An FPGA-Based Neuro-Fuzzy Sensor for Personalized Driving Assistance

TL;DR

This paper presents an FPGA-based neuro-fuzzy sensor that recognizes driving styles in real-time, enabling personalized and safe ADAS enhancements with high-speed performance using naturalistic driving data.

Contribution

The work introduces a novel neuro-fuzzy sensor implemented on FPGA that personalizes driving style recognition for ADAS, achieving real-time processing and high accuracy.

Findings

Successfully implemented on Xilinx Zynq FPGA

Achieved 0.53 microseconds in time headway personalization

Fulfills advanced ADAS safety and performance standards

Abstract

Advanced driving-assistance systems (ADAS) are intended to automatize driver tasks, as well as improve driving and vehicle safety. This work proposes an intelligent neuro-fuzzy sensor for driving style (DS) recognition, suitable for ADAS enhancement. The development of the driving style intelligent sensor uses naturalistic driving data from the SHRP2 study, which includes data from a CAN bus, inertial measurement unit, and front radar. The system has been successfully implemented using a field-programmable gate array (FPGA) device of the Xilinx Zynq programmable system-on-chip (PSoC). It can mimic the typical timing parameters of a group of drivers as well as tune these typical parameters to model individual DSs. The neuro-fuzzy intelligent sensor provides high-speed real-time active ADAS implementation and is able to personalize its behavior into safe margins without driver intervention. In particular, the personalization procedure of the time headway (THW) parameter for an ACC in steady car following was developed, achieving a performance of 0.53 microseconds. This performance fulfilled the requirements of cutting-edge active ADAS specifications.