Design, Modelling, and Implementation of a Fuzzy Controller for an Intelligent Road Signaling System

Abstract

Crossing points are not always 100% visible for drivers due to different factors (e.g., poor road maintenance, occlusion of vertical signs, and adverse weather conditions). USA estimated in 2015 the number of traffic accidents involving pedestrians and vehicles in 70,000 of whom 5,376 resulted in deceased people. To contribute in this field, this paper presents the design, implementation, and testing of a smart prototype system applied to pedestrian crossings-not regulated by semaphores-which try to reduce the accident rate on roads. The hardware and software system consists of a set of autonomous, intelligent, and wireless low-cost devices that generate a visual warning barrier perceived by drivers from a suitable distance when pedestrians traverse a crosswalk. In this way, drivers can reduce the speed of their vehicles and stop safely. The system's intelligence is carried out by a fuzzy controller that performs sensory fusion at both low level and high level with various types of sensors from local and neighboring devices. The tests conducted have determined an average success of 94.64% and a precision of 100%, thus corresponding with a very good test according to a ROC analysis. As a result, the system proposed has been patented and extended to international PCT.

Walking Secure: Safe Routing Planning Algorithm and Pedestrian’s Crossing Intention Detector Based on Fuzzy Logic App

“…Additional functionality has been included to add value to the app. This can communicate with a smart crosswalk as the one described in [ 25 ] to alert drivers about the presence of pedestrians with the intention to cross a zebra crossing. The interaction between the app and the intelligent crosswalk is managed by a gateway that can communicate with the app via Bluetooth and with the nodes of the intelligent crosswalk via Wi-Fi ( Figure 6 ).…”

“…To sum up, the novelties proposed in this manuscript regarding the state of the art are (i) the development of a fuzzy logic approach with low computational cost to detect the pedestrian’s crossing intention through the own smartphones’ built-in sensors; (ii) the development of an optimization algorithm for calculating, tracing and guiding people through safe routes within a city considering pedestrian areas such as crossings, streets and walkways; (iii) in case of detecting the pedestrian’s intention to cross, the app has the additional capability to communicate with a luminous intelligent crosswalk previously developed by this research team [ 25 ]. As a result, such an intelligent crosswalk creates a light barrier to alert drivers so they can safely stop their vehicles.…”

Analysis of Machine Learning Techniques Applied to Sensory Detection of Vehicles in Intelligent Crosswalks

“…The HB100 (AgilSense, Ang Mo Kio, Singapore) is the model used for implementation. Further technical details on the sensors used in this implementation can be found in [ 7 , 8 ].…”

“…An important area of intelligent cities is the intelligent transport system (ITS), which is a set of technological solutions designed to coordinate, improve, and increase transport safety on public roads [ 6 ]. Within this area, an intelligent road-marking system was first developed to reduce the rate of accidents around pedestrian crossings—a prior work from the same team [ 7 , 8 ]. The system has the ability to distinguish vehicles from pedestrians by means of a fuzzy classifier that performs sensor fusion using the following elements: (i) ultrasound sensors that detect people passing through the pedestrian crossing; (ii) magnetic field sensors to detect vehicles near a pedestrian crossing; and (iii) radar sensors to detect vehicles approaching the pedestrian crossing.…”

A Novel Fuzzy Model Predictive Control of a Gas Turbine in the Combined Cycle Unit

“…With the flourishing development of control methods in recent years, some advanced control algorithms have emerged continuously and obtained great achievements, such as fuzzy control [9][10][11], predictive control [12][13][14][15], and neural network control [16][17][18][19]. In all of those methods, predictive control is developed in the industrial process directly [20].…”