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This study is motivated by the fact that localization in Vehicle-to-Vehicle communication becomes a more critical problem because both the terminals of the communication link are in motion. The positional awareness merely based on GPS or local sensors has an error margin of around 10 meters, which can worsen in uncertain real-time conditions such as road topology and highway traffic. The paper analyses the relation between beamforming and beam alignment for highly directive antennas. This is more challenging in the events of localization of transceivers. When the subsystem models presented in this paper are taken into consideration, the joint vehicle dynamics-beamforming approach will improve the SNR for a constant power gain. The vehicle dynamics model is designed to be more realistic considering the non-linear acceleration based on the throttle-brake jerks due to internal engine noises as well as external traffic conditions. The prediction subsystem highlights the flaws of the Kalman Filter for non-linear parameters and the need for an Unscented Kalman Filter. The beamforming strategies are supported by the requirements of localization and the hardware constraints on the antenna due to phase shifters and the number of elements to yield more realistic results. 

The Intelligent Transportation System has become one of the most globally researched topics, with Connected and Autonomous Vehicles(CAV) at its core. The CAV applications can be improved by the study of vehicle platooning immune to realtime traffic and vehicular network losses. In this work, we explore the need to integrate the Network model and Platooning system model for highway environments. The proposed platoon model is designed to be adaptive in length, providing the node vehicles to merge and exit. This overcomes the assumption that all the platoon nodes should have a common source and destination. The challenges of the existing platoon model, such as relay selection, acceleration threshold, are addressed for highly modular platoon design. The presented algorithm for merge and exit events optimizes the trade-off between network parameters such as communication range and vehicle dynamic parameters such as velocity and acceleration threshold. It considers the network bounds like SINR and link stability and vehicle trajectory parameters like the duration of the vehicle in the platoon. This optimizes the traffic throughput while maintaining stability using the PID controller. The work tries to increase the vehicle inclusion time in the platoon while preserving the overall traffic throughput.