scholarly journals Hardware Simulation of Rear-End Collision Avoidance System Based on Fuzzy Logic

2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Noor Cholis Basjaruddin ◽  
Didin Saefudin ◽  
Anggun Pancawati
Keyword(s):  
Fuzzy Logic ◽  
Collision Avoidance ◽  
Fuzzy Inference ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Inference System ◽  
Fuzzy Logic Algorithm ◽  
Pile Up ◽  
Assistance Systems ◽  
Collision Avoidance System

Rear-end collisions are the most common type of traffc accident. On the highway, a real-end collision may involve more than two vehicles and cause a pile-up or chain-reaction crash. Referring to data released by the Australian Capital Territory (ACT), rear-end  collisions which occurred throughout 2010 constituted as much as 43.65% of all collisions. In most cases, these rear-end collisions are caused by inattentive drivers, adverse road conditions and poor following distance. The Rear-end Collision Avoidance System (RCAS) is a device to help drivers to avoid rear-end collisions. The RCAS is a subsystem of Advanced Driver Assistance Systems (ADASs) and became an important part of the driverless car. This paper discusses a hardware simulation of a RCAS based on fuzzy logic using a remote control car. The Mamdani method was used as a fuzzy inference system and realized by using the Arduiono Uno microcontroller system. Simulation results showed that the fuzzy logic algorithm of RCAS can work as designed.

scholarly journals Vessel Multi-Parametric Collision Avoidance Decision Model: Fuzzy Approach

2021 ◽  
Vol 9 (1) ◽  
pp. 49
Author(s):  
Tanja Brcko ◽  
Andrej Androjna ◽  
Jure Srše ◽  
Renata Boć
Keyword(s):  
Fuzzy Logic ◽  
Collision Avoidance ◽  
Decision Model ◽  
Fuzzy Inference ◽  
Weather Conditions ◽  
Target Vessel ◽  
Planning Tool ◽  
Inference System ◽  
International Regulations ◽  
Input Variables

The application of fuzzy logic is an effective approach to a variety of circumstances, including solutions to maritime anti-collision problems. The article presents an upgrade of the radar navigation system, in particular, its collision avoidance planning tool, using a decision model that combines dynamic parameters into one decision—the collision avoidance course. In this paper, a multi-parametric decision model based on fuzzy logic is proposed. The model calculates course alteration in a collision avoidance situation. First, the model collects input data of the target vessel and assesses the collision risk. Using time delay, four parameters are calculated for further processing as input variables for a fuzzy inference system. Then, the fuzzy logic method is used to calculate the course alteration, which considers the vessel’s safety domain and International Regulations for Preventing Collisions at Sea (COLREGs). The special feature of the decision model is its tuning with the results of the database of correct solutions obtained with the manual radar plotting method. The validation was carried out with six selected cases simulating encounters with the target vessel in the open sea from different angles and at any visibility. The results of the case studies have shown that the decision model computes well in situations where the own vessel is in a give-way position. In addition, the model provides good results in situations when the target vessel violates COLREG rules. The collision avoidance planning tool can be automated and serve as a basis for further implementation of a model that considers the manoeuvrability of the vessels, weather conditions, and multi-vessel encounter situations.

A Fuzzy Lane Tracking System for Driver Assistance

2006 ◽  
Author(s):  
Annalisa Milella ◽  
Giulio Reina
Keyword(s):  
Fuzzy Logic ◽  
Autonomous Vehicles ◽  
Tracking System ◽  
Critical Role ◽  
Lane Detection ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Inference System ◽  
Driver Support Systems ◽  
Lane Position

In the last few years, driver-assistance systems are increasingly being investigated in automotive field to provide a higher degree of comfort and safety. Lane position determination plays a critical role toward the development of autonomous and computer-aided driving. This paper presents an accurate and robust method for detecting lateral road marking with applications in autonomous vehicles and driver support systems. Much like other lane detection systems, ours is based on computer vision and Hough transform. Our approach, however, is unique in that it combines geometrical and intensity information of the image, based on a fuzzy logic inference system implementing in-depth understanding of different driving and environmental conditions. We call it Fuzzy Logic lane (FLane) tracking system. Details of the main components of the FLane module are presented along with experimental results obtained under varying lighting and road conditions. It is shown that the proposed method is reliable and effective in detecting road border and can be successfully employed for driver assistance.

scholarly journals Interval Type-2 Fuzzy Inference System Based on Closest Point of Approach for Collision Avoidance between Ships

2020 ◽  
Vol 10 (11) ◽  
pp. 3919 ◽  
Author(s):  
Sung Wook Ohn ◽  
Ho Namgung
Keyword(s):  
Fuzzy Logic ◽  
Collision Avoidance ◽  
Membership Function ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Collision Risk ◽  
Inference System ◽  
Interval Type

According to International Regulations for Preventing Collision at Sea, collision avoidance started from assessing the collision risk. In particular, the radar was mentioned as suitable equipment for observation and analysis of the collision risk. Thus, many researches have been conducted by utilizing the radar. Fuzzy Inference System based on Type-1 Fuzzy Logic (T1FIS) using Distance to Closest Point of Approach ( D C P A ) and Time to Closest Point of Approach ( T C P A ) computed via the radar has been largely used for assessing the collision risk. However, the T1FIS had significant limitations on the membership function not including linguistic and numerical uncertainties. In order to solve the issue, we developed the Fuzzy Inference System based on Interval Type-2 Fuzzy Logic (IT2FIS) as follows: (i) the T1FIS was selected among proposed methods based on the type-1 fuzzy logic; (ii) we extended the T1FIS into the IT2FIS by gradually increasing the Footprint of Uncertainty (FOU) size taking into consideration symmetry, and (iii) numerical simulations were conducted for performance validation. As a result, the IT2FIS using the FOU size “±5%” (i.e., interval 10% between upper membership function and lower membership function) not only computed the appropriate and linear collision risk index smoothly until near-collision situation but also help to overcome uncertainties that exist in real navigation environments.

scholarly journals PENENTUAN KECEPATAN PUTAR KIPAS ANGIN DAN INTENSITAS LAMPU DENGAN FUZZY LOGIC MAMDANI

2020 ◽  
Vol 3 (2) ◽  
Author(s):  
Chyquitha Danuputri
Keyword(s):  
Fuzzy Logic ◽  
Light Intensity ◽  
Intelligent Systems ◽  
Smart Home ◽  
Room Temperature ◽  
Fuzzy Inference ◽  
Inference System ◽  
Fuzzy Logic Algorithm ◽  
Temperature And Humidity ◽  
The Republic

<p><em>Intelligent systems are one of the most important branches of the computer world. Computers are expected to be able to solve various problems in the real world, not just a tool for doing calculations. To make this system, algorithms are needed that are in accordance with the problems faced so that they can solve or produce the decisions needed to solve these problems appropriately. Mamdani fuzzy logic algorithm is one of the algorithms that can be applied in intelligent systems. Fuzzzy mamdani algorithm, is one part of the Fuzzy Inference System which is useful for making the best conclusion or decision in an uncertain problem. This research focuses on the calculation of the fuzzy logic algorithm in providing answers to the uncertainties found in smart home systems used to control the speed of a fan and lights, while the factors that become uncertain in controlling a fan are room temperature and humidity and For lamps, they have a factor of light intensity and time of the region, for these factors, the researchers use the Humanity Guide Hygiene standard reference for humidity and the Regulation of the Minister of Health of the Republic of Indonesia Number 1077 / Menkes / Per / V / 2011 concerning Guidelines for Air Sanitation in Home Spaces. Through this research, it can be seen that using the mamdani fuzzy logic algorithm can provide a result in the form of a decision to determine how fast a fan should rotate based on the temperature and humidity factors in the room as well as the level of light intensity that the lights must emit.</em><strong><em></em></strong></p>

Hardware Simulation of Advanced Driver Assistance Systems Based on Fuzzy Logic

2018 ◽  
Vol 11 (1) ◽  
pp. 24 ◽  
Author(s):  
Noor Cholis Basjaruddin ◽  
Dodi Budiman Margana ◽  
Kuspriyanto Kuspriyanto ◽  
Raka Rinaldi ◽  
Suhendar Suhendar
Keyword(s):  
Fuzzy Logic ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Advanced Driver Assistance Systems ◽  
Assistance Systems

scholarly journals Analysis of The Impact of Fuzzy Logic Algorithm On Handover Decision in A Cellular Network

2017 ◽  
Vol 5 (5) ◽  
pp. 46-62 ◽  
Author(s):  
Emily Teresa Nyambati ◽  
Vitalice K. Oduol
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Intelligent Systems ◽  
Fuzzy Inference ◽  
Inference System ◽  
Handover Decision ◽  
Fuzzy Logic Algorithm ◽  
Gsm Network ◽  
Work Done ◽  
The Impact

Fuzzy logic is one of the intelligent systems that can be used to develop algorithms for handover. For success in handing over, the decision-making process is crucial and thus should be highly considered. The performance of fixed parameters is not okay in the changing cellular system environments. The work done on this paper aims to analyse the impact of utilising the fuzzy logic system for handover decision making considering the Global System for Mobile communication (GSM) network. The results from the different simulations show that the need to handover varies depending on the input(s) to the Fuzzy Inference System (FIS). By increasing the number of data, thus the criteria parameters used in the algorithm, an Optimised Handover Decision (OHOD) is realised.

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