Beamforming techniques are essential to compensate for severe path loss in millimeter-wave (mmWave) communications. These techniques adopt large antenna arrays and formulate narrow beams to obtain satisfactory received powers. However, performing accurate beam alignment over such narrow beams for efficient link configuration by traditional beam selection approaches, mainly relied on channel state information and exhaustive search, typically impose significant latency and computing overheads, which is often infeasible in vehicle-to-vehicle (V2V) communications like highly dynamic scenarios. In contrast, utilizing out-of-band contextual information, such as vehicular position information, is a potential alternative to reduce such overheads. This paper proposes a solution that utilizes deep learning to predict the optimal beams for vehicular communication at 60 GHz. By analyzing vehicular position information, the solution can identify the beams that provide sufficient mmWave received powers, ensuring the best line-of-sight links for vehicle-to-vehicle (V2V) communications. The proposed solution was tested on real-world measured mmWave sensing and communication datasets, and the results show that it can achieve an average of 84.58% of received power of link status, making it a promising solution for beamforming in mmWave enabled V2V communications.