Agent Drive


AgentDrive is a consolidated simulation framework for realistic vehicles simulation enabling testing of agent-based algorithms for route planning, navigation, cooperative driving, traffic optimization, and others vehicle coordination and cooperation methods.

Cluster of Activities

AgentDrive covers several activities in the vehicles simulation and coordination domain. It goes from realistic vehicles modeling and simulation through multilevel control and coordination to planning and optimization.

Ground Vehicles Modelling and Simulation

Realistic simulation of the ground vehicles is based on the integration of agent-based simulation with the game physics simulation engine. The ground assets are simulated by a ray-casted model of a wheeled ground vehicle. In principle, the model can be of any shape and can use any number of wheels. The model is defined using several parameters including wheel friction, suspension stiffness, damping, compression, engine power, break force, and others. The fidelity of the physical model helps to anticipate real-world effects for future design of the control, planning, and cooperation mechanisms. E.g. the planning algorithm has to take in account details of the environment as friction, and momentum. The real position of an asset reflects the motion dynamic and introduces the uncertainty of the plan execution which the planner has to be aware of.


Planning, controlling and simulation of the vehicles is based on three layers architecture composed of (i) deliberative layer for high level coordination and planning, (ii) reactive layer for short horizon path planning, plan repairing and environment feedback processing, and (iii) simulation layer for physics simulation of the vehicle.



Traffic Simulation

Multi-agent traffic simulation provides a realistic modeling with high fidelity continuous vehicle movement incorporating physical dynamics of the vehicles. The deliberative agent part of the system enables to implement and evaluate complex cooperation and coordination algorithms.

There are many different vehicles on a highway. Each of them acts somehow according to its logic. The logic determines behavior of the vehicle and reaction to critical situations. We distinguish cooperative and non-cooperative logic. Their main difference is in communication and interaction. Non-cooperative vehicles do not interact with other vehicles. They do not use negotiations with other vehicles. Cooperative vehicles are able to interact with the other cooperative vehicles.

Cooperative vehicles utilize the peer-to-peer collision avoidance algorithm originally developed for airplanes. Extended collision avoidance algorithm proved to be applicable in the highway traffic simulation in various scenarios. The vehicles than adapt their behavior with respect to agreed actions, e.g. in case of lane change the vehicles can adapt the speed in advance to avoid the need to break. The method helps the vehicles synchronize the speeds and improve the lane changing.



Vehicle Routing and Logistics

The vehicle routing problem and asset coordination is supported by the multi-agent solver based on task allocation. For the vehicle routing problem benchmarks the solver provides a solution with the quality of 81% compared to the optimal solution. It’s high applicability and low computational complexity allow us to deploy the solver not only in standard logistics domains (such as vehicle routing, pickup and delivery, time windowed problems, etc.) but also in wide scale of the dynamic routing and logistics scenarios such as cooperative navigation, dynamic frontiers exploration, undervalued cooperative tracking, cooperative area surveillance, etc.

The multi-agent task allocation solver supports routing and logistics problem solution in the tactical mission oriented projects for both ground and aerial asset scenarios. One of the strongest features is its high fidelity and scalability in dynamic scenarios with mixed types of assets.




Jiri Vokrinek (project coordinator, contact person), Antonin Komenda, Pavel Janovsky, Martin Schaefer, Karel Jalovec, Ales Franek, Jan Jiranek, Jan Harvalík

Related Activities



  • Petr Kalina and Jiri Vokrinek: Improved Agent Based Algorithm for Vehicle Routing Problem with Time Windows using Efficient Search Diversification and Pruning Strategy. In Proceedings of the Third International Workshop on Artifitial Intelligence and Logistics. Lyon: CNRS-ENS, 2012, p. 13-18.
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  • Petr Kalina and Jiri Vokrinek: Parallel Solver for Vehicle Routing and Pickup and Delivery Problems with Time Windows Based on Agent Negotiation. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, SMC 2012. New York: IEEE - Systems, Man, and Cybernetics Society, 2012, p. 1-6.
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  • Petr Kalina and Jiri Vokrinek: Algorithm for Vehicle Routing Problem with Time Windows Based on Agent Negotiation. In Proceedings of the Seventh International Workshop on Agents in Traffic and Transportation (ATT) at AAMAS 2012. Geneve: European Office of Aerospace Research and Development, 2012, p. 1-10.
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  • Vokrinek, J. and Komenda , A. and Pechoucek , M.: Abstract Architecture for Task-oriented Multi-agent Problem Solving. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on. 2011, vol. 41, p. 31 -40. ISSN 1094-6977.
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  • David Sislak and Premysl Volf and Michal Pechoucek: Agent-Based Cooperative Decentralized Airplane Collision Avoidance. IEEE Transactions on Intelligent Transportation Systems. 2011, vol. 12, p. 36-46. ISSN 1524-9050.
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  • Jiri Vokrinek and Antonin Komenda and Michal Pechoucek: Cooperative Agent Navigation in Partially Unknown Urban Environments. In PCAR '10: The Third International Symposium on Practical Cognitive Agents and Robots. Proceedings of the AAMAS-10 Workshops.. Toronto, Canada: IFAAMAS: Internatioal Foundation for Autonomous Agents and Multiagent Systems, 2010, p. 46-53. ISBN 0-98265-710-0.
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  • Jiri Vokrinek and Antonin Komenda and Michal Pechoucek: Agents Towards Vehicle Routing Problems. In AAMAS 2010: Proceedings of the Ninth International Conference on Autonomous Agents and Multi-Agent Systems. Toronto, Canada: IFAAMAS: Internatioal Foundation for Autonomous Agents and Multiagent Systems, 2010, p. 773-780. ISBN 0-98265-710-0/978-0-9826571-1-9.
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