The work reported here utilizes the best aspects of information theory and deep-learning concepts so as to provide, for the first time, a solution for a real-world location verification system (LVS) in the context of vehicular networks. it is well established that global positioning system coordinates supplied by vehicles will be a vital component of such emerging networks. This supplied location information, if erroneous and not verified, can seriously degrade the overall system performance and lead to significant safety issues. A number of location verification protocols and systems have been developed to address this important problem but all have operational constraints and performance limitations due to their requirement for ideal static channel conditions and assumed threat models. In this article, we remove such limitations by designing a neural-network-based LVS (NN-LVS) that can accommodate a priori unknown channel conditions and unknown threat models. Under most channel conditions, the NN-LVS shows a performance improvement of 50%, or more, relative to other LVSs. We also derive a new information-theoretic bound on the total error for an LVS and show how this new bound allows for a useful tradeoff in learning-time versus verification-performance for the NN-LVS. We demonstrate an improved performance for the NN-LVS within the context of vehicular networks using time of arrival measurements of the vehicles' transmitted signals measured at multiple verifying base stations. The work reported here, we believe, paves the way to the actual deployment in real-world conditions of LVSs for emerging vehicular networks.