ISR-TrafSim

ISR-TrafSim – version 2.0

Source code (MATLAB) available in: ISR-TrafSim.v2.0

http://github.com/conde-ISR-UC-PT/ISR-TrafSim-V2.0

Intelligent Traffic Management at Intersections: Legacy Mode for Vehicles not Equipped with V2V and V2I Communications; L.C.Bento, R.Parafita, S.Santos and U.Nunes; 16th IEEE Int.Conf. Intelligent Transportation Systems, Hague, Netherlands, 2013.

Abstract-This paper describes a legacy algorithm for an intelligent traffic management system applied to automatic regulation of traffic at intersections. The application of the legacy algorithm enables the intelligent intersection to accommodate vehicles, in a low percentage, not equipped or with faulty V2V and V2I communications. The developed intelligent traffic management techniques, which are based on a spatiotemporal reservation scheme, aim to minimize accidents, traffic congestion and consequently the environmental costs of road traffic. The performance of three intelligent traffic management algorithms applied to road intersections, roundabouts and crossroads, are analyzed. Compared with traditional traffic management techniques, simulation results using the developed intelligent traffic management techniques, show that the traffic output flux can be increased, traffic flow rate can be higher and the average time to cross intersections can be significantly reduced. This reduction is more pronounced when the traffic flow is heavy. The conducted studies show that the inclusion of a low percentage of vehicles, not equipped or with faulty V2V and V2I communications, in intelligent intersections using the legacy algorithm, have a low impact on the traffic flow.

ISR-TrafSim – version 1.0

Source code (MATLAB) available in: ISR-TrafSim.v1.0

Intelligent Traffic Management at Intersections Supported by V2V and V2I Communications; L.C.Bento, R.Parafita and U.Nunes; 15th IEEE Int.Conf. Intelligent Transportation Systems; Anchorage; USA, 2012.

Abstract—This paper describes an intelligent traffic management system applied to road intersections, namely roundabout and crossroads. A microscopic traffic simulator was developed to study intelligent traffic management techniques and evaluate their performance. The intelligent management techniques are aimed to minimize accidents, traffic congestion and consequently the environmental costs of road traffic. Each vehicle is modeled by an agent and each agent provides information depending on its vehicle sensors. Two intersection types, roundabout and crossroads, were simulated each using its intelligent traffic management system. Both intersections use an algorithm based on a spatio-temporal reservation scheme. The envisioned intelligent traffic management algorithm is supported by vehicle-to-vehicle and vehicle-to-infrastructure communications, allowing the exchange of information between vehicles and the intersection intelligent traffic management system. The developed intelligent traffic management system is very well suited for autonomous vehicles, it can also be used by human drivers if they follow accurately the proposed speed profile along the path.

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Inter-Vehicle Sensor Fusion for Accurate Vehicle Localization Supported by V2V and V2I Communications; L.C.Bento, R.Parafita and U.Nunes; 15th IEEE Int.Conf. Intelligent Transportation Systems; Anchorage; USA, 2012.

Abstract—Cooperative driving system techniques aim to minimize accidents, traffic congestion and consequently the environmental costs of road traffic. An accurate vehicle’s pose is of extreme importance for the inner working of the traffic management systems. An agent based traffic simulator was developed integrating typical automotive sensors, vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, vehicle’s agent and infrastructure agent. In this paper, we propose to further enhance a multi-sensor localization algorithm in environments where V2V and V2I communication is possible. This paper describes an inter-vehicle sensor fusion for an accurate vehicle positioning, where each vehicle is modeled by an agent, and each agent provides information depending on its vehicle sensors. In the first fusion stage, data from four wheel encoders and one steering encoder are fused by means of an EKF, providing robust odometric information, namely in face of undesirable effects of wheels slippage. Next, a second fusion stage is processed for integrating odometric and inter-vehicle absolute positioning data.