Experimental Assessment of UWB and Vision-Based Car Cooperative Positioning System

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2021-12, Vol.13 (23), p.4858
• Main Authors: Masiero, Andrea, Toth, Charles, Gabela, Jelena, Retscher, Guenther, Kealy, Allison, Perakis, Harris, Gikas, Vassilis, Grejner-Brzezinska, Dorota
• Format: Article
• Language: English
• Subjects: Accuracy; Automobiles; Availability; Collaboration; Communication; cooperative positioning; Data retrieval; extended kalman filter; Global navigation satellite system; Information sharing; Infrastructure; Integration; Investigations; Learning algorithms; Lidar; Localization; Machine learning; Navigation satellites; Platforms; Radios; Receivers & amplifiers; Remote sensing; Robustness (mathematics); Sensors; ultra-wide band; Ultrawideband radar; Vehicle-to-infrastructure; Vehicles; Vision; Working conditions
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs13234858

The availability of global navigation satellite systems (GNSS) on consumer devices has caused a dramatic change in every-day life and human behaviour globally. Although GNSS generally performs well outdoors, unavailability, intentional and unintentional threats, and reliability issues still remain. This has motivated the deployment of other complementary sensors in such a way that enables reliable positioning, even in GNSS-challenged environments. Besides sensor integration on a single platform to remedy the lack of GNSS, data sharing between platforms, such as in collaborative positioning, offers further performance improvements for positioning. An essential element of this approach is the availability of internode measurements, which brings in the strength of a geometric network. There are many sensors that can support ranging between platforms, such as LiDAR, camera, radar, and many RF technologies, including UWB, LoRA, 5G, etc. In this paper, to demonstrate the potential of the collaborative positioning technique, we use ultra-wide band (UWB) transceivers and vision data to compensate for the unavailability of GNSS in a terrestrial vehicle urban scenario. In particular, a cooperative positioning approach exploiting both vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) UWB measurements have been developed and tested in an experiment involving four cars. The results show that UWB ranging can be effectively used to determine distances between vehicles (at sub-meter level), and their relative positions, especially when vision data or a sufficient number of V2V ranges are available. The presence of NLOS observations is one of the principal factors causing a decrease in the UWB ranging performance, but modern machine learning tools have shown to be effective in partially eliminating NLOS observations. According to the obtained results, UWB V2I can achieve sub-meter level of accuracy in 2D positioning when GNSS is not available. Combining UWB V2I and GNSS as well V2V ranging may lead to similar results in cooperative positioning. Absolute cooperative positioning of a group of vehicles requires stable V2V ranging and that a certain number of vehicles in the group are provided with V2I ranging data. Results show that meter-level accuracy is achieved when at least two vehicles in the network have V2I data or reliable GNSS measurements, and usually when vehicles lack V2I data but receive V2V ranging to 2–3 vehicles. These working conditions typically