A method for vehicle-to-pedestrian collision avoidance, comprising: physically linking at least one vehicle to at least one Long-Term Evolution (LTE)-capable user equipment (UE) terminal; physically linking at least one pedestrian (P) to at least one Long-Term Evolution (LTE)-capable user equipment (UE) terminal; and determining a spatiotemporal positioning of each terminal 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; characterised in that the at least one Location Service Client (LCS) server includes an embedded artificial intelligence algorithm comprising a Recurrent Neural Network (RNN) algorithm to analyze the spatiotemporal positioning of the terminals and determine a likely future trajectory of the at least one vehicle and the at least one pedestrian (P) so as to maximize a reward metric based on Reinforcement Learning (RL) analysis; and the at least one Location Service Client (LCS) server communicates the likely future trajectory of the at least one vehicle and the at least one pedestrian (P) to the at least one terminal physically linked to the at least one pedestrian (P); the at least one terminal physically linked to the at least one pedestrian (P) including 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 (P) is below a vehicle-to-pedestrian proximity threshold limit; and, if the proximity threshold limit is reached, the terminal physically linked to the at least one pedestrian (P) communicates a collision-avoidance emergency signal to the at least one pedestrian (P) and to the at least one vehicle that meet the proximity threshold limit. physically linking at least one vehicle to at least one Long-Term Evolution (LTE)-capable user equipment (UE) terminal; physically linking at least one pedestrian (P) to at least one Long-Term Evolution (LTE)-capable user equipment (UE) terminal; and determining a spatiotemporal positioning of each terminal 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; characterised in that the at least one Location Service Client (LCS) server includes an embedded artificial intelligence algorithm comprising a Recurrent Neural Network (RNN) algorithm to analyze the spatiotemporal positioning of the terminals and determine a likely future trajectory of the at least one vehicle and the at least one pedestrian (P) so as to maximize a reward metric based on Reinforcement Learning (RL) analysis; and the at least one Location Service Client (LCS) server communicates the likely future trajectory of the at least one vehicle and the at least one pedestrian (P) to the at least one terminal physically linked to the at least one pedestrian (P); the at least one terminal physically linked to the at least one pedestrian (P) including 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 (P) is below a vehicle-to-pedestrian proximity threshold limit; and, if the proximity threshold limit is reached, the terminal physically linked to the at least one pedestrian (P) communicates a collision-avoidance emergency signal to the at least one pedestrian (P) and to the at least one vehicle that meet the proximity threshold limit. <EOS>