- Keywords: Microcontrollers, Neural Networks, LiIon Battery, Electric Vehicles, Machine Learning, embedded software, CAN networks
- TL;DR: Can intrusion detection and battery charge prediction use cases automatically mapped on Automotive grade MCUs
- Abstract: Neural networks and Machine Learning in general, represent today one of the greatest expectations for the realization of models that can serve to determine the behavior and operation of different physical systems. Undoubtedly the calculation resources necessary for the training and the realization of the model are great especially if linked to the amount of data needed to detect the salient parameters of the model. At the same time, the models so obtained can be integrated on embedded systems, thanks to TinyML technologies, allowing to work exactly where the physical phenomena to analyze happen. In the consumer and industrial world these technologies have taken hold, and are also watched with interest by other sectors such as the automotiveworld. In this articlewe present a framework for the implementation of models based on neural networks on automotive family microprocessors, demonstrating their efficiency in two typical applications of the vehicle world: intrusion detection on the CAN bus communication network and the determination of the residual capacity of batteries for electric vehicles.