|Abstract:||The main concern of autonomous driving (AD) is the safety of human beings, both inside and outside the vehicle. Safety depends on a wide variety of complex factors such as vehicle speed, weather, state of the road, complexity of the environment (surrounding vehicles, pedestrians or obstacles), or situational awareness, among others. In order to cope with these factors, different sensors are placed in the vehicle to measure dozens of parameters (absolute speed, distance to surrounding vehicles and pedestrians, relative speed, absolute position, distance to next crossing, etc.). Accurate knowledge of these and other parameters is a key to safety, but even more important is to ensure their reliability. Such guarantee on reliability is what the aviation community refers to as integrity. The implementation of an integrity layer is crucial since in safety-critical applications it can be more important to know whether information is reliable than the precise information itself. Attempts to rely positioning integrity of land users upon the use of Satellite Based Augmentation Systems (SBAS) only have revealed major shortages, given that SBAS integrity has been conceived for use in open sky and clean operation scenarios as it is the case of aviation users. As a result, a lot of effort is being devoted to the investigation of autonomous techniques for determining the integrity of the navigation solution taking into account the local effects on GNSS signals in harsh environmental conditions. GMV has been working for a decade in developing GNSS-based navigation systems for automotive applications where integrity and accuracy are top-priority requirements. As a result, GMV has developed navigation technologies of very high accuracy and proven integrity which can combine GNSS with a wide variety of other sensors both from the vehicle (accessed through CAN bus) and external to it (low-cost inertial sensors have been successfully hybridised with GNSS in aftermarket integrity-enabled solutions). The purpose of this paper is to present the performances achieved with GMV navigation and integrity technologies, which are an input to automotive applications like in ESCAPE project (), where the GNSS-based systems are essential and where the GMV navigation and integrity technologies will be combined with vehicle sensors and camera measurements to provide an accurate and reliable solution. ESCAPE (European Safety Critical Applications Positioning Engine) is a project co-funded by the European GNSS Agency (GSA) under the European Union’s Fundamental Elements research and development programme. It started on October 2016 with duration of 3 years and with the main objective of developing a localisation system that provides the vehicle pose estimates to be employed in safety critical applications like Autonomous Driving (AD) or Advanced Driving Assistance Systems (ADAS). The project is led by the Spanish company FICOSA in collaboration with partners from across Europe: Renault and IFSSTAR from France, STMicroelectronics and Instituto Superiore Mario Boella from Italy and GMV from Spain. ESCAPE will enable a high-grade of data fusion with different vehicle sensors and the exploitation of key technological differentiators such as the Precise Point Positioning service (PPP), the potential use of the Galileo ionospheric model and the provision of an integrity layer to assess the degree of trust one can associate to the position information provided by the device.|
Proceedings of the 30th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2017)
September 25 - 29, 2017
Oregon Convention Center
|Pages:||655 - 664|
|Cite this article:||
Tijero, E. Domínguez, Pons, E. Carbonell, Calle, J.D. Calle, Fernández, L. Martínez, Madrid, P.F. Navarro, Varo, C. Moriana, Sáenz, M. Azaola, "Advanced GNSS Algorithms for Safe Autonomous Vehicles," Proceedings of the 30th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2017), Portland, Oregon, September 2017, pp. 655-664.
ION Members/Non-Members: 1 Download Credit