Thomas Speth

Enhanced Inter-Vehicular relative positioning

Intelligent Transportation System (ITS) applications for integral and cooperative vehicle safety as well as some Advanced Driver Assistance Systems (ADASs) benefit from precise determination of relative positions between dynamic traffic objects. With conventional Global Navigation Satellite System (GNSS) measurements, e.g. using Global Positioning System (GPS), the required accuracy cannot be achieved. For this reason, an exchange of GNSS observations via Vehicular Ad-Hoc Network (VANET) is proposed in this paper. In particular, the European Inter-Vehicle Communication (IVC) protocol stack ITS-G5 is employed. With these exchanged GNSS observations, Differential GNSS (DGNSS) or Real-Time Kinematic (RTK) calculations provide a precise relative position vector. However, due to relative movement of traffic objects, this position vector becomes obsolete for increasing transmission delays. For this reason, a mitigating kinematic model is set up and validated experimentally. With respect to fixed RTK solutions, this kinematic model reduces the errors by an average of 61% compared to position calculations ignoring IVC latency.

Dynamic position calibration by road structure detection

In this research study, the potential of landmarks for accuracy improvement in terms of satellite-based localization is analyzed. Advanced driver assistance systems (ADAS) and vehicle-to-vehicle (V2V) communication applications benefit from precise determination of position. Especially applications for cooperative car safety work best when the position determination is as accurate as possible. The novel approach is related to the differential GPS method, but without using a base station. Previously measured landmarks are used instead. To determine the potential of the approach, the level of increasing localization accuracy is evaluated. Moreover the distribution of naturally existing landmarks is analyzed using the recorded data of an endurance testing vehicle.

Test Methodology for Automotive Surround Sensors in Dynamic Driving Situations

Modern vehicles use surround sensors to measure their local environment. The information are processed and forwarded to intelligent pre-crash or automation functions enhancing vehicle safety or enabling automated driving. False or inaccurate measurements can lead to fatal consequences for humans and vehicles. High accuracy and robust environment perception in all driving situations, including high dynamic driving situations e.g. skidding, are compulsory requirements for these systems. Therefore, automotive surround sensors must be tested in various driving situations. This paper presents a new, non-destructive and reproducible test methodology for testing surround sensors in high dynamic driving situations. Therefore, the vehicles motion during a skid driving situation was mathematically described. The test methodology was validated through experiments carried out with a real test vehicle. Finally, the experimental setup and the results are presented and discussed.

Precise relative ego-positioning by stand-alone RTK-GPS

Intelligent Transportation System (ITS) applications for integral and cooperative vehicle safety as well as some Advanced Driver Assistance Systems (ADASs) benefit from highly accurate positioning. Shared position data between dynamic traffic objects via Inter-Vehicle Communication (IVC) are the backbone for deriving vehicle trajectories. These can be used for assessing a situations criticality in vehicle safety. However, with conventional Global Navigation Satellite System (GNSS) measurements, e.g. using Global Positioning System (GPS), the required accuracy cannot be achieved. There are known Cooperative Positioning (CP) methods like Differential GNSS (DGNSS) and Real-Time Kinematic (RTK) for enhanced positioning. Augmentation data are typically transmitted by a wireless communication link like cellular mobile communication. However, there exist dead spots where no correction data are available. For this reason, we introduce in this paper a method for stand-alone RTK by using own stored observations. Thereby, precise relative ego-positioning is possible during correction data interruption. The buffer time is varied in experiment and the error distribution is analyzed.