: Raisuddin, Abu Mohammed; Cortinhal, Tiago; Holmblad, Jesper; Aksoy, Eren Erdal
Publié dans: 2024 IEEE Intelligent Vehicles Symposium (IV), 2024
Éditeur: IEEE
DOI: 10.36227/TECHRXIV.24297166.V1

Rain Reflectivity Distribution and Detectability in Automotive Radar

Auteurs: Henrik Toss, Kristian Karlsson, Pierre Duthon, Yuri Poledna
Publié dans: 2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), 2026
Éditeur: IEEE
DOI: 10.1109/ITSC60802.2025.11423144

Data Readiness Levels for Automated Driving

Auteurs: Ian Marsh, Victor Stenmark, Yuri Poledna, Heikki Hyyti, Martin Sanfridson, Eren Aksoy
Publié dans: 2026 IEEE Intelligent Vehicles Symposium (IV), 2026
Éditeur: IEEE
DOI: 10.1109/IV66570.2026.11623873

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

Auteurs: Amine Ben-Daoued; Frédéric Bernardin; Pierre Duthon
Publié dans: ATEC ITS Congress, 2023
Éditeur: HAL

Stylistic-STORM (ST-STORM): Self-supervised Spectral Disentanglement Using Adversarial Learning and JEPA for Weather Analysis

Auteurs: Hamed Ouattara, Pierre Duthon, Pascal Houssam Salmane, Frédéric Bernardin, Omar Ait Aider
Publié dans: Lecture Notes in Computer Science, Pattern Recognition, 2026
Éditeur: Springer Nature Switzerland
DOI: 10.1007/978-3-032-31673-8_1

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

Auteurs: Wang, Yiting, Zhao, Haonan, Debattista, Kurt and Donzella, Valentina
Publié dans: 26th IEEE International Conference on Intelligent Transportation Systems (ITSC 2023), 2023
Éditeur: WRAP Warwick

Accelerating the Approval of Automated Driving Vehicles through standardized XiL test environments

Auteurs: Maikol Funk Drechsler, Christoph Sell, Yuri Poledna, Werner Huber
Publié dans: 2025 IEEE International Conference on Vehicular Electronics and Safety (ICVES), 2026
Éditeur: IEEE
DOI: 10.1109/ICVES65691.2025.11376566

Compressing Noisy Bayer Images: Impact on Object Detection in Automotive

Auteurs: Hetian Wang, Pak Hung Chan, Valentina Donzella
Publié dans: 2025 IEEE International Conference on Vehicular Electronics and Safety (ICVES), 2026
Éditeur: IEEE
DOI: 10.1109/ICVES65691.2025.11376319

Parametric Physics-Based Snow Model for Automotive Cameras

Auteurs: Pak Hung Chan, Kurt Debattista, Valentina Donzella
Publié dans: 2024 IEEE SENSORS, 2024
Éditeur: IEEE
DOI: 10.1109/SENSORS60989.2024.10784836

Peer reviewed articles (20)

Rethinking probabilistic learning for counterfactual low-light image enhancement in robust engineering vision systems

Auteurs: Zixiang Wei, Yiting Wang, Kurt Debattista, Valentina Donzella
Publié dans: Knowledge-Based Systems, Numéro 340, 2026, ISSN 0950-7051
Éditeur: Elsevier BV
DOI: 10.1016/J.KNOSYS.2026.115666

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

Auteurs: Pak Hung Chan; Sepeedeh Shahbeigi Roudposhti; Xinyi Ye; Valentina Donzella
Publié dans: IEEE Sensors Journal, 2025, ISSN 1558-1748
Éditeur: IEEE
DOI: 10.36227/TECHRXIV.24517249

Raw Camera Data Object Detectors: An Optimisation for Automotive Video Processing and Transmission

Auteurs: Pak Hung Chan, Chuheng Wei, Anthony Huggett, Valentina Donzella
Publié dans: IEEE Access, Numéro 13, 2025, ISSN 2169-3536
Éditeur: IEEE
DOI: 10.36227/TECHRXIV.23807499.V1

Data-Driven Headlight Flare Model for Automotive Cameras

Auteurs: Boda Li, Hetian Wang, Yiting Wang, Pak Hung Chan, Darryl Perks, Valentina Donzella
Publié dans: IEEE Open Journal of Vehicular Technology, Numéro 6, 2025, ISSN 2644-1330
Éditeur: Institute of Electrical and Electronics Engineers (IEEE)
DOI: 10.1109/OJVT.2025.3601400

The inconvenient truth of ground truth errors in automotive datasets and DNN-based detection

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

A Novel Score-based LiDAR Point Cloud degradation Analysis Method

Auteurs: Sepeedeh Shahbeigi, Honahan Robinson, Valentina Donzella
Publié dans: IEEE Transactions and Journals, 2024, ISSN 1803-7232
Éditeur: IEEE Xplore
DOI: 10.1109/ACCESS.2024.3359300

Snowy Scenes: A Multimodal Multitask Dataset Toward Snow-Tonomous Vehicles

Auteurs: Thien-Thu Ngo, Abu Mohammed Raisuddin, Idriss Gouigah, Paula Litkey, Petri Manninen, Heikki Hyyti, Jyri Maanpaa, Tuomas Herranen, Vaisanen Sofie, Kalle Niemi, Lars Pernickel, Eren Erdal Aksoy
Publié dans: IEEE Robotics and Automation Letters, Numéro 11, 2026, ISSN 2377-3766
Éditeur: Institute of Electrical and Electronics Engineers (IEEE)
DOI: 10.1109/LRA.2026.3717187

Automotive DNN-Based Object Detection in the Presence of Lens Obstruction and Video Compression

Auteurs: Gabriele Baris, Boda Li, Pak Hung Chan, Carlo Alberto Avizzano, Valentina Donzella
Publié dans: IEEE Access, Numéro 13, 2025, ISSN 2169-3536
Éditeur: Institute of Electrical and Electronics Engineers (IEEE)
DOI: 10.1109/ACCESS.2025.3544773

Fast Point Ranking - Robust Cloud Voxelization and Denoising for Lidar Odometry and Mapping in Adverse Weather Conditions

Auteurs: Eugeniu Vezeteu, Heikki Hyyti, Ville Kyrki, Juha Hyyppä
Publié dans: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Numéro X-2/W2-2025, 2025, ISSN 2194-9050
Éditeur: Copernicus GmbH
DOI: 10.5194/ISPRS-ANNALS-X-2-W2-2025-199-2025

A Comparative Review of the SWEET Simulator: Theoretical Verification Against Other Simulators

Auteurs: Amine Ben-Daoued; Frédéric Bernardin; Pierre Duthon
Publié dans: Journal of Imaging, 2024, ISSN 3064-6936
Éditeur: MDPI
DOI: 10.3390/JIMAGING10120306

LiDAR De-Snow Score (DSS): Combining Quality and Perception Metrics for Optimized De-Noising

Auteurs: Valentina Donzella, Pak Hung Chan, Daniel Gummadi, Abu Mohammed Raisuddin, Eren Erdal Aksoy
Publié dans: IEEE Sensors Journal, Numéro 25, 2025, ISSN 1530-437X
Éditeur: 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

Auteurs: Tiago Cortinhal, Eren Erdal Aksoy
Publié dans: Robotics and Autonomous Systems, 2023, ISSN 0921-8890
Éditeur: Science Direct | Elsevier
DOI: 10.1016/j.robot.2023.104583

A Survey and New Perspective of Sensing in the Dark for Intelligent Transportation Systems

Auteurs: Yiting Wang, Boda Li, Zixiang Wei, Anima Rahman, Daniel Gummadi, Haonan Zhao, Valentina Donzella
Publié dans: IEEE Transactions on Intelligent Transportation Systems, Numéro 26, 2025, ISSN 1524-9050
Éditeur: Institute of Electrical and Electronics Engineers (IEEE)
DOI: 10.1109/TITS.2025.3600557

Two-dimensional time-to-collision measures for articulated vehicles: Predicting sideswipe and rear-end collisions

Auteurs: Abhijeet Behera, Sogol Kharrazi, Erik Frisk, Maytheewat Aramrattana
Publié dans: Transportation Research Interdisciplinary Perspectives, Numéro 39, 2026, ISSN 2590-1982
Éditeur: Elsevier BV
DOI: 10.1016/J.TRIP.2026.102236

Geometric and radiometric Calibration of a rotating multi-beam Lidar using a rotating tilted Platform

Auteurs: Heikki Hyyti, Matias Mäki-Leppilampi, Harri Kaartinen
Publié dans: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Numéro XLIX-B1-2026, 2026, ISSN 2194-9034
Éditeur: Copernicus GmbH
DOI: 10.5194/ISPRS-ARCHIVES-XLIX-B1-2026-255-2026

Robustness of Panoptic Segmentation for Degraded Automotive Cameras Data

Auteurs: Yiting Wang, Haonan Zhao, Daniel Gummadi, Mehrdad Dianati, Kurt Debattista, Valentina Donzella
Publié dans: IEEE Transactions on Automation Science and Engineering, Numéro 22, 2025, ISSN 1545-5955
Éditeur: Institute of Electrical and Electronics Engineers (IEEE)
DOI: 10.1109/TASE.2025.3605753

REHEARSE-3D: A Multi-Modal Emulated Rain Dataset for 3D Point Cloud De-Raining

Auteurs: Abu Mohammed Raisuddin, Jesper Holmblad, Hamed Haghighi, Yuri Poledna, Maikol Funk Drechsler, Valentina Donzella, Eren Erdal Aksoy
Publié dans: Sensors, Numéro 26, 2026, ISSN 1424-8220
Éditeur: MDPI AG
DOI: 10.3390/S26020728

SWEET: A Realistic Multiwavelength 3D Simulator for Automotive Perceptive Sensors in Foggy Conditions

Auteurs: Amine Ben-Daoued; Pierre Duthon; Frédéric Bernardin
Publié dans: Journal of Imaging, 2023, ISSN 2313-433X
Éditeur: MDPI
DOI: 10.3390/jimaging9020054

A Unified Generative Framework for Realistic LiDAR Simulation in Autonomous Driving Systems

Auteurs: Hamed Haghighi, Yiting Wang, Mehrdad Dianati, Kurt Debattista, Valentina Donzella
Publié dans: IEEE Sensors Journal, Numéro 26, 2026, ISSN 1530-437X
Éditeur: Institute of Electrical and Electronics Engineers (IEEE)
DOI: 10.1109/JSEN.2025.3642208

From operational design domain to test cases: A methodology to include harsh weather

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

Thesis and dissertations (1)

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

Auteurs: Julius Pesonen
Publié dans: Machine Learning, Data Science and Artificial Intelligence, 2023
Éditeur: Aaltodoc publication archive

Other (3)

Correlating traditional image quality metrics and DNN-based object detection: a case study with compressed camera data

Auteurs: Daniel Gummadi, Pak Hung Chan, Hetian Wang, Valentina Donzella
Publié dans: TechRxiv, 2023
Éditeur: TechRxiv
DOI: 10.36227/techrxiv.24566371.v1

Analysis of Faster R-CNN network prediction in the presence of lens occlusion and video compression

Auteurs: Gabriele Baris, Boda Li, Pak Hung Chan, Carlo Alberto Avizzano, Valentina Donzella
Publié dans: TechRxiv, 2023
Éditeur: TechRxiv
DOI: 10.36227/techrxiv.23047412.v1

An Experimental Study on ObjectTracking

Auteurs: Mahmoud Alshaikh
Publié dans: 2025
Éditeur: n/a

Book chapters (2)

Road Grip Uncertainty Estimation Through Surface State Segmentation

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

Dense Road Surface Grip Map Prediction from Multimodal Image Data

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