  In this paper, a novel environment-embedded vehicular channel model is
proposed by scatterer recognition from light detection and ranging (LiDAR)
point clouds via Synesthesia of Machines (SoM). To provide a robust data
foundation, a new intelligent sensing-communication integration dataset in
vehicular urban scenarios is constructed. Based on the constructed dataset, the
complex SoM mechanism, i.e., mapping relationship between scatterers in
electromagnetic space and LiDAR point clouds in physical environment, is
explored via multilayer perceptron (MLP) with electromagnetic propagation
mechanism. By using LiDAR point clouds to implement scatterer recognition,
channel non-stationarity and consistency are modeled in an environment-embedded
manner. Using ray-tracing (RT)-based results as the ground truth, the scatterer
recognition accuracy exceeds 90%. The accuracy of the proposed model is further
verified by the close fit between simulation results and RT results.
