A Versatile Machine Learning-Based Vehicle-to-Vehicle Connectivity Model

We present in this paper a versatile machine learning (ML) based vehicle-to-vehicle (V2V) connectivity model constructed from empirical measurements to enable the mutual exchange of data among vehicles. We portray the results of a campaign for measurements and relevant models for the V2V channel within the 5-GHz band in the form of attained RSSI and bit rate values in multiple V2V environments. To that end, we perform a parallel analysis to compare performance of an assortment of ML regression algorithms to assess their capabilities in predicting RSSI values and bit rates, including K-nearest-neighbors, AdaBoost, Regression Trees, Random Forest, SGD, SVM, and Artificial Neural Networks to predict connectivity patterns. Results in the form of numerical analysis illustrate that in our connectivity model RSSI and bitrate could be effectually predicted utilizing a subset of the group of considered ML regression algorithms. Visualization results and evaluation metrics furnished identification of an optimum ML algorithm. Our presented models should be useful to employ in the context of V2V communication systems. As features into the ML algorithms, we explored the use of GPS coordinates of communicating nodes (vehicles) to predict instantaneous RSSI and bitrates on the road. Thus, all parameters of route environment that influence signals propagation and the associated implicit relationships are taken into account in the learning phase, and the subsequent prediction of future values. Numerical results corroborate the assumed models and approach.