  Vehicle-to-vehicle (V2V) wireless communication systems are fundamental in
many intelligent transportation applications, e.g., traffic load control,
driverless vehicle, and collision avoidance. Hence, developing appropriate V2V
communication systems and standardization require realistic V2V propagation
channel models. However, most existing V2V channel modeling studies focus on
car-to-car channels; only a few investigate truck-to-car (T2C) or
truck-to-truck (T2T) channels. In this paper, a hybrid geometry-based
stochastic model (GBSM) is proposed for T2X (T2C or T2T) channels in freeway
environments. Next, we parameterize this GBSM from the extensive channel
measurements. We extract the multipath components (MPCs) by using a joint
maximum likelihood estimation (RiMAX) and then cluster the MPCs based on their
evolution patterns.We classify the determined clusters as line-of-sight,
multiple-bounce reflections from static interaction objects (IOs),
multiple-bounce reflections from mobile IOs, multiple-bounce reflections, and
diffuse scattering. Specifically, we model multiple-bounce reflections as
double clusters following the COST 273/COST2100 method. This article presents
the complete parameterization of the channel model. We validate this model by
contrasting the root-mean-square delay spread and the angular spreads of
departure/arrival derived from the channel model with the outcomes directly
derived from the measurements.
