The implementation of optimized management process for road intersections in Cooperative Intelligent Transport Systems (C-ITS) is highly recommended in order to reduce traffic jams and fuel consumption. Most of solutions are implemented on light controllers and they use various sensors in order to optimize intersection management. The use of vehicle communications, mainly I2V (Infrastructure to Vehicle), enhances the solutions since the vehicles continuously provide messages about their status (position, timestamp, speed, heading, etc.). In this paper, we suppose that a manager is embedded on a hotspot located at a road intersection. It will know the status of each road (thanks to requests sent by vehicles approaching the intersection) and will give the right to the most relevant vehicle to pass through the intersection (many vehicles could drive in the same time when they use concurrent roads). Our algorithm measures the weight of each lane and allows to non-crossing ones having the highest weight to traverse the intersection. The calculation of the weight is based on many parameters: the waiting time, the length of the road queue and some others. Our algorithm considers that the intersection topology as stable. The algorithm has been implemented on a simulator on three urban scenarios and our results show very promising performances, they show considerable enhancement in terms of computing time.

We derive independence tests by means of dependence measures thresholding in a semiparametric context. Precisely, the estimates of φ-mutual informations, associated to φ-divergences between a joint distribution and the product distribution of its marginals, are derived through the dual representation of φ-divergences. The asymptotic properties of the proposed estimates are established, including consistency, asymptotic distributions, and large deviations principle. The obtained tests of independence are compared via their relative asymptotic Bahadur efficiency and numerical simulations. It follows that the proposed semiparametric mutual information test is the optimal one. On the other hand, the proposed approach provides a new method for estimating the mutual information in a semiparametric setting, as well as a model selection procedure in a large class of dependence models, including semiparametric copulas.