ROADVIEW_EU

Are you interested in learning about the #robust #perception of #autonomous #vehicles? Then join Sergio Fernandez Navarro’s session and register now. ?? https://lnkd.in/dNcnepZB ?? 27 March 2025, 10:00 – 11:30 CET ?? About Sergio’s session As part of the ROADVIEW webinar – Robust Perception of #AVs, Sergio will offer an overview of his work on robustness against environmental artifacts in dense point cloud. ?? About Dr Sergio Fernandez Navarro Dr Sergio Fernandez Navarro is a senior expert in #LiDAR technology at Valeo. He holds two master's degrees in telecommunications engineering and another in computer vision and robotics. He further advanced his research with a PhD in computer vision, during which he also began working with LiDAR technology. Following this, he joined Valeo's LiDAR software algorithm team, where he built his career as an algorithm developer and architect. Currently, Sergio serves as the technical lead for #SCALA3 LiDAR and is Valeo's senior expert in LiDAR technology. #AV #AutonomousDriving #AI #ComputerVision

Are you interested in learning about robust perception of autonomous vehicles, especially in the context of #harsh #weather? Then join Petri Manninen’s session on 27 March 2025. Register now ?? https://lnkd.in/dNcnepZB ?? 27 March 2025, 10:00 – 11:30 CET ?? About Petri’s session As part of the ROADVIEW webinar – Robust Perception of #AVs, Petri will offer an overview of his work within the ROADVIEW project on the environment-aware high-definition mapping method for real-time positioning of an autonomous vehicle on the map. ?? About Petri Petri Manninen is a Research Scientist at the Finnish Geospatial Research Institute (FGI). He holds a master’s in #robotics and automation engineering and a bachelor’s in electrical, electronics, and #communication engineering. His research focuses on mobile laser scanning, #LiDAR perception, LiDAR odometry, and #HD #map #positioning, with a broader interest in robotics, autonomous systems, perception, localisation, and mapping. #AV #AutonomousDriving #AI #ComputerVision

?? Last week, #ROADVIEW partners gathered in #Ingolstadt, Germany for the project’s 7th General Assembly, hosted by Technische Hochschule Ingolstadt. ?? The meeting was a valuable opportunity for partners to present their progress, engage in fruitful discussions, and set priorities for the months ahead. ?? A highlight of the event was the exclusive visit to Technische Hochschule Ingolstadt’s testing track and facilities, where partners explored cutting-edge testing vehicles and research infrastructure. A special thank you to our guest participants, Felix Friedmann and Fabio Reway, for their insightful contributions and engaged discussions! #AV #CCAM #HorizonEurope

Writing this post to highlight some cool work done at Carissma/THI* and ROADVIEW_EU that seems really useful for other researchers & RnD institutes! Hoping to connect ppl and that more AV research will be shared as open source: Last Friday I joined the review of the EU ROADVIEW project - in which partners operate a vehicle autonomously on a test track and inject obstacles from simulation into the sensors. This way they have real driving dynamics and can simulate critical situations without danger. Looking at the broad spectrum of contributions here, some things seem super useful for other research institutes as well: * Valentina Donzella's group is working on realistic sensor models with focus on correct error modeling * THI/Carissma's work on simulated sensor data injection - air gapped by by filming screen (they validated good model accuracy) or by injection via GMSL via Konrad Technologies (see image below) * THI/Carissma's custom Drive-by Wire Interface - which they are employing in several vehicles now! * Yuri Poledna's work on running the AV stack on the NVIDIA DRIVE platform For THI/Carissma, Maikol Drechsler should be a great point of contact, he did an amazing job showing the group around and explaining! It was great seeing everything come together and I hope as much as possible will end up becoming open source. also realized that Autonomous Driving Meetup + me should focus more on AV open source. Thanks Maikol, Valentina and all Roadview contributors! --- * THI: Technische Hochschule Ingolstadt

?? Join us online for a ROADVIEW webinar on the perception of #AVs! Engage in focused discussions, exchange knowledge, and connect with the #CCAMcommunity. ?? Register now! ?? https://lnkd.in/dJ-arJ4V ?? 27 March 2025 ??10:00-11:30 CET ?? Full programme: 10:00 – 10:00: Welcome & introduction 10:05 – 10:30: Reaching robustness against environmental artefacts in dense point cloud 10:30 – 10:55: Environment-aware high-definition mapping method for real-time positioning of an autonomous vehicle on the map 10:55 - 11:20: Environment-aware high-definition mapping method for real-time positioning of an autonomous vehicle on the map 11:20 - 11:30: Open Q&A session The event is organised by the ROADVIEW project and it will feature the participation of the following guest speakers: Sergio Fernandez Navarro (Valeo), Jyri Maanp?? (Finnish Geospatial Research Institute (FGI)), Petri Manninen (Finnish Geospatial Research Institute (FGI)), and Eren Erdal Aksoy (Halmstad University). #CAVs #HorizonEurope #EU

?? Last week, #ROADVIEW project coordinator Eren Erdal Aksoy (Halmstad University) participated in the #RTR2025 Conference in #Brussels, #Belgium. The RTR Conference is a key platform for Horizon 2020 and Horizon Europe projects to showcase their latest achievements and explore future research directions – focusing on environmental, economic, and societal benefits. A special thank you to the conference organisers and the CCAM Association for having ROADVIEW! #CCAM #AV #HEU #EU

Robust #CCAM systems to ensure security and safety require integration of new technologies. This afternoon at the #RTR2025, CCAM Partnership projects exchanged on what they can bring to the picture to ensure users' trust. SELFY PROJECT has been working on tools for cybersecurity, including a healing system and data management system. AI in automotive means both challenges and opportunities. AIthena works on methodologies, anonymisation thanks to AI and on AI for explainable decision making. AI4CCAM additionally addresses methods to generate simulations, contributes to improve AI models and evaluates user acceptance. ROADVIEW_EU, on the other hand, works on in-vehicle perception to address weather-related challenges. Those projects can provide very useful input into the upcoming large-scale demonstrations, planned for the final phase of the CCAM Partnership.

?? Unveiling #ROADVIEW R&D efforts: Weather Conditional Navigation System Have you ever wondered what happens when an automated vehicle encounters #harsh #weather conditions that compromise its perception systems? In this video, we explain how the ROADVIEW project deals with this scenario. #CCAM ?#AV #EU ?? Useful links YouTube video: https://lnkd.in/diUrbBmJ Voiceover credits: 11.ai?

?? Last week, the #ROADVIEW partner WMG, University of Warwick hosted the inaugural #IEEE #UK and #Ireland Chapter event. During this, Valentina Donzella presented the latest progress of the ROADVIEW project, capturing the interest of both academic and industry professionals. Interested? Follow our social media channels for more updates! #CCAM #AV #HEU #EU

?? On the road to the commercialisation of an automated vehicle, its software needs to be deeply tested. Due to the system’s automated nature, these tests are unique; however, if conducted on public roads and a system failure occurs, a catastrophic accident is highly likely. Named after the action of practise a play or song for a later public performance, #REHEARSE (adveRse wEatHEr datAset for sensoRy noiSe modEls) comes to help with the minimisation of this gap in the initial setup from the testing system. The dataset collected data from the CARISSMA Outdoor Track in #Ingolstadt, #Germany, and the CEREMA Pavin fog and rain test chamber in #ClermontFerrand, #France. Interested? Click on the links below to learn more! ?? Useful links Learn more about REHEARSE: https://lnkd.in/dkukXFzM Access the REHEARSE dataset: https://lnkd.in/dUhmErWY