SFB/Transregio 28 Cognitive Automobiles
From 2006 until 2010, researchers from KIT, Technische Universität München, Universität der Bundeswehr München, and Fraunhofer IITB worked together in the Transregional Collaborative Research Center (Sonderforschungsbereich/Transregio) 28 Cognitive Automobiles.
Subproject B3: Distributed Cooperation
Vision and Fusion Laboratory and Fraunhofer IITB (now IOSB) were in charge of subproject B3 Distributed Cooperation. The main goal of subproject B3 was to increase traffic safety by cooperative behavior of communicating vehicles.
In addition to the information from the car's local perception obtained by sensors such as video and GPS, high-level information is exchanged with other traffic participants via vehicle-to-vehicle communication. This results in more adequate, trustworthy and reliable information on the traffic situation.
Communication-based safety systems can mitigate dangers by a timely warning, for example when approaching the tail end of a traffic jam.
However, exchange of information is not sufficient in many situations. Rather, coordinated and trustworthy behavior of multiple vehicles is required. The latter was be investigated within the subproject B3.
Consider the example of a suddenly appearing obstacle on a multi-lane road. All involved vehicles have to coordinate their maneuvers in order to avoid the obstacle. Otherwise, a collision between some of the vehicles might result. Therefore, the reliable and timely execution of the individual parts of the multi-vehicle maneuver has to be assured.
Overtaking is another important scenario for distributed cooperation. Coordinated braking of the vehicles on the right lane allows the overtaking vehicle to merge earlier, thus reducing the risk of a collision with oncoming traffic.
The first step towards cooperative behavior is the aggregation of all involved vehicles into a cooperative group. For each cooperative group, a common relevant picture will be set up which contains the important information on the current and future traffic situation. Situation recognition and assessment are based on the common relevant picture. Depending on the detected situation, adequate cooperative action decisions for each vehicle are generated, which are executed in a coordinated manner.
Goals of Subproject B3
- Cooperative multi-criteria decisions for exploiting the safety benefit from coordinated actions of multiple cooperative vehicles within a traffic scenario.
- Handling of safety-critical situations which temporarily require coordinated behavior by automated driving actions.
- Formal representation of cooperative groups:
- criteria for join, leaving, formation, split, merging, decomposition,
- representation in a distributed computing envrionment,
- inter-group communication.
- Fusion of all relevant information available to the cooperative group into a common relevant picture which contains
- cooperating vehicles (vehicle state, abilities, missions, intentions),
- relevant non-cooperative vehicles and objects,
- mutual relations,
- relevant environmental conditions.
- Recognition of dangerous situations which cannot be handled by the drivers.
- Planning cooperative maneuvers for collision avoidance and mitigation.
- Methods for a formal representation of the common relevant picture:
- Ontologies: situation modeling, rule-based inference, situation recognition
- Bayesian networks
- Methods for optimal distributed decisions:
- Cooperative motion planning
- Statistical decision theory
- Agent-based simulation: modeling, simulation and verification using well-defined example scenarios
- Assessment of performance and reliability of the above-mentioned methods
The feasibility of distributed group formation and the cooperative collision avoidance have been shown by means of a traffic simulator. Different algorithms for planning cooperative motions have been compared. In many scenarios, the investigated algorithms are more successful in terms of collision avoidance than state-of-the-art methods such as prioritized motion planning. The cooperative motion planning algorithms show potential for a real-time implementation.
|Cooperative Group of Vehicles and Dangerous Situations, Recognition of||Watson, K.; Frese, C.; Batz, T.; Beyerer, J.||Meyers, R. (ed.), Encyclopedia of Sustainability Science and Technology, pp. 2463-2489, Springer, 2012.|
|Planung kooperativer Fahrmanöver für kognitive Automobile||Frese, C.||Dissertation, Karlsruher Schriften zur Anthropomatik, vol. 10, KIT Scientific Publishing, 2012.|
|Collision Avoidance by Cooperative Driving Maneuvers||Frese, C.; Beyerer, J.||ATZ Elektronik Worldwide no. 5/2011, pp. 48-52, Springer, 2011.|
|Kollisionsvermeidung durch kooperative Fahrmanöver||Frese, C.; Beyerer, J.||Automobiltechnische Zeitschrift ATZ Elektronik 6 no. 5, pp. 70-75, Springer, 2011.|
|Information fusion for cognitive automobiles||Puente León, F.; Beyerer, J.||Information Fusion 12 no. 4, pp. 242-243, Elsevier, 2011.|
|A Comparison of Motion Planning Algorithms for Cooperative Collision Avoidance of Multiple Cognitive Automobiles||Frese, C.; Beyerer, J.||Proceedings of IEEE Intelligent Vehicles Symposium, pp. 1154-1160, 2011.|
|A Comparison of Algorithms for Planning Cooperative Motions of Cognitive Automobiles||Frese, C.||Technical report IES-2010-06. In: Beyerer, J.; Huber, M. (eds.), Proceedings of the 2010 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory, Karlsruher Schriften zur Anthropomatik, vol. 7, pp. 75-90, KIT Scientific Publishing, 2010.|
|Planning Cooperative Motions of Cognitive Automobiles Using Tree Search Algorithms||Frese, C.; Beyerer, J.||Dillmann, R.; Beyerer, J.; Hanebeck, U. D.; Schultz, T. (eds.), KI 2010: Advances in Artificial Intelligence, Lecture Notes in Artificial Intelligence, vol. 6359, pp. 91-98, Springer, 2010.|
|Team AnnieWAY's Autonomous System for the DARPA Urban Challenge 2007||Kammel, S.; Ziegler, J.; Pitzer, B.; Werling, M.; Gindele, T.; Jagszent, D.; Schröder, J.; Thuy, M.; Goebl, M.; von Hundelshausen, F.; Pink, O.; Frese, C.; Stiller, C.||Buehler, M.; Iagnemma, K.; Singh, S. (eds.), The DARPA Urban Challenge - Autonomous Vehicles in City Traffic, Springer Tracts in Advanced Robotics, vol. 56, pp. 359-391, Springer, 2009.|
|Kooperative Bewegungsplanung zur Unfallvermeidung im Straßenverkehr mit der Methode der elastischen Bänder||Frese, C.; Batz, T.; Beyerer, J.||Dillmann, R.; Beyerer, J.; Stiller, C.; Zöllner, J. M.; Gindele, T. (eds.), Autonome Mobile Systeme, pp. 193-200, Springer, 2009.|
|Cooperative Motion Planning using Branch and Bound Methods||Frese, C.||Technical report IES-2009-13. In: Beyerer, J.; Huber, M. (eds.), Proceedings of the 2009 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory, pp. 187-201, KIT Scientific Publishing, 2009.|
|Recognition of dangerous situations within a cooperative group of vehicles||Batz, T.; Watson, K.; Beyerer, J.||Proceedings of IEEE Intelligent Vehicles Symposium, pp. 907-912, 2009.|
|Cooperative Behavior of Groups of Cognitive Automobiles based on a Common Relevant Picture||Frese, C.; Batz, T.; Beyerer, J.||at - Automatisierungstechnik 56 no. 12, pp. 644-652, Oldenbourg Wissenschaftsverlag, 2008.|
|Team AnnieWAY's Autonomous System for the 2007 DARPA Urban Challenge||Kammel, S.; Ziegler, J.; Pitzer, B.; Werling, M.; Gindele, T.; Jagszent, D.; Schröder, J.; Thuy, M.; Goebl, M.; von Hundelshausen, F.; Pink, O.; Frese, C.; Stiller, C.||Journal of Field Robotics 25 no. 9, pp. 615-639, 2008.|
|Life Cycle Management for Cooperative Groups of Cognitive Automobiles in a Distributed Environment||Frese, C.; Batz, T.; Wieser, M.; Beyerer, J.||Proceedings of IEEE Intelligent Vehicles Symposium, pp. 1125-1130, 2008.|
|Team AnnieWAY Technical System Description||Kammel, S.; Pitzer, B.; Vacek, S.; Schröder, J.; Frese, C.; Werling, M.; Goebl, M.||DARPA Urban Challenge Technical Paper, 2007.|
|Bildung kooperativer Gruppen kognitiver Automobile||Frese, C.; Beyerer, J.||Berns, K.; Luksch, T. (Hrsg.): Autonome Mobile Systeme 2007, Informatik aktuell, Springer, pp. 177-183, 2007.|
|Cooperation of Cars and Formation of Cooperative Groups||Frese, C.; Beyerer, J.; Zimmer, P.||Proceedings of IEEE Intelligent Vehicles Symposium, Istanbul, pp. 227-232, 2007.|