2026-04-13T15:11:43Zhttps://recercat.cat/oai/request
oai:recercat.cat:2117/4593542026-03-31T12:08:20Zcom_2072_1033col_2072_452950
RECERCAT author Lázaro Villa, José Antonio author Casas Pla, Josep Ramon author Ruiz Hidalgo, Javier author Cortada Garcia, Martí author Martin Pey, Gerard author Salavedra Pujol, Judit author Wang, Mingrui author Theodoropoulou, Eleni author Lyberopoulos, George author Garcia Gómez, Pablo author Royo Royo, Santiago author Riu Gras, Jordi author Marcus, Carina author Eriksson, Olof author Schrenk, Bernhard author Fàbrega Sánchez, Josep Maria 2024 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 –such as high-resolution cameras or Radio/Light Detection and Ranging (RaDAR/LiDAR)– 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 diverse 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.This work has received funding from the Smart Networks and Services Joint Undertaking (SNS JU) under the European Union’s Horizon Europe research and innovation Programme under Grant Agreement No. 101139182, and Spanish MICIU founded, TRAINER-B (PID2020-118011GB-C22).Peer ReviewedPostprint (author's final draft) Open Access Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Comunicacions mòbils Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telecomunicació òptica Autonomous driving Crowdsourced SLAM Data fusion 3D scene completion 3D object detection Freespace optical communication Optical communication terminals 6G 6G-EWOC: Crowdsourced SLAM data fusion for safe and efficient ADAS driving Conference report