Method and system for vehicle-to-pedestrian collision avoidance

A vehicle-to-pedestrian collision avoidance system, comprising participants consisting of a set of at least two Long-Term Evolution (LTE)-capable user equipment (UE) terminals physically linked to at least one vehicle and at least one pedestrian;

• wherein a spatiotemporal positioning of the terminals is determined from Long Term Evolution (LTE) cellular radio signals mediated by at least three Long-Term Evolution (LTE) cellular base stations (BS) and at least one Location Service Client (LCS) server;
• the at least one Location Service Client (LCS) server includes an embedded Artificial Intelligence algorithm comprising a Recurrent Neural Network (RNN) algorithm, analyzes the spatiotemporal positioning of the terminals and determines the likely future trajectory of the participants so as to maximize a reward metric based on Reinforcement Learning (RL) analysis;
• and communicates the likely future trajectory of the participants to the terminals physically linked to the at least one pedestrian;
• the terminals physically linked to the at least one pedestrian include an embedded Artificial Intelligence algorithm comprising a Conditional Random Fields (CRFs) algorithm to determine if the likely future trajectory of the at least one pedestrian is below a vehicle-to-pedestrian proximity threshold limit and, if this condition is reached, the terminal physically linked to the at least one pedestrian communicates a collision-avoidance emergency signal to at least one of:
• the at least one pedestrian and the at least one vehicle that meet the proximity threshold limit.