Future-proof and (semi-) autonomous driving: a reallocation of tasks
Modern embedded processors now provide sufficient performance for most traditional applications inside the car. However, new functions – including image processing and natural-looking human-machine interfaces (HMI) – have increasingly challenging requirements. With HMIs, even simpler systems, such as a warning tone generator, make technically complex demands on simple ECUs. An example is a signal sent to an ECU for audio amplification, so that the HMI can make passengers aware of a source of danger and its location. Image and speech recognition algorithms use neural networks and deep learning, which was still in the supercomputer domain until recently. The requirement for new solutions is not even being driven by the need for more computing power – instead, it is the sheer size of the databases shared by the various ECUs. Networks like CAN were never designed for the shared usage of camera or microphone data and this can prove too much for microcontrollers that only have integrated memory. Extended diagnostic functions also impose increasing memory and communication tasks on ECUs whose initial function was relatively simple.
Looked at from another perspective, it is becoming more difficult to achieve semiconductor cost reductions through technological advances alone. Although semiconductor makers, like Renesas with its R-Car product, provide highly scalable system-on-chip (SoC) families, certain minimum costs for a single ECU cannot really be reduced any further. After all, they do still need a package that includes connectors, power supply and a circuit board. We need a different way of thinking if we are to continue getting more functionality for the same price.