Supervised Learning Approach for Relative Vehicle Localization Using V2V MIMO Links

Estimating vehicle locations is important for realizing Intelligent Transportation Systems (ITS). This paper considers utilizing vehicle-to-vehicle (V2V) communication for relative vehicular localization. In particular, we develop a machine learning (ML) solution that uses the Channel State Information (CSI) from multiple-antenna transceivers for vehicular localization. We develop suitable pre-processing to obtain a compact CSI representation as an input feature to the ML solution. The proposed solution is then based on feed-forward neural networks. Training and evaluation are done on measured real-world data in the 5.9 GHz band. The performance on two routes shows that the proposed feature may improve the performance while reducing the number of trainable parameters. Furthermore, the paper raises a number of interesting observations regarding the learnability in V2V ML-based localization solutions.