Live testing of an autonomous car during last November provided positioning performances with safety margins, achieved by estimated protection levels designed into the positioning engine. In the European Safety Critical Applications Positioning Engine (ESCAPE) GNSS Engine (EGE), a real-time Precise Point Positioning (PPP) hybrid algorithm employs dual-frequency GPS and Galileo measurements, inertial sensors and PPP corrections from a web server over a cellular network.
A robust GPS + Galileo hybrid configuration for the positioning algorithm performs consistency checks in parallel for safety. This enhances the accuracy by integrating data from several vehicle sensors. EGE enables potential use of the Galileo signal authentication feature and tests provision of an integrity layer to assess the degree of confidence one can associate with the position information provided by the device.
Automotive intelligent cameras provide lateral distance measurements to road markings, combined with data showing position computed relative to lane-level accurate maps. This yields an accurate position relative to the map and enables estimation of the associated integrity Protection Levels (PLs), computed for multiple-target integrity risks (IRs).
The EGE was integrated in an autonomous car for driving tests in July 2019 at the University of Technology of Compiègne (UTC) in France. The car also carried a high-grade trajectory reference system (GNSS+high-grade IMU); its post-processed centimeter-error solution served as a truth reference to assess EGE performance.
The EGE (European Safety Critical Applications Positioning Engine GNSS Engine) provides a hybrid solution with an integrity layer, making the most of all sensors that should be present in autonomous vehicles and it creates a new paradigm of safety-oriented navigation technology on the road.