Traffic Forecasting using Vehicle-to-Vehicle Communication

2021/04/12 by Steven Wong, Wong, Steven, Lejun Jiang +9
Engineering · #FOS: Computer and information sciences #I.2.7 #Machine Learning (cs.LG) #Traffic Prediction and Management Techniques #Traffic control and management #Vehicular Ad Hoc Networks (VANETs)

paper · pdf · doi:10.48550/arxiv.2104.05528

openalex publication_date 2021/04/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We take the first step in using vehicle-to-vehicle (V2V) communication to provide real-time on-board traffic predictions. In order to best utilize real-world V2V communication data, we integrate first principle models with deep learning. Specifically, we train recurrent neural networks to improve the predictions given by first principle models. Our approach is able to predict the velocity of individual vehicles up to a minute into the future with improved accuracy over first principle-based baselines. We conduct a comprehensive study to evaluate different methods of integrating first principle models with deep learning techniques. The source code for our models is available at https://github.com/Rose-STL-Lab/V2V-traffic-forecast .

Citations

• Congested Traffic States in Empirical Observations and Microscopic Simulations
• VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation
• Learning Lane Graph Representations for Motion Forecasting

Related

• V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction
• DeepTFP: Mobile Time Series Data Analytics based Traffic Flow Prediction
• Deep Learning-Based Vehicle Speed Prediction for Ecological Adaptive Cruise Control in Urban and Highway Scenarios
• PredictionNet: Real-Time Joint Probabilistic Traffic Prediction for Planning, Control, and Simulation
• Deep Learning applied to Road Traffic Speed forecasting