Target classification layer design via vehicle-to-vehicle communication

Abstract

Recently, an active safety system based on vehicle-to-vehicle communication was introduced to minimize the threat of car accidents on the road and to overcome the limitations of the current sensor-based advanced driver assist system. In order to implement the system based on vehicle-to-vehicle communication, target classification is the key layer to be developed. In this study, based on the transmitted path history of the remote vehicles, the road geometry around the host vehicle is reconstructed without in-vehicle sensors such as vision cameras. A tracking algorithm for the remote vehicle is formulated in order to predict its position continuously by using an extended Kalman filter. A local map is obtained with an outlier filter, and the target classification algorithm is designed from vehicle-to-vehicle communication data. The proposed algorithms are validated by simulations and experiments carried out using test vehicles.