To resolve the contradictions between the increasing demand of vehicular wireless applications and the shortage of spectrum resources, high mobility, short link lifetime, and spectrum efficiency, a novel cognitive radio (CR) and efficient management of spectrum in vehicular communication is required. Therefore, to exhibit the importance of spectral efficiency, a system model is proposed for cooperative centralized and distributed spectrum sensing in vehicular networks. The proposed architecture is used to minimize both the spectral scarcity and high mobility issues. Furthermore, we analyze the decision fusion techniques in cooperative spectrum sensing for vehicular networks. In addition, a system model is designed for decision fusion techniques using renewal theory, and then, we analyze the probability of detection of primary channel and the average waiting time for CR user or secondary user in PU transmitter. Finally, mathematical analysis is performed to check the probability of detection and false alarm. The results show that the cooperative cognitive model is more suitable for vehicular networks that minimize interference and hidden PU problem.Index Terms-Centralized spectrum sensing, cognitive radio (CR), decision fusion controller (DFC), distributed spectrum sensing.

The aim of Intelligent Transportation Systems (ITS) is to automate the interactions among vehicles and infrastructure to accomplish high levels of safety measures, comfort, and competence in vehicular communication. To utilize the future trends of increasing traffic safety and efficiency in ITS, integrating vehicles and infrastructures with the cooperative vehicular technique will be the feasible solution. In order to demonstrate the importance of cooperative communication in vehicular networks, a spectral efficient architecture has been proposed for cooperative centralized and distributed spectrum sensing in vehicular networks. We discuss the possibilities of Cognitive Radio in the cooperative vehicular environment. In order to exhibit cooperative vehicular networks, hardware modules are designed for a vehicle to vehicle, vehicle to infrastructure and infrastructure to infrastructure communications. Furthermore, quantitative analysis is made in order to calculate the energy optimization, connectivity failure probability and traffic management in cooperative vehicular networks. In addition, we test the results of the cooperative vehicular network by simulating it in NS2. In this respect, we have considered three different cases, Emergency vehicles, VIP vehicles, and normal vehicles. It is inferred from the results that end-to-end delay for emergency vehicles in the cooperative environment is considerably less as compared to VIP and normal vehicles.

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In an autonomous vehicle (AV), in order to efficiently exploit the acquired resources, big data analyses will be a reliable source for extracting valuable information from various sensors and actuators. The data extracted with the combined ability of telematics and real-time investigation forms the vibrant asset for self-driving cars. To demonstrate the significances of big data analysis, this study proposes a competent architecture for real-time big data analysis for an AV, which indeed keeps pace with the latest trends and advancement concerning an emerging paradigm. There are a massive amount of sensors and independent systems needed to be realised for better competence in an AV, and the proposed model focuses on independent sensors that distinguish objects and handles visual information to decide the path. In order to attain the objective as mentioned above, a sensor fusion mechanism is proposed, which combines 3D camera sensor data and Lidar sensor information to provide an optimised solution for path selection. Furthermore, three algorithms, namely overlapping algorithm, sequential adding algorithm, the distance-focused algorithm is designed for higher efficiency in sensor fusion mechanism. The proposed methodology is for the best exploitation of the enormous dataset, meant for real-time processing for an AV.

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