  Intelligent transportation systems and autonomous vehicles are expected to
bring new experiences with enhanced efficiency and safety to road users in the
near future. However, an efficient and robust vehicular communication system
should act as a strong backbone to offer the needed infrastructure
connectivity. Deep learning (DL)-based algorithms are widely adopted recently
in various vehicular communication applications due to their achieved low
latency and fast reconfiguration properties. Yet, collecting actual and
sufficient transportation data to train DL-based vehicular communication models
is costly and complex. This paper introduces a cellular vehicle-to-everything
(C-V2X) verification platform based on an actual traffic simulator and
spectrum-aware access. This integrated platform can generate realistic
transportation and communication data, benefiting the development and
adaptivity of DL-based solutions. Accordingly, vehicular spectrum recognition
and management are further investigated to demonstrate the potentials of
dynamic slidelink access. Numerical results show that our platform can
effectively train and realize DL-based C-V2X algorithms. The developed
slidelink communication can adopt different operating bands with remarkable
spectrum detection performance, validating its practicality in real-world
vehicular environments.
