integrity

Recently published datasets have been increasingly comprehensive with respect to their variety of simultaneously used sensors, traffic scenarios, environmental conditions, and provided annotations. However, these datasets typically only consider data collected by one independent vehicle.

There is currently a lack of comprehensive, real-world, multi-vehicle datasets fostering research on cooperative applications such as object detection, urban navigation, or multi-agent SLAM. In this paper, we aim to fill this gap by introducing the novel LUCOOP dataset, which provides time-synchronized multi-modal data collected by three interacting measurement vehicles.

GNSS integrity monitoring requires proper bounding to characterize all ranging error sources. Unlike classical approaches based on probabilistic assumptions, our alternative integrity approach depends on deterministic interval bounds as inputs. The intrinsically linear uncertainty propagation with intervals is adequate to describe remaining systematic uncertainty, the so-called imprecision. In this contribution, we make a proposal on how to derive the required intervals in order to quantify and bound the residual error for empirical troposphere models, based on the refined sensitivity analysis via interval arithmetic. This will contribute to a realistic uncertainty assessment of GNSS-based single point positioning.

For safety critical applications like autonomous driving, high trust in the reported navigation solution is mandatory. This trust can be expressed by the navigation performance parameters, especially integrity. Multipath errors are the most challenging error source in GNSS since only partial correction is possible. In order to ensure high integrity of GNSS-based urban navigation, signal propagation mechanisms and the potential error sources induced by the complex measurement environment should be sufficiently understood.

Successfully organized and co-chaired the first iLoc workshop on the topic of High-integrity Localization for Automated Vehicles at IEEE IV 2022.

Invited paper at the iLoc workshop: Deterministic approaches for bounding GNSS uncertainty: A comparative analysis

This paper introduces two deterministic approaches for GNSS uncertainty bounding and compares them with the conventional least-squares method theoretically and experimentally with simulated and real measurements.

for the paper entitled Deterministic approaches for bounding GNSS uncertainty: A comparative analysis

Talk at NAVITEC 2022, awarded best student presentation of the track Integrity and High-accuracy.

The first workshop on iLoc: High-integrity Localization for Automated Vehicles at the 33rd IEEE Intelligent Vehicles Symposiums (IV).