Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Resurrection of “second order” models of traffic flow
SIAM Journal on Applied Mathematics
Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence
Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence
Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence
Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence
Robot Motion Planning
Multi agent simulation of unorganized traffic
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Modeling Traffic Flow at an Urban Unsignalized Intersection
ICCS '02 Proceedings of the International Conference on Computational Science-Part I
ACM SIGGRAPH 2006 Papers
Interactive navigation of multiple agents in crowded environments
Proceedings of the 2008 symposium on Interactive 3D graphics and games
A behavioral multi-agent model for road traffic simulation
Engineering Applications of Artificial Intelligence
Virtualized Traffic: Reconstructing Traffic Flows from Discrete Spatio-Temporal Data
VR '09 Proceedings of the 2009 IEEE Virtual Reality Conference
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This paper presents a novel traffic simulation scheme capable of modeling most forms of urban, chaotic traffic. Different from other lane-based or following-based approaches, ours models traffic as a large navigational problem in an agent based simulation context. While this generalization makes the traffic more reflective of certain scenarios, it also leads to some complexity that we address. It is able to handle dense traffic and selects for each car, independently, the optimal velocity and acceleration to find a path through a fast evolving obstacle network. The selection of parameters at each simulation step is posed as an optimization problem ensuring smooth motion subject to car kinematics. In addition to overtaking, the approach is efficiently able to handle hard cases like behavior at traffic lights and turning. We demonstrate our simulation at real-time rates using average computing resources.