A Hardware/Software Extreme Learning Machine Solution for Improved Ride Comfort in Automobiles

Abstract

Automotive ride comfort has become an important research topic in recent years due to the increasing level of automation in currently produced cars. These premises also apply to manned cars. In this work, a hybrid hardware/software extreme learning machine for improved ride comfort in automobiles is proposed. This system is based on a single-chip implementation able to provide real-time information about the level of ride comfort by classifying driving data into several comfort classes. To develop this system, unsupervised hierarchical clustering analysis (HCA) and supervised extreme learning machine (ELM) have been used jointly, to enhance the overall performance of the entire system, reaching classification success rates of up to 95%. This approach has been implemented on a Xilinx Zynq-7000 programmable system-on-chip. This chip is able to process data in real time and to identify the comfort class, achieving low latency marks and high operational frequencies due to its DSP-based implementation. These performance and accuracy marks, together with its low power consumption make this development suitable for novel practical implementations in current production cars.