This paper describes an intelligent traffic management system applied to road intersections, namely round-about 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.

This article presents a novel intersection traffic management system for automated vehicles and quantifies its impact on fuel consumption and greenhouse gas emissions of CO₂ relative to traditional traffic signal and roundabout intersection control. The developed intelligent traffic management (ITM) techniques, which are based on a spatiotemporal reservation scheme, ensure that vehicles proceed through the intersection without colliding with other vehicles while at the same time reducing the intersection delay and environmental impacts. Specifically, the spatiotemporal reservation scheme provides each vehicle a collision-free path that is decomposed into a speed profile along with navigational instructions. The integration of the developed microscopic traffic simulator with instantaneous emission model, provides improved assessments of the environmental impact of traffic control strategies at intersections. The simulator architecture integrates several ITM algorithms, vehicle sensors, V2V/V2I communications, and emission and fuel consumption models. Each vehicle is modeled by an agent and each agent provides information depending on the specific vehicle sensors. The ITM system is supported by V2V and V2I communications, allowing the exchange of information among vehicles and infrastructure. The data include the estimated vehicle position and speed. Compared with traditional traffic management techniques, the simulation results prove that the proposed ITM system reduces CO₂ emissions significantly. The research also shows that these reductions are more significant when the traffic flow increases.

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.

This paper presents a guidance system for au- tonomous vehicles navigation in semi-structured outdoor envi- ronments. It integrates redundant encoders data and absolute positioning data provided by landmarks and artificial beacons. Natural features are localized using a laser range sensor, and magnetic sensing rulers were developed to detect magnetic markers buried in the ground. 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 absolute positioning data. Simulation and real experiments using a four-wheels actuated electrical vehicle are presented. I. INTRODUCTION Odometry being essential for autonomous navigation, is not enough due to its relative and integrative nature. So, it is required to complement odometric data with absolute positioning. Due to systematic error problems, some effort has been dedicated in modelling the uncertainty propagation in order to get somehow a reliable odometry model (6). Data fusion of ABS sensors and GPS for outdoor localization, based on an Extended Kalman Filter (EKF) had been pre- sented by Bonnifait, et al. (2).