Robust Automated Driving in Extreme Weather

Projektinformationen

ROADVIEW

ID Finanzhilfevereinbarung: 101069576

DOI

10.3030/101069576

EK-Unterschriftsdatum 9 Juni 2022

Startdatum 1 September 2022

Enddatum 31 August 2026

Finanziert unter

• Climate, Energy and Mobility

Gesamtkosten

€ 8 014 105,55

EU-Beitrag

€ 6 652 915,80

Koordiniert durch

HOGSKOLAN I HALMSTAD
Sweden

CORDIS bietet Links zu öffentlichen Ergebnissen und Veröffentlichungen von HORIZONT-Projekten.

Links zu Ergebnissen und Veröffentlichungen von RP7-Projekten sowie Links zu einigen Typen spezifischer Ergebnisse wie Datensätzen und Software werden dynamisch von OpenAIRE abgerufen.

Leistungen

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.

Veröffentlichungen

Conference proceedings (2)

Synthetic Extreme Weather for AI training: Concept and Validation

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

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

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

Other (6)

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

Autoren: Abu Mohammed Raisuddin, Tiago Cortinhal, Jesper Holmblad, Eren Erdal Aksoy
Veröffentlicht in: TechRxiv, 2023
Herausgeber: TechRxiv
DOI: 10.36227/techrxiv.24297166.v1

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

Autoren: Tiago Cortinhal, Idriss Gouigah, Eren Erdal Aksoy
Veröffentlicht in: arXiv, 2023
Herausgeber: arXiv
DOI: 10.48550/arXiv.2309.08932

Raw camera data object detectors: an optimisation for automotive processing and transmission

Autoren: Pak Hung Chan, Chuheng Wei, Anthony Huggett, Valentina Donzella
Veröffentlicht in: TechRxiv, 2023
Herausgeber: TechRxiv
DOI: 10.36227/techrxiv.23807499.v1

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

Autoren: Daniel Gummadi, Pak Hung Chan, Hetian Wang, Valentina Donzella
Veröffentlicht in: TechRxiv, 2023
Herausgeber: TechRxiv
DOI: 10.36227/techrxiv.24566371.v1

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

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

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

Autoren: Pak Hung Chan, Sepeedeh Shahbeigi Roudposhti, Xinyi Ye, Valentina Donzella
Veröffentlicht in: TechRxiv, 2023
Herausgeber: TechRxiv
DOI: 10.36227/techrxiv.24517249.v1

Thesis and dissertations (1)

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

Autoren: Julius Pesonen
Veröffentlicht in: Machine Learning, Data Science and Artificial Intelligence, 2023
Herausgeber: Aaltodoc publication archive

Peer reviewed articles (3)

A Novel Score-based LiDAR Point Cloud degradation Analysis Method

Autoren: Sepeedeh Shahbeigi, Honahan Robinson, Valentina Donzella
Veröffentlicht in: IEEE Transactions and Journals, 2024, ISSN 1803-7232
Herausgeber: 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

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

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

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