On-Board Traffic Prediction for Connected Vehicles: Implementation and Experiments on Highways

Abstract

An on-board traffic prediction algorithm is proposed for connected vehicles traveling on highways. The prediction is based on data received from other connected vehicles ahead in the traffic stream, leveraging the fact that a vehicle will enter the traffic that other vehicles ahead have already met. Our method includes traffic state estimation with Kalman filter and prediction via traffic flow models describing the propagation of congestion waves. The end result is an individualized speed preview in real time up to about half a minute for the connected vehicle executing prediction. Most importantly, the traffic prediction was successfully implemented on board of a real vehicle and predictions were tested in real traffic with experiments involving connected human-driven vehicles.