Autonomous Driving: Project OPA3L

Professor Dr. Roger Förstner (Space Technology) and Professor Dr. Thomas Pany (Satellite Navigation) have acquired funding from the Federal Ministry for Economic Affairs and Energy for the project OPA3L “Optimally Assisted, Highly Automated, Autonomous and Cooperative Vehicle Navigation and Localization.”

Autonomous driving is one of the most controversial and complex issues of mobility today. One of the core competences of autonomous vehicles is reliable and precise navigation. This is achieved with a variety of sensors that are based on many different measurement principles, so that shortcomings of individual sensors can be compensated by others. The most prominent examples of such sensors include an inertial measurement unit (IMU), which measures acceleration and rotational rates with inertial sensors, light detection and ranging (LiDAR) sensors to create 3D point clouds of the environment with laser scans, and global navigation satellite system (GNSS) receivers, which determine their location on earth through radio signals transmitted from satellites.

^{Fig. 1: Navigation sensor system on the roof of the test vehicle of OPA3L: LiDAR, GNSS receiver and inertial measurement unit, IMU (© Universität der Bundeswehr München)}

Only the GNSS receivers can determine an absolute position, i.e. a coordinate on a map. High-precision positioning via GNSS is necessary for autonomous driving. It is based on special algorithms (such as Precise Point Positioning) and additional information on the quality of and external influences on the satellite signal. Future GNSS generations, particularly Galileo, will be able to send correction data together with the actual navigation signal. The data can be decoded at the receiver and included in the calculation.

With the OPA3L project, we are developing a navigation engine – based on the GNSS software receiver we developed called MuSNAT (Multi-Sensor Navigation Analysis Tool) – with an additional sensor fusion. This tool processes GNSS signals, IMU data and LiDAR point clouds and combines the resulting information to create a positioning solution.

Simply calculating a position is not sufficient for safety-critical applications, for example in road traffic. Validation is required to avoid risks to the integrity of vehicle navigation and to the road users. This is achieved with RAIM-based methods, which originated in civilian aviation and have been adapted for terrestrial use in vehicles. This technology in combination with information from additional sensors offers a broad approach to integrity checks.

^{Fig. 3: Decoded satellite clock parameters of the Galileo High Accuracy Service (HAS), which has been in test operation since 2022 and is being evaluated and improved by us in the course of OPA3L. The HAS forms the basis for autonomous driving in OPA3L (© Universität der Bundeswehr München)}

Project period: March 1, 2019 – February 14, 2023
Funding by: Federal Ministry for Economic Affairs and Energy – German Space Program – Navigation

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