An Intelligent System-on-a-Chip for a Real-Time Assessment of Fuel Consumption to Promote Eco-Driving

TL;DR

This paper presents a real-time, FPGA-implemented intelligent system that personalizes eco-driving recommendations to reduce fuel consumption and emissions, demonstrating significant potential improvements and suitability for advanced driving assistance systems.

Contribution

It introduces a SOM-based system for personalized eco-driving advice, implemented on FPGA for real-time performance, a novel approach for reducing vehicle emissions.

Findings

Fuel consumption reductions of 9.5% to 31.5% or more.

Successful FPGA implementation with real-time operation.

Personalized recommendations improve eco-driving habits.

Abstract

Pollution that originates from automobiles is a concern in the current world, not only because of global warming, but also due to the harmful effects on people's health and lives. Despite regulations on exhaust gas emissions being applied, minimizing unsuitable driving habits that cause elevated fuel consumption and emissions would achieve further reductions. For that reason, this work proposes a self-organized map (SOM)-based intelligent system in order to provide drivers with eco-driving-intended driving style (DS) recommendations. The development of the DS advisor uses driving data from the Uyanik instrumented car. The system classifies drivers regarding the underlying causes of non-optimal DSs from the eco-driving viewpoint. When compared with other solutions, the main advantage of this approach is the personalization of the recommendations that are provided to motorists, comprising the handling of the pedals and the gearbox, with potential improvements in both fuel consumption and emissions ranging from the 9.5\% to the 31.5\%, or even higher for drivers that are strongly engaged with the system. It was successfully implemented using a field-programmable gate array (FPGA) device of the Xilinx ZynQ programmable system-on-a-chip (PSoC) family. This SOM-based system allows for real-time implementation, state-of-the-art timing performances, and low power consumption, which are suitable for developing advanced driving assistance systems (ADASs).