A V2P Warning System on the Basis of LoRa Wireless Network

Abstract

Vehicle-to-Everything (V2X) communication is a groundbreaking technology that enables interconnected services in the realm of smart transportation. Among the various V2X applications, Vehicle-to-Pedestrian (V2P) communication plays a crucial role in enhancing road traffic efficiency and safety by facilitating the exchange of information between vehicles and pedestrians. However, the existing V2P warning systems neglect the inherent uncertainty associated with pedestrian trajectories, leading to suboptimal accuracy in detecting collision risks between vehicles and pedestrians. Consequently, the potential for improving road safety is limited. To address this issue, we propose an advanced pedestrian-vehicle anti-collision model. This model takes into account the uncertain nature of pedestrian movement and leverages the Long Range (LoRa) wireless network to establish a V2P warning system. Specifically, we employ the long short-term memory artificial neural network (LSTM) to accurately predict pedestrian trajectories. By combining the pedestrian’s trajectory with a multi-dimensional normal distribution function, we obtain the probability density function that characterizes the pedestrian’s movement. Subsequently, we deduce the preliminary collision area between pedestrians and vehicles. Finally, we utilize a confidence probability metric to determine whether a warning should be issued to both pedestrians and vehicles. Simulation results demonstrate the effectiveness of our system in accurately warning pedestrians and vehicles, even under varying speeds and Global Positioning System (GPS) positioning errors. The experimental evaluation of our proposed method further validates its superior performance and efficacy.