Approaches for Integrating Deep Learning Models for Inference Using AUTOSAR in ECU

Deep Learning (DL) models are typically deployed on high-computation platforms like GPUs and FPGAs in cloud environments, leading to high latency and network dependency. In contrast, performing deep learning inference on microcontrollers in automotive applications reduces latency, minimizes network dependency, enhances security, and offers a cost-effective solution. This paper investigates the integration of deep learning models into AUTOSAR-compliant C-code for automotive Electronic Control Units (ECUs). Adhering to AUTOSAR standards guarantees compatibility, scalability, maintainability, and efficient resource management. The study emphasizes toolchains that transform deep learning models, initially trained and developed in Python with frameworks such as TensorFlow or PyTorch, or using other tools like MATLAB, into high-quality AUTOSAR-compliant C code, tailored for specific compilers and hardware optimizations. Upon deployment, the deep learning software module within the ECU must satisfy rigorous real-time system requirements. In this paper, we demonstrate the integration of a deep learning model into an ECU, emphasizing automation and seamless incorporation into existing workflows. The workflow setup can be extended to various deep learning models, but we use a simple neural network for estimating the battery's state of health to illustrate the process. The model is trained in Python, quantized for ECU deployment, and converted to AUTOSAR-compliant $\mathbf{C}$-code through three methods: manual implementation with TargetLink, Simulink combined with the Deep Learning Toolbox, and a hybrid approach integrating Simulink-generated code into existing TargetLink logic using Sfunctions. This comparison focuses on fixed-point scaling and hardware optimization to ensure code efficiency, reliability, and correctness, verified through static code analysis and unit testing. Functional tests in a Software-in-the-Loop (SIL) environment validate system performance and accuracy by comparing results to a physics-based model. Hardware-in-the-Loop (HIL) testing assesses real-time behavior and robustness, ensuring reliable performance with actual hardware components under real-world conditions.