Towards driving autonomously: Autonomous cruise control in urban environments

For automatic driving, vehicles must be able to recognize their environment and take control of the vehicle. The vehicle must perceive relevant objects, which includes other traffic participants as well as infrastructure information, assess the situation and generate appropriate actions. This work is a first step of integrating previous works on environment perception and situation analysis toward automatic driving strategies. We present a method for automatic cruise control of vehicles in urban environments. The longitudinal velocity is influenced by the speed limit, the curvature of the lane, the state of the next traffic light and the most relevant target on the current lane. The necessary acceleration is computed in respect to the information which is estimated by an instrumented vehicle.

[1]  Rüdiger Dillmann,et al.  Obstacle detection with a Photonic Mixing Device-camera in autonomous vehicles , 2008, Int. J. Intell. Syst. Technol. Appl..

[2]  S. Rauch,et al.  Autonomes Fahren auf der Autobahn – eine Potentialstudie für zukünftige Fahrerassistenzsysteme , 2012 .

[3]  Dennis Nienhuser,et al.  Recognition and tracking of temporary lanes in motorway construction sites , 2009, 2009 IEEE Intelligent Vehicles Symposium.

[4]  Ralf Kohlhaas,et al.  Anticipatory driving assistance for energy efficient driving , 2011, 2011 IEEE Forum on Integrated and Sustainable Transportation Systems.

[5]  Christian Raubitschek,et al.  Predictive driving strategies under urban conditions for reducing fuel consumption based on vehicle environment information , 2011, 2011 IEEE Forum on Integrated and Sustainable Transportation Systems.

[6]  Thomas Schamm,et al.  A model-based approach to probabilistic situation assessment for driver assistance systems , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[7]  Ralf Kohlhaas,et al.  Anticipatory energy saving assistant for approaching slower vehicles , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[8]  Cheng Wang,et al.  Design and capabilities of the Munich Cognitive Automobile , 2008, 2008 IEEE Intelligent Vehicles Symposium.

[9]  Thomas Gumpp,et al.  Probabilistic hierarchical detection, representation and scene interpretation of lanes and roads , 2012, 2012 IEEE International Conference on Vehicular Electronics and Safety (ICVES 2012).

[10]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[11]  Jochen Frey,et al.  Robust 3 D Measurement with PMD Sensors , 2005 .

[12]  Julius Ziegler,et al.  Team AnnieWAY's autonomous system for the 2007 DARPA Urban Challenge , 2008, J. Field Robotics.

[13]  Thomas Gumpp,et al.  Relevance estimation of traffic elements using Markov logic networks , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[14]  Markus Maurer,et al.  Stadtpilot: First fully autonomous test drives in urban traffic , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[15]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .