  In this paper, a novel channel modeling approach, named light detection and
ranging (LiDAR)-aided geometry-based stochastic modeling (LA-GBSM), is
developed. Based on the developed LA-GBSM approach, a new millimeter wave
(mmWave) channel model for sixth-generation (6G) vehicular intelligent
sensing-communication integration is proposed, which can support the design of
intelligent transportation systems (ITSs). The proposed LA-GBSM is accurately
parameterized under high, medium, and low vehicular traffic density (VTD)
conditions via a sensing-communication simulation dataset with LiDAR point
clouds and scatterer information for the first time. Specifically, by detecting
dynamic vehicles and static building/tress through LiDAR point clouds via
machine learning, scatterers are divided into static and dynamic scatterers.
Furthermore, statistical distributions of parameters, e.g., distance, angle,
number, and power, related to static and dynamic scatterers are quantified
under high, medium, and low VTD conditions. To mimic channel non-stationarity
and consistency, based on the quantified statistical distributions, a new
visibility region (VR)-based algorithm in consideration of newly generated
static/dynamic scatterers is developed. Key channel statistics are derived and
simulated. By comparing simulation results and ray-tracing (RT)-based results,
the utility of the proposed LA-GBSM is verified.
