Overtaking assistant system (OAS) with fuzzy logic method using camera sensor

Anindyaguna, Khalid; Basjaruddin, Noor Cholis; Saefudin, Didin

doi:10.1109/icimece.2016.7910420

Cited by 16 publications

(9 citation statements)

References 3 publications

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“…Diferent types and scenarios of manoeuvring are discussed in literature. 4-10% of collisions have been recorded annually among vehicles on the road due to wrong tactical level operations, including joining or leaving the highway operation [8,9]. It is a challenging manoeuvre that requires a series of careful actions to include maintaining optimal speed while ensuring accident avoidance and passenger comfort [10].…”

Section: Literature Reviewmentioning

confidence: 99%

“…After solving (7), the expression of the maximum permissible velocity is given in (8). Te maximum closing velocity as per the frequency of the velocity command for fast asymptotic interception is given in (9). Finally, the value of the permissible closing velocity component for velocity command is given by solving (8) and ( 9) which yields an expression (10).…”

Section: Rendezvous Guidance Law-basedmentioning

confidence: 99%

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Unified Navigational Approach for an Autonomous Vehicle Joining a Multilane Highway from Ramp

Ghumman

Kunwar

Jabbar

et al. 2022

Journal of Robotics

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Navigating an autonomous vehicle through a circular ramp to join a multilane highway having high-density traffic is a very challenging task. Occupant and vehicle safety are the major considerations while establishing merging scenarios. This article proposes and experimentally implements safe and tactical navigational procedures for merging and leaving the highway scenario using the rendezvous guidance (RG) technique and its modified form. Test scenarios have been established for navigating AVs to merge and leave the highway on a multilane highway with or without the presence of any other vehicle in the zone. In addition, the navigation with enhanced performance of the modified RG algorithm is also established and compared to the conventional RG algorithm. The results indicate that time is reduced considerably by using the modified RG technique. The experimental and simulation results are compared, and their results are the same.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Rendezvous Guidance Law-basedmentioning

confidence: 99%

Unified Navigational Approach for an Autonomous Vehicle Joining a Multilane Highway from Ramp

Ghumman

Kunwar

Jabbar

et al. 2022

Journal of Robotics

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“…Utilizing appropriate membership by fuzzy system inference is increased the effectiveness of the method. This method assumes the adjacent points of pixels then divide them into classes by using membership function [11].…”

Section: Figure 2 Steps Involved In Fuzzy Image Processingmentioning

confidence: 99%

Fuzzy Logic based Edge Detection Method for Image Processing

Alawad

Rahman

Khalifa

et al. 2018

IJECE

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Edge detection is the first step in image recognition systems in a digital image processing. An effective way to resolve many information from an image such depth, curves and its surface is by analyzing its edges, because that can elucidate these characteristic when color, texture, shade or light changes slightly. Thiscan lead to misconception image or vision as it based on faulty method. This work presentsa new fuzzy logic method with an implemention. The objective of this method is to improve the edge detection task. The results are comparable to similar techniques in particular for medical images because it does not take the uncertain part into its account.

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“…The Pixy Cmucam5 sensor as an object detector has image processing capabilities. Pixy CMUcam5 sensor is used as the vision-based sensor to detect a specific pattern on the target object [28], [29]. This sensor is placed in front of the robot with a wide viewing range.…”

Section: Introductionmentioning

confidence: 99%

Implementation of a Camera Sensor Pixy 2 CMUcam5 to A Two Wheeled Robot to Follow Colored Object

Perkasa

Megantoro²,

Winarno

2021

jrc

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This article discusses the design of a colored object follower robot. The colored object used has a simple shape. For the detection process, a wheeled robot that uses sensors based on digital images of Pixy 2. Pixy2 can learn to detect objects that you teach it, just by pressing a button. Additionally, Pixy2 has new algorithms that detect and track lines for use with line-following robots. Pixy2 camera is able to recognize and track all objects whose color has been memorized. In maneuvering, this robot has 2 wheels on the right and left. Movement control is carried out by the Arduino Uno microcontroller board. This robot moves according to the direction of movement of the object. The conclusion obtained in this research is that this wheeled robot can be examined from the left, front and right side objects properly, then it follows the direction of the detected object.

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Overtaking assistant system (OAS) with fuzzy logic method using camera sensor

Cited by 16 publications

References 3 publications

Unified Navigational Approach for an Autonomous Vehicle Joining a Multilane Highway from Ramp

Unified Navigational Approach for an Autonomous Vehicle Joining a Multilane Highway from Ramp

Fuzzy Logic based Edge Detection Method for Image Processing

Implementation of a Camera Sensor Pixy 2 CMUcam5 to A Two Wheeled Robot to Follow Colored Object

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