Vehicle Intersection Collision Monitoring Algorithm Based on VANETs and Uncertain Trajectories

Abstract

In order to ensure driving safely, the driving safety assistance system must be able to aware of potential collision accidents in advance, especially significant for the intersection where traffic accidents occur more frequently. In the proposed approach, we utilized the widely adopted architecture of recurrent neural networks, Long-Short Term Memory networks (LSTM) architecture to form a deep stacked LSTM network which can accurately predict future longitudinal and lateral locations for vehicles. Considering that VANETs is one of the most important applications for improving the safety of driving, and in order to reduce the system’s communication and computational costs, a vehicle intersection collision monitoring algorithm based on VANETs and uncertain trajectories is proposed. The algorithm is divided into two categories: uncertain trajectories prediction algorithm and vehicle collision monitoring algorithm. The proposed approach provides approximate answers to the user at the users required level of accuracy while achieving near-optimal communication and computational costs. Finally, extensive experiments were conducted to show the efficiency and efficacy of the proposed approach.