A comprehensive survey on cooperative intersection management for heterogeneous connected vehicles

Nowadays, with the advancement of technology, world is trending toward high mobility and dynamics. In this context, intersection management (IM) as one of the most crucial elements of the transportation sector demands high attention. Today, road entities including infrastructures, vulnerable road users (VRUs) such as motorcycles, moped, scooters, pedestrians, bicycles, and other types of vehicles such as trucks, buses, cars, emergency vehicles, and railway vehicles like trains or trams are able to communicate cooperatively using vehicle-to-everything (V2X) communications and provide traffic safety, efficiency, infotainment and ecological improvements. In this paper, we take into account different types of intersections in terms of signalized, semi-autonomous (hybrid) and autonomous intersections and conduct a comprehensive survey on various intersection management methods for heterogeneous connected vehicles (CVs). We consider heterogeneous classes of vehicles such as road and rail vehicles as well as VRUs including bicycles, scooters and motorcycles. All kinds of intersection goals, modeling, coordination architectures, scheduling policies are thoroughly discussed. Signalized and semi-autonomous intersections are assessed with respect to these parameters. We especially focus on autonomous intersection management (AIM) and categorize this section based on four major goals involving safety, efficiency, infotainment and environment. Each intersection goal provides an in-depth investigation on the corresponding literature from the aforementioned perspectives. Moreover, robustness and resiliency of IM are explored from diverse points of view encompassing sensors, information management and sharing, planning universal scheme, heterogeneous collaboration, vehicle classification, quality measurement, external factors, intersection types, localization faults, communication anomalies and channel optimization, synchronization, vehicle dynamics and model mismatch, model uncertainties, recovery, security and privacy

Traffic congestion prevention system

Transport is one of the key elements in the development of any country; it can be a powerful catalyst for economic growth. However, the infrastructure does not give enough to the huge number of vehicles which produces several problems, particularly in terms of road safety, and loss of time and pollution. One of the most significant problems is congestion, this is a major handicap for the road transport system. An alternative would be to use new technologies in the field of communication to send traffic information such as treacherous road conditions and accident sites by communicating, for a more efficient use of existing infrastructure.  In this paper, we present a CPS system, which can help drivers in order to have a better trip. For this raison we find the optimal way to reduce travel time and fuel consumption. This system based on our recent work [1]. It´s new approach aims to avoid congestion and queues, hat assure more efficient and optimal use of the existing road infrastructure. For that we concentrate by analyzing the useful and reliable traffic information collected in real time. The system is simulated in several conditions, Experimental result show that our approach is very effective. In the future work, we try to improve our system by adding more complexity in our system

Novel Internet of Vehicles Approaches for Smart Cities

Smart cities are the domain where many electronic devices and sensors transmit data via the Internet of Vehicles concept. The purpose of deploying many sensors in cities is to provide an intelligent environment and a good quality of life. However, different challenges still appear in smart cities such as vehicular traffic congestion, air pollution, and wireless channel communication aspects. Therefore, in order to address these challenges, this thesis develops approaches for vehicular routing, wireless channel congestion alleviation, and traffic estimation. A new traffic congestion avoidance approach has been developed in this thesis based on the simulated annealing and TOPSIS cost function. This approach utilizes data such as the traffic average travel speed from the Internet of Vehicles. Simulation results show that the developed approach improves the traffic performance for the Sheffield the scenario in the presence of congestion by an overall average of 19.22% in terms of travel time, fuel consumption and CO2 emissions as compared to other algorithms. In contrast, transmitting a large amount of data among the sensors leads to a wireless channel congestion problem. This affects the accuracy of transmitted information due to the packets loss and delays time. This thesis proposes two approaches based on a non-cooperative game theory to alleviate the channel congestion problem. Therefore, the congestion control problem is formulated as a non-cooperative game. A proof of the existence of a unique Nash equilibrium is given. The performance of the proposed approaches is evaluated on the highway and urban testing scenarios. This thesis also addresses the problem of missing data when sensors are not available or when the Internet of Vehicles connection fails to provide measurements in smart cities. Two approaches based on l1 norm minimization and a relevance vector machine type optimization are proposed. The performance of the developed approaches has been tested involving simulated and real data scenarios