Grid-based online road model estimation for advanced driver assistance systems

The information about the road course and individual lanes is an important requirement in driver assistance systems and for automated driving applications. It is often stored in a highly accurate offline map so that the road and the lanes are known in advance. However, there exist situations where an offline map can become unusable or invalid. This paper presents a novel approach for a road model estimation solely based on online measurements from sensors mounted on the ego vehicle. It combines perception data like detected lane markings, the movement history of dynamic objects in the vehicle's environment and detected road boundaries into a grid-based road model. This approach allows for an estimation of the road model even when one source of information is not available and offers a redundant source of information about the road, which is necessary in critical applications such as automated driving. The presented approach was tested and evaluated with a prototype vehicle and real sensor data from German highway scenarios.

[1]  Heidi Loose,et al.  Dreidimensionale Straßenmodelle für Fahrerassistenzsysteme auf Landstraßen , 2013 .

[2]  Martin Buss,et al.  Road course estimation in unknown, structured environments , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[3]  Torsten Bertram,et al.  Track-to-Track Fusion With Asynchronous Sensors Using Information Matrix Fusion for Surround Environment Perception , 2012, IEEE Transactions on Intelligent Transportation Systems.

[4]  Edwin Olson,et al.  Multi-Sensor Lane Finding in Urban Road Networks , 2008, Robotics: Science and Systems.

[5]  Gerd Wanielik,et al.  Probabilistic road estimation and lane association using radar detections , 2011, 14th International Conference on Information Fusion.

[6]  Yoshiko Kojima,et al.  Improved lane detection based on past vehicle trajectories , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[7]  Klaus C. J. Dietmayer,et al.  Generic grid mapping for road course estimation , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[8]  Friedrich M. Wahl,et al.  Camera-based lane border detection in arbitrarily structured environments , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[9]  Darius Burschka,et al.  Efficient occupancy grid computation on the GPU with lidar and radar for road boundary detection , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[10]  Christoph Stiller,et al.  Non-parametric lane estimation in urban environments , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[11]  Julius Ziegler,et al.  Making Bertha Drive—An Autonomous Journey on a Historic Route , 2014, IEEE Intelligent Transportation Systems Magazine.

[12]  Nico Kämpchen,et al.  Technologies for highly automated driving on highways , 2012 .