We evaluate the feasibility of generating formally verified C code that adheres to both functional and non-functional requirements using Large Language Models (LLMs) for three real industrial, automotive safety-critical software modules. We explore the capabilities of ten LLMs and four prompting techniques - Zero-Shot, Zero-Shot Chain-of-Thought, One-Shot, and One-Shot Chain-of-Thought - to generate C programs for the three modules. Functional correctness of generated programs is assessed through functional verification, and adherence to non-functional requirements is evaluated using an industrial static analyzer, along with human evaluation. The results demonstrate that it is feasible for LLMs to generate functionally correct code, with success rates of 540/800, 59/800, and 46/800 for the three modules. Additionally, the generated programs frequently adhere to the defined non-functional requirements. In the cases where the LLM-generated programs did not adhere to the non-functional requirements, deviations typically involve violations of single-read and single-write access patterns or minimal variable scope constraints. These findings highlight the promise and limitations of using LLMs to generate industrial safety-critical C programs, providing insight into improving automated LLM-based program generation in the automotive safety-critical domain.