A versatile hardware/software platform for personalized driver assistance based on online sequential extreme learning machines

Abstract

In the present scenario of technological breakthroughs in the automotive industry, machine learning is greatly contributing to the development of safer and more comfortable vehicles. In particular, personalization of the driving experience using machine learning is an innovative trend that comprises the development of both customized driver assistance systems and in-cabin comfort features. In this work, a versatile hardware/software platform for personalized driver assistance, using online sequential extreme learning machines (OS-ELM), is presented. The system, based on a programmable system-on-chip (SoC), is able to recognize the driver and personalize the behavior of the car. The platform provides high speed, small size, efficient power consumption, and true capability for real-time adaptation (i.e., on-chip self-learning). In addition, due to the plasticity and scalability of the OS-ELM algorithm and the programmable nature of the SoC, this solution is flexible enough to cope with the incremental changes that the new generation of vehicles are demanding. The implementation details of a system, suitable for current levels of driving automation, are provided.