Lanelets: Efficient map representation for autonomous driving

In this paper we propose a highly detailed map for the field of autonomous driving. We introduce the notion of lanelets to represent the drivable environment under both geometrical and topological aspects. Lanelets are atomic, interconnected drivable road segments which may carry additional data to describe the static environment. We describe the map specification, an example creation process as well as the access library libLanelet which is available for download. Based on the map, we briefly describe our behavioural layer (which we call behaviour generation) which is heavily exploiting the proposed map structure. Both contributions have been used throughout the autonomous journey of the Mercedes Benz S 500 Intelligent Drive following the Bertha Benz Memorial Route in summer 2013.

[1]  Dinggang Shen,et al.  Lane detection using spline model , 2000, Pattern Recognit. Lett..

[2]  Ernst D. Dickmanns,et al.  Recursive 3-D Road and Relative Ego-State Recognition , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Ralf G. Herrtwich,et al.  Making Bertha See , 2013, 2013 IEEE International Conference on Computer Vision Workshops.

[4]  Rafael Toledo-Moreo,et al.  Creating Enhanced Maps for Lane-Level Vehicle Navigation , 2010, IEEE Transactions on Intelligent Transportation Systems.

[5]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[6]  Marios Hadjieleftheriou,et al.  R-Trees - A Dynamic Index Structure for Spatial Searching , 2008, ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems.

[7]  Markus Schreiber,et al.  LaneLoc: Lane marking based localization using highly accurate maps , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[8]  Michael Baer,et al.  Multiple track 4D-road representation , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[9]  Carsten Hasberg,et al.  Simultaneous Localization and Mapping for Path-Constrained Motion , 2012, IEEE Transactions on Intelligent Transportation Systems.

[10]  Cláudio Rosito Jung,et al.  A robust linear-parabolic model for lane following , 2004, Proceedings. 17th Brazilian Symposium on Computer Graphics and Image Processing.

[11]  Martin Lauer,et al.  Team AnnieWAY's Entry to the 2011 Grand Cooperative Driving Challenge , 2012, IEEE Transactions on Intelligent Transportation Systems.

[12]  David Harel,et al.  Statecharts: A Visual Formalism for Complex Systems , 1987, Sci. Comput. Program..

[13]  Naim Dahnoun,et al.  A novel system for robust lane detection and tracking , 2012, Signal Process..

[14]  Maan El Badaoui El Najjar,et al.  Road Selection Using Multicriteria Fusion for the Road-Matching Problem , 2007, IEEE Transactions on Intelligent Transportation Systems.

[15]  Wolfram Burgard,et al.  Map-Based Precision Vehicle Localization in Urban Environments , 2008 .

[16]  In So Kweon,et al.  Finding and tracking road lanes using "line-snakes" , 1996, Proceedings of Conference on Intelligent Vehicles.

[17]  Bui Tuong Phong Illumination for computer generated pictures , 1975, Commun. ACM.

[18]  Julius Ziegler,et al.  Trajectory planning for Bertha — A local, continuous method , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[19]  Wolfram Burgard,et al.  OctoMap : A Probabilistic , Flexible , and Compact 3 D Map Representation for Robotic Systems , 2010 .

[20]  Andreas Geiger,et al.  Lost! Leveraging the Crowd for Probabilistic Visual Self-Localization , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.