Autonomous vehicles (AV), more popularly called self-driving cars, have been a technological challenge that is yet to be fully conquered by any company. Over the last decade, several technological advancements have led to the miniaturization of hardware to an acceptable form which has led to the development and testing of Deep Learning algorithms for various sub-applications such as street sign classification, lane classification, etc. The overarching goal is full autonomy also called L5.

Level 0: No driving automation Level 1: Driver assistance Level 2: Partial driving automation Level 3: Conditional driving automation Level 4: High driving automation Level 5: Full driving automation

Autonomous vehicle hardware development platforms, whether hardware in loop test setup or a car fitted with sensors such as cameras, LiDars, Radars, GPS, etc., often have a hybrid compute architecture. The safety software components are run on ASIL D-certified hardware, such as an Infineon Aurix microcontroller, while the Machine Learning algorithm is run on compute-intensive hardware supporting GPUs/NPUs, etc. A software layer sandwiched between the operating system and algorithm applications is called middleware.