En ciudades y otros entornos urbanos, existen dos desafíos comunes para el rendimiento del GNSS. El primero es la interferencia multitrayecto, que se produce cuando las señales rebotan en edificios, fachadas de cristal e incluso coches aparcados antes de llegar al receptor. La oclusión de la señal es otro problema que ocurre cuando edificios y estructuras altas bloquean físicamente la visión de algunas señales satelitales. Altos edificios de cristal, calles estrechas, puentes de hormigón y pasos elevados forman cañones urbanos y pueden ser una barrera incluso para los sistemas de navegación más sofisticados.
The term “urban canyon” was inspired by New York’s Canyon of Heroes — a stretch of Lower Broadway where tall buildings line the streets similar to a canyoenn. These human-built canyons can confuse GNSS receivers making it hard to accurately calculate a vehicle’s position. For autonomous cars, that’s not just inconvenient — it’s a major safety issue. However, with the right technology, the automotive world can “close’” these urban canyons, explains Manuel Del Castillo, vice president of business development at Focal Point Positioning.
On open roads with a clear view of the sky, satellite navigation can be remarkably accurate. Signals from multiple GNSS constellations reach the vehicle’s receiver unimpeded, helping calculate position with impressive accuracy. However, this often isn’t the case in dense urban areas.
Tall glass buildings, narrow streets, concrete bridges and overpasses all form urban canyons — and can be a barrier to even the most sophisticated navigation systems.
The Challenge
In cities and other urban environments, there are two common challenges for GNSS performance. The first is multipath interference, which occurs when signals bounce off buildings, glass façades and even parked cars before reaching the receiver. Rather than receiving one clean signal from the satellite, the receiver gets a clean signal and several delayed copies, leading to erroneous positioning estimates.
Signal occlusion is another issue, which occurs when tall buildings and structures physically block some satellite signals from view. The signals that are actually received from that satellite are reflections. This makes it difficult for the receiver to lock onto a stable fix.
In practice, both issues can cause sudden anomalies — enough to place a car on the wrong street entirely. For drivers, this is frustrating. For autonomous systems, it’s a safety risk.
The Road to Autonomy
Urban GNSS challenges aren’t new — taxi drivers in London and New York have long experienced their navigation systems getting “lost” among the towers. However, positioning accuracy is now more important than ever as automotive technology evolves and we hand over more control to our vehicles.
Advanced driver assistance systems (ADAS) are now pushing the limits of conventional GNSS. Features such as lane-keeping, automated lane changes and intelligent speed adaptation all rely on knowing the vehicle’s exact position – not just the road it’s on, but which lane.
As we move further towards autonomous driving, the stakes will be even higher. If GNSS references are unreliable, this could cause serious errors on the road. A sudden position jump in the middle of a complex urban manoeuvre is more than inconvenient — it’s dangerous.
Closing the Canyon
If autonomous cars are to drive safely and reliably in urban environments, GNSS must evolve. The answer lies in rethinking how satellite signals are processed — and in tackling the root causes of error. Traditional receivers rely heavily on hardware-based processing, meaning they integrate new technologies at a slow pace.
To help overcome this challenge, we developed S-GNSS Auto — software that enhances GNSS receiver reliability and accuracy in autonomous vehicles. Delivered as a simple firmware upgrade, it transforms GNSS into a more powerful component of the ADAS stack in areas where traditional solutions fall short.
We recently integrated S-GNSS Auto onto STMicroelectronics’ Teseo GNSS devices, and tested the impact of the joint solution in some of the most challenging urban environments: Shinjuku in Tokyo, and Frankfurt and the Black Forest in Germany. The combined solution demonstrated an improvement in measurement accuracy by up to four times and position accuracy by up to three times in the challenging sections of these environments. By ignoring reflected or non-line-of-sight signals, S-GNSS Auto can also reject potential spoofing attacks, enhancing the security of the GNSS receiver.
McKinsey reports that 12% to 20% of cars could have advanced autonomous driving capabilities by 2030. For automakers, this means expanding the roads and environments that can safely support these capabilities. S-GNSS® Auto helps make that possible by improving GNSS reliability and laying the foundation for advanced vehicle-to-everything (V2X) and ADAS technologies needed to support autonomous vehicle safety in challenging urban areas. Working directly from the chip, it provides a cost-effective and accessible way for automotive OEMs to upgrade their technology via a firmware upgrade.
To see the impact of the integrated S-GNSS Auto and Teseo solution, download the latest data from our trials in Japan and Germany here.
Fuente: https://www.gpsworld.com
