Year: 2024
Type: conference-paper
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 –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.
Authors: José A. Lázaro, Josep R. Casas, Javier Ruiz-Hidalgo, Martí Cortada Garcia, Gerard Martín Pey, Judit Salavedra Pujol, Mingrui Wang, Eleni Theodoropoulou, George Lyberopoulos, Carina Marcus, Olof Eriksson, Pablo García, Santiago Royo, Jordi Riu, Bernhard Schrenk, Josep M. Fàbrega
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4411232731