5G applications have become increasingly popular in recent years as the spread of fifth-generation (5G) network deployment has grown. For vehicular networks, mmWave band signals have been well studied and used for communication and sensing. In this work, we propose a new dynamic ray tracing algorithm that exploits spatial and temporal coherence. We evaluate the performance by comparing the results on typical vehicular communication scenarios with GEMV^2, which uses a combination of deterministic and stochastic models, and WinProp, which utilizes the deterministic model for simulations with given environment information. We also compare the performance of our algorithm on complex, urban models and observe a reduction in computation time by 36% compared to GEMV^2 and by 30% compared to WinProp, while maintaining similar prediction accuracy.