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Martin Liebner

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

ICRA Conference 2020 Conference Paper

How to Keep HD Maps for Automated Driving Up To Date

David Pannen
Martin Liebner
Wolfgang Hempel
Wolfram Burgard

The current state of the art in automotive high definition digital (HD) map generation based on dedicated mapping vehicles cannot reliably keep these maps up to date because of the low traversal frequencies. Anonymized data collected from the fleet of vehicles that is already on the road provides a huge potential to outperform such state of the art solutions in robustness, safety and up-to-dateness of the map while achieving comparable quality. We thus present a solution based on crowdsourced data to (i) detect changes in the map independent of the type of change, (ii) automatically trigger map update jobs for parts of the map, and (iii) create and integrate map patches to keep the map always up to date. The developed solution provides a crowdsourced up to date HD map to make reliable prior information on lane markings and road edges available to automated driving functions.

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ICRA Conference 2019 Conference Paper

HD Map Change Detection with a Boosted Particle Filter

David Pannen
Martin Liebner
Wolfram Burgard

In this paper, we present a change detection algorithm that can run in real time as part of a backend-based stream processing pipeline. It can process the floating car data collected by series-production vehicles to detect changes in an automotive high definition digital (HD) map used for automated driving. The algorithm uses a particle filter approach with odometry, GNSS and landmark readings to localize the vehicle within the digital map. While all particles together represent the probability distribution for the vehicle's position at a given time, each individual particle also serves as a hypothesis about the vehicle's position. This is used to compute various metrics for how well the current sensor readings match the world model encoded in the HD map. The different metrics are evaluated by a number of weak classifiers that are used as input for a trained Adaboost classifier. The achievable detection rate of a single vehicle is then compared to that of a simple crowd-based approach, where each vehicle votes on whether or not the current section of the road has changed.

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IROS Conference 2019 Conference Paper

Lane Marking Learning based on Crowdsourced Data

David Pannen
Martin Liebner
Wolfram Burgard

In this paper, we propose a new algorithm that derives lane marking maps from crowdsourced data. We process the data in four steps: (i) We make use of a point landmark map and, if available, an existent lane marking map for trajectory optimization and alignment, (ii) use a custom DBSCAN variant to cluster observations that belong to the same lane marking, (iii) apply a novel graph fitting approach to extract lane marking dashes, lines and even complex structures such as splits and merges and (iv) optimize the graph geometry with domain knowledge. The process of point-landmark- and lane marking-based trajectory alignment and the lane marking derivation is repeated iteratively to improve the results. Evaluation is carried out on a 9km highway section by comparison with high accuracy aerial photographs and manually labeled ground truth lane markings.

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