@conference {cde-Mas-Gimenez25a, title = {Multimodal data acquisition prototype for autonomous driving in adverse weather conditions}, booktitle = {XIV Reuni{\'o}n OptoElectr{\'o}nica (OPTOEL 2025)}, year = {2025}, month = {07/2025}, publisher = {OPTOEL}, organization = {OPTOEL}, address = {Terrassa, Spain}, abstract = {

Autonomous driving has made significant progress in recent years, but adverse weather conditions still remain a major challenge for perception and decision-making algorithms. This work presents a multimodal data acquisition prototype designed to enhance autonomous vehicle perception in challenging environments such as fog, heavy rain, and snow. The system is mounted on a Dacia Duster and features a diverse sensor suite including visible, Short-Wave Infra-Red, thermal, and polarimetric cameras with a solid-state LiDAR and automotive radars. The whole system is calibrated with dedicated calibration boards developed on purpose to enable data fusion across all modalities. Initial results demonstrate the effectiveness of our approach, showing that critical environmental features can be detected reliably across various weather conditions. Future work includes the release of a fully annotated multimodal dataset to support further research in adverse weather perception.

}, isbn = {979-13-87613-54-9}, doi = {10.5821/ebook-9791387613549}, url = {https://optoel.es/2025/wp-content/uploads/2025/07/LIBRO-ACTAS-OPTOEL2025.pdf$\#$?page=403}, author = {Gerard de-Mas-Gim{\'e}nez and Adri{\`a} Subirana and Pablo Garc{\'\i}a-G{\'o}mez and Eduardo Bernal P{\'e}rez and Jordi Riu and Josep R Casas and Santiago Royo} } @conference {cde-Mas-Gimenez25b, title = {Preview of a multimodal data set for robust and safe autonomous driving under adverse weather conditions}, booktitle = {IMEKO PhotoMet - International Symposium on Modern Photonic Metrology}, year = {2025}, month = {09/2025}, publisher = {11 IMEKO PhotoMet}, organization = {11 IMEKO PhotoMet}, address = {Modena, Italy}, abstract = {

Autonomous driving has made remarkable strides in recent years, yet adverse weather conditions such as fog, heavy rain, snow, and low-light environments remain major obstacles for perception systems. These conditions reduce visibility and introduce complex artifacts like scattering, reflection, and sensor saturation, which degrade the performance of traditional camera-LiDAR-based perception algorithms. Current state-of-theart autonomous driving data sets avoid these edge cases. To address this, we introduce a new multimodal data set specifically curated to support research in robust perception under challenging environmental conditions. The data set was collected using a multimodal platform installed on a custom prototype which includes a diverse suite of sensors: visible RGB cameras, a Short-Wave Infra-Red (SWIR) camera, long-wave infrared thermal camera, and polarimetric camera. In addition to these optical sensors, the system incorporates a high-resolution solid-state LiDAR and two automotive-grade radars. These modalities were selected for their complementary responses to different environmental challenges. The prototype has a [60{\textordmasculine},40{\textordmasculine}] of overlapped FOV with all modalities ensuring enough redundancy to provide a good understanding of the vehicle{\textquoteright}s surroundings. Additionally, a GNSS/INS system is mounted to provide with localization and vehicle odometry. All sensors are temporally paired using a best effort synchronization algorithm. They are also spatially registered using multimodal calibration boards, enabling accurate data fusion even at long distances. The data set covers a wide range of real-world driving scenarios with urban, road and highway at high-velocities scenes across various lighting and weather conditions. From clear sky, to heavy rain, snow and even fog. Each frame includes 2D and 3D annotations for object detection tasks. The data set is structured as a relational database with an architecture similar to the reference data set, nuScenes. To ease visualization and navigation and through the data set, an extended python developer kit tool will be published. This work offers a preview of a multimodal data set which offers a rich and unique resource to the research community, enabling the development and evaluation of algorithms designed to operate reliably in degraded visibility. The data set is expected to be open-sourced later this year.

}, keywords = {Adverse weather conditions, autonomous driving, Data fusion, LiDAR, Multimodal imaging}, url = {https://photomet.org/abstracts}, author = {Gerard de-Mas-Gim{\'e}nez and Adri{\`a} Subirana and Eduardo Bernal P{\'e}rez and Jordi Riu and Josep R Casas and Pablo Garc{\'\i}a-G{\'o}mez and S. Royo} } @conference {cLazaro25, title = {Verifying Key Performance Indicators across Multiple 6G-EWOC Demonstrations}, booktitle = {European Conference on Networks and Communications (EuCNC) }, year = {2025}, month = {06/2025}, publisher = {IEEE}, organization = {IEEE}, address = {Poznan, Poland}, abstract = {

Testing, measuring, and validating Key Performance Indicators (KPIs) are crucial tasks across SNS-JU projects. These activities foster collaboration at the TMV Working Group, enabling the sharing of methods, definitions, target values, and best practices within the 6G vision. Thanks to collaboration of multiple agents, objectives can be different although the main goal of the WG is to develop and mature 6G technology. The 6G-EWOC project aims to describe how KPIs will be verified in different categories through multiple demonstrations. These categories include data rate and capacity in several technologies, sensing, positioning and localization, and energy efficiency. By addressing these different KPIs, the 6G-EWOC project ensures comprehensive evaluation and validation, contributing to the progress of the 6G vision.

}, url = {European\%20Conference\%20on\%20Networks\%20and\%20Communications\%20(EuCNC)}, author = {Jos{\'e} A L{\'a}zaro and Josep R Casas and Santiago Royo and Ruiz-Hidalgo, J. and F{\`a}brega, J.M. and Ricardo Mart{\'\i}nez and Renaudier, J. and Mardoyan, H. and Jensen, J.B. and Altab{\'a}s, J.A. and Pablo Garc{\'\i}a-G{\'o}mez and Jordi Riu and Baskopoulos, P. and Kalavrouziotis, D. and Syrivelis, D. and Op de Beeck, C. and Theodoropoulou, E. and Lyberopoulos, G. and Thor, J.} } @conference {cLazaro24, title = {6G-EWOC: Crowdsourced SLAM Data Fusion for Safe and Efficient ADAS Driving}, booktitle = {IEEE Future Networks World Forum: Symposium on Future Networks for Connected and Automated Mobility }, year = {2024}, month = {10/2024}, publisher = {IEEE}, organization = {IEEE}, address = {Dubai}, abstract = {

The development of transport infrastructures for Advanced Driver Assistance Systems (ADAS) and autonomous vehicles operating efficiently and safely in congestion-free traffic flows is a major challenge for telecommunications technologies.\  Simultaneous Localization and Mapping (SLAM) plays a crucial role in ensuring uninterrupted journeys for emergency vehicles and increasing the safety of vulnerable road users in complex traffic scenarios. Accurate SLAM mapping for ADAS systems requires data from different sensor technologies {\textendash}such as high-resolution cameras or Radio/Light Detection and Ranging (RaDAR/LiDAR){\textendash} to be effectively combined or fused. Sensor fusion results in high data throughput and low latency requirements. However, optimal mapping outcomes occur when processing systems fuse data from sensors positioned at different locations within the traffic scene. By crowdsourcing diverse sensors, we can multiply the view angles, mitigate occlusions and improve the overall scene coverage. Yet, this approach introduces additional challenges for communication systems within both the vehicles and the infrastructure. Addressing these challenges is essential for seamless development of safe and efficient ADAS driving techniques.

}, doi = {10.1109/FNWF63303.2024.11028711}, url = {https://ieeexplore.ieee.org/document/11028711}, author = {Jos{\'e} A L{\'a}zaro and Josep R Casas and Ruiz-Hidalgo, J. and Cortada-Garcia, M. and Mart{\'\i}n-Pey, G. and Salavedra-Pujol, J. and Wang, M. and Theodoropoulou, E. and Lyberopoulos, G. and Pablo Garc{\'\i}a-G{\'o}mez and S. Royo and Jordi Riu and Marcus, C. and Eriksson, O. and Schrenk, B. and F{\`a}brega, J.M.} } @conference {cSchrenk24, title = {6G-EWOC: Optical Wireless Communication in Support of Autonomous Driving}, booktitle = {IEEE Future Networks World Forum: Symposium on Future Networks for Connected and Automated Mobility }, year = {2024}, month = {10/2024}, publisher = {IEEE}, organization = {IEEE}, address = {Dubai}, abstract = {

The road towards an efficient transport infrastructure, which safely operates fully autonomous vehicles in congestion-free traffic flows, is subject to several technological challenges that reach also into the realm of telecommunications. Future 6G networks are required to handle huge amounts of sensor information, both in terms of low-latency communication and real-time data processing. The 6G-EWOC project contributes to this challenge through a concerted use of optical wireless communication and sensing with radio-frequency based cellular technology. Three optical-wireless scenarios are discussed, including (i) instant data exchange between vehicles leveraging their lighting assets for low-latency communication, (ii) the provision of vehicular data offloading to a fiber-based 6G infrastructure through beamsteering-assisted optical remote radio heads to enable data fusion at the edge cloud, and (iii) the seamless wireless extension of fiber-grade capacity to isolated network segments situated in fiber-scarce brownfields, which is accomplished through transparent optical air interfaces that ensure fiber-grade continuity by preventing a translucent electro-optic termination of the single-mode fiber channel. Another important contribution aims to advance light- and radio-based sensing technologies towards networked versions that offer simultaneous communication and detecting capabilities.

}, doi = {10.1109/FNWF63303.2024.11028710}, url = {https://ieeexplore.ieee.org/document/11028710}, author = {Schrenk, B. and Theodoropoulou, E. and Lyberopoulos, G. and Marcus, C. and Eriksson, O. and Jos{\'e} A L{\'a}zaro and Josep R Casas and Federico Dios and Pablo Garc{\'\i}a-G{\'o}mez and S. Royo and Jordi Riu and F{\`a}brega, J.M.} } @conference {cGarcia-Gomez22, title = {Multimodal imaging System based on solid-State LiDAR for Advanced perception applications}, booktitle = {10th International Symposium on Optronics in defence \& security}, year = {2022}, month = {06/2022}, publisher = {3AF OPTRO2022}, organization = {3AF OPTRO2022}, address = {Versailles, France}, url = {https://www.3af-optro.com/}, author = {Pablo Garc{\'\i}a-G{\'o}mez and S. Royo and Noel Rodrigo and Josep R Casas and Jordi Riu} } @conference {cGarcia-Gomez21, title = {Multimodal solid-state LiDAR for advanced perception applications}, booktitle = {OPTOEL}, year = {2021}, month = {06/2021}, abstract = {

Perception of the environment is an essential requirement for the fields of autonomous vehicles and robotics. Consequently, LiDAR imaging sensors have become crucial sen-sors for such applications due to their 3D geometry sensing capability. However, auton-omous systems claim for high amounts of data to make reliable decisions so many dif-ferent sensors are often combined. In this context, we present a multimodal imaging sys-tem based on a solid-state LiDAR combined with three other imaging sensors that pro-vides multimodal information with low parallax fusion error.

}, keywords = {artificial intelligence, autonomous navigation, computer Vision, enhanced perception, robotics, sensor fusion, solid-state LiDAR}, author = {Pablo Garc{\'\i}a-G{\'o}mez and Noel Rodrigo and Jordi Riu and Josep R Casas and S. Royo} } @conference {cGarcia-Gomez19, title = {Self-registered lidar and polarimetric images in real-time: application to detection of small objects at sea}, booktitle = {7th Workshop on Active Imaging}, year = {2019}, month = {11/2019}, publisher = {French-German Research Institute of Saint-Louis (ISL)}, organization = {French-German Research Institute of Saint-Louis (ISL)}, address = {Saint-Louis, France}, author = {Pablo Garc{\'\i}a-G{\'o}mez and Jordi Riu and Josep R Casas and S. Royo} }