6G-EWOC: Crowdsourced SLAM Data Fusion for Safe and Efficient ADAS Driving

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.