With the advancement of the automotive industry, more sophisticated systems are being incorporated into vehicles to enhance performance, safety, and other essential features. As a result, the software to control these complex systems continue to grow and complex. There is a need to test all these software thoroughly in a systematic way to ensure the correctness of implementation and functionality. Till now, test cases for the automotive software unit feature are being created manually and turns out to be inefficient and time consuming. There is a pressing need, therefore, to generate the testcases automatically based on the test requirements. To address this challenge, this research illustrates the use of large language models (LLMs) in automated test case generation for MIL/SIL/PIL platform. Recently, LLMs have been effectively applied to address challenges in natural language understanding, text generation, code generation, and more. Following this concept, our approach is to first create a structured document from the test requirements, utilizing the Llama LLM model, which is guided by prompt engineering. This structured document then used to create the testcases automatically using scripts. The performance of our approach is demonstrated with several examples including both static and dynamic scenarios. Additionally, we show the results on the publicly available test specification document from the Ministry of Road Transport and Highways, India. We present a comparative analysis of automatically versus manually generated test cases, showing that automation reduces effort by approximately 67%, completing tasks in one-third the time required for manual creation. Our approach of using LLM is seen to be more efficient generating test cases in a faster way for both static and dynamic scenarios. Further, our approach produces consistent and reproducible results managing different paraphrasing of the same test requirements.