High Accuracy Positioning Engine with an Integrity Layer for Safety Autonomous Vehicles

Autonomous vehicles are becoming a reality. One of the most critical functions of an autonomous vehicle is to be able to obtain an accurate and reliable estimation of the position and attitude of the vehicle. This is even more critical when operating without human supervision as for the SAE Level 4. This paper presents an innovative positioning engine capable of providing highly accurate real-time position and attitude estimates along with an integrity layer consisting of Protection Levels (PL), which bound the error of each estimated value with a certain confidence level. The position and attitude estimated by the engine are based on the measurements provided by an intelligent off-the shelf automotive camera, a mass market dual-frequency GNSS receiver, low-cost inertial sensors, vehicle odometry and lane-level navigation maps. Highly accurate positions are computed by means of a real time PPP hybrid algorithm that employs dual-frequency GPS and Galileo measurements, inertial sensors and PPP corrections obtained from a web server when the connection is available through the cellular network. Another robust hybrid standard positioning algorithm runs in parallel allowing consistency checks. Last but not least, the automotive intelligent camera provides lateral distance measurements to the road lane marks which are employed to improve the accuracy by means of the lane-level accurate maps and also to provide an accurate position relative to the map. The position and attitude values provided by the engine (including the position computed relative to the map) are complemented with the estimation of the associated integrity Protection Levels (PL), computed for multiple target integrity risks. The implementation of an integrity layer is crucial since in safety-critical applications it can be more important to know whether the information is reliable than the precise information itself. This integrity layer determines the degree of usability of the location and attitude estimations, which is used as part of autonomous vehicle architecture to ensure that the vehicle operates safely. This engine has been developed within the ESCAPE project ([1]) and is being integrated and tested with an autonomous car. ESCAPE (European Safety Critical Applications Positioning Engine) is a project co-funded under the Fundamental Elements program of the European GNSS Agency (GSA). It started on October 2016 with a duration of 3 years and with the main objective of developing a localisation system that provides the vehicle position and attitude estimations 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, IFSSTAR and the University of Technology of Compiegne from France, STMicroelectronics and Instituto Superiore Mario Boella from Italy and GMV from Spain. ESCAPE enables a high-grade of data fusion (GNSS, inertial sensors, cameras and vehicle sensors) and the exploitation of several key technological differentiators such as the precise point positioning service (PPP), the potential use of the Galileo signal authentication and the provision of an integrity layer to assess the degree of trust one can associate to the position information provided by the device. Therefore, the three key pillars of the ESCAPE positioning engine are: ? The smart exploitation of different localization data sources to provide a highly accurate navigation solution, including GPS and Galileo dual-frequency measurements, intelligent cameras providing lateral distance to road lane-marks, inertial measurement units, vehicle odometry, PPP corrections and high definition maps; ? The unique provision of real-time integrity protection levels associated to the location estimates, which express the “degree of usability” of the positioning information for safety-critical applications. The PLs associated to the positions computed relative to the map are fundamental for autonomous driving applications; ? The full integration of the ESCAPE engine into a vehicle with autonomous driving capabilities, and its test on several different reference paths and environmental conditions. The innovative ESCAPE GNSS Engine (EGE) is close-to-market, its components are organized in a modular architecture and has safety at its core as its specification and design are based on the ISO 26262 recommendations ([2]). The EGE includes the following main components: ? GNSS receiver chipset: an automotive-grade multi-band and multi-constellation GNSS receiver. ? Inertial Measurements Unit (IMU) ? Communication peripherals needed for the communications of the board with the vehicle and with the cellular network. ? Main processor running the EGE algorithms: - GNSS+Sensors Positioning&Integrity algorithm, where GNSS measurements are integrated with those provided by an inertial measurement unit (IMU) in order to provide a GNSS pose with integrity. - Camera-based Positioning&Integrity algorithm. The accurate maps containing road lane-marks along with the intelligent camera measurements allow a second positioning service with accuracies that can reach a few centimeters in the cross direction, thus complementing and enhancing the positions provided by the GNSS+Sensors Positioning&Integrity algorithm.