Volume IV-1/W1
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1/W1, 51-58, 2017
https://doi.org/10.5194/isprs-annals-IV-1-W1-51-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1/W1, 51-58, 2017
https://doi.org/10.5194/isprs-annals-IV-1-W1-51-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

  30 May 2017

30 May 2017

EXTRACTING LANE GEOMETRY AND TOPOLOGY INFORMATION FROM VEHICLE FLEET TRAJECTORIES IN COMPLEX URBAN SCENARIOS USING A REVERSIBLE JUMP MCMC METHOD

O. Roeth1, D. Zaum2, and C. Brenner3 O. Roeth et al.
  • 1Corporate Research, Robert Bosch GmbH Hildesheim, Germany
  • 2Chassis Systems Control, Robert Bosch GmbH Hildesheim, Germany
  • 3Institute of Cartography and Geoinformatics, Leibniz Universit¨at Hannover, Germany

Keywords: Lane Accurate Map Construction, Trajectories, GPS, DGPS, IMU, Road Network, Reversible-Jump Markov chain Monte Carlo

Abstract. Highly automated driving (HAD) requires maps not only of high spatial precision but also of yet unprecedented actuality. Traditionally small highly specialized fleets of measurement vehicles are used to generate such maps. Nevertheless, for achieving city-wide or even nation-wide coverage, automated map update mechanisms based on very large vehicle fleet data gain importance since highly frequent measurements are only to be obtained using such an approach. Furthermore, the processing of imprecise mass data in contrast to few dedicated highly accurate measurements calls for a high degree of automation.

We present a method for the generation of lane-accurate road network maps from vehicle trajectory data (GPS or better). Our approach therefore allows for exploiting today’s connected vehicle fleets for the generation of HAD maps. The presented algorithm is based on elementary building blocks which guarantees useful lane models and uses a Reversible Jump Markov chain Monte Carlo method to explore the models parameters in order to reconstruct the one most likely emitting the input data. The approach is applied to a challenging urban real-world scenario of different trajectory accuracy levels and is evaluated against a LIDAR-based ground truth map.