Development of a Vehicle for Driving with Convolutional Neural Network (original) (raw)
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Advanced Driver Assistance System using Convolutional Neural Network
2021
Road sign recognition is an essential task in driving process to drive safely and to avoid accidents. Road sign recognition is not a simple task as there are many unfavorable factors such as bad weather, illumination, physical damage etc. The purpose of Road sign is to inform drivers and autonomous vehicles about current state of road and also provide them other important data for navigation. This paper aims to build Convolutional neural network (CNN) model to recognize road signs and to inform the drivers in advance for safe driving. The advantage of using Convolutional neural network (CNN) is its potential to build an internal representation of two-dimensional images. This enables the model to learn scale and position variant structures in the data, which is required when working with images. The proposed system achieves an accuracy of 87%.
Image Recognition by Using a Convolutional Neural Network to Identify Objects for Driverless Car
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
The concept of the paper was inspired by the recent surge in the automated car industry. The designed car was capable of detecting the road signals and taking the right and left turns accordingly. Object detection is a key ability required by most computer used in automated vehicles. The latest research in this area has been making great progress in many directions. Object detection and tracking has a variety of uses, our paper explain how to use convolutional neural network for object detection in autonomous vehicles. Automatic car always has the potential to solve traffic problems with the help of Convolution Neural Network (CNN). However, in the current scenario complete autonomy is still to be achieved. Although today's CNN have brought us closer to autonomy than ever before. CNN contain artificial neurons which are trained using preset rules and these rules determine whether it will provide an output or not when given a set of inputs. CNN will analyze various road footages, which include various scenarios such as collisions, empty roads, traffic, etc. CNN will analyze and send appropriate instructions to the car such as brake, accelerate, slow down, etc.
1/10TH Scale Autonomous Vehicle Based on Convolutional Neural Network
International Journal on Smart Sensing and Intelligent Systems, 2020
A vehicle capable of using sensors to detect and control its driving actions is called an autonomous vehicle. The development of autonomous vehicles caters to many application areas in the technological advancement of society. This research paper shows a demonstration and implementation of an autonomous vehicle based on a convolutional neural network. The vehicle uses a 1/10th scale RC car as its primary base for the system control with the camera as its primary input. For the computing platform, a Raspberry Pi 4 microprocessor board is used. To enhance the capabilities, the ultrasonic sensor has been implemented in the system as well. The unique aspect of this project is the system design, the CAD modeling, and the track built used to train and test the self-driving capability of the car. The CNN model and the software algorithm also are exclusive to this research project. This research has potential in a variety of application areas in education and also for robotics and autonomou...
Building of Informatics, Technology and Science (BITS), 2022
The main justification for implementing an Autonomous Vehicle (AV) system in the real world is the safety aspect of driving, because if there is an error in driving then the error will become a gap that can threaten the safety of the driver himself and other drivers, therefore an AV system is made to reduce driver errors. in driving. The aim of this research is to implement one of the parts of the AV system, that is object recognition, and in this study, we also conduct an experiment with simulating the object recognition feature that has been implemented in order to get more concrete results. Architectural object recognition is designed to extract key features from traffic sign images, the traffic sign detection uses the customized Convolutional Neural Networks (CNNs) architecture. After the architectural has been implemented, training will be carried out using Custom Traffic Sign Dataset and experiments will also be conducted to simulate object recognition by applying ROS2 as a car robotic system that represents a car's functionality system in the real world. the results of this study for the implementation of the modified CNNs architecture is 99.96% and the results of the simulations carried out show that the prototype can detect traffic signs objects with a distance of 10m.
Use of Machine Learning in Automobile Industry to Improve Safety Using CNN
IJRASET, 2021
Vision-based vehicle steering system cars can have three main roles: 1) road access; 2) an obstacle to find; and 3) signal recognition. The first two have already been taught many years and there have been many positive results, but a sign of traffic recognition is a less readable field. Road signs provide drivers with the most important information on the road, to do driving is safe and easy. We think road signs should play the same role of private cars. The color and shape are very different from the natural environment. The algorithm described in this paper uses this feature. It has two main parts. The first, to find, uses color range to separate image analysis and shapes to get symptoms. The second, in stages, uses the neural network. Some effects from natural forums are shown. On the other hand, the algorithm works to detect other types of marks can tell a moving robot to perform a specific task that place.
Vehicle Control Using Raspberry pi and Image Processing
Intelligent Communication, Control and Devices, 2018
The objective of the proposed work is to implement the available technique to detect the stop board and red traffic signal for an autonomous car that takes action according to traffic signal with the help of raspberry pi3 board. The system also uses ultrasonic sensor for distance measurement for the purpose of speed control of vehicle to avoid collision with ahead vehicle. Rpi camera module is ued for signboard detection and ultrasonic sensors are used to get the distance information from the real world.The proposed system will get the image of the real world from the camera and then masking and contour techniques are used to detect the red signals of the traffic and To determine the traffic board signs like stop board system will use haar cascade technique to determine the stop words.So car will be able to take action and reduces the chances of human errors like driver mistakes that results road accidents .The coding for this whole system is in python and for image processing opencv is used that is much efficient as compare to the matlab .Ultrasonic sensor is used for the obstacle detection in place of camera because distance finding from the camera is more complex and computational as compare to the ultrasonic sensor. Ultrasonic sensor directly gives the obstacle distance infront of it without more complex computations.
The Design of Preventive Automated Driving Systems Based on Convolutional Neural Network
Electronics
As automated vehicles have been considered one of the important trends in intelligent transportation systems, various research is being conducted to enhance their safety. In particular, the importance of technologies for the design of preventive automated driving systems, such as detection of surrounding objects and estimation of distance between vehicles. Object detection is mainly performed through cameras and LiDAR, but due to the cost and limits of LiDAR’s recognition distance, the need to improve Camera recognition technique, which is relatively convenient for commercialization, is increasing. This study learned convolutional neural network (CNN)-based faster regions with CNN (Faster R-CNN) and You Only Look Once (YOLO) V2 to improve the recognition techniques of vehicle-mounted monocular cameras for the design of preventive automated driving systems, recognizing surrounding vehicles in black box highway driving videos and estimating distances from surrounding vehicles through ...
Autonomous Vehicle Control using Image Processing
International Journal for Research in Applied Science & Engineering Technology, 2021
A significant perspective related with vehicles is their speed. A faster vehicle encourages us to arrive at our destination in lesser time, sparing our valuable time. In any case, tragically, we have been seeing the ascent in vehicular mishaps because of uncontrolled speeding by the drivers. The traffic signs alone cannot ensure the safety of vehicles since it is dependent upon the driver to adhere to the directions. Likewise, there is the situation of human-mistake where the driver essentially neglects to pay special mind to the traffic signs. This paper focuses on the road traffic sign detection systems which help in informing the intelligent vehicle about the possible road conditions ahead and be cautious about it with the help of Image processing. A module which consists of a Raspberry Pi or USB camera with a wide view and a simple processor is installed on the vehicle. The developed system works with three different stages: image pre-processing, detection, and recognition. The entire developed system is programmed using Python incorporated with OpenCV library and implemented using open source hardware platform and open-source software environment.
Traffic Sign Detection Using Convolutional Neural Network on Autonomous Vehicle System
2020
Artificial intelligence (AI) gives autonomous vehicle ability to drive by itself. It is believed that AI can reduce the risk of accidents caused by human error. One example of AI implementation on autonomous vehicle is visual system for detecting traffic sign. Convolutional Neural Network (CNN), a part of deep learning method, is used in this research to build traffic sign detection model for Indonesia. However, dataset is needed by this method to perform training. The unavailability of Indonesian traffic sign dataset may become challenge in building the model due to the distinct characteristics of traffic sign among countries. The proposed solution is to feed Extended Malaysian Traffic Sign Dataset (EMTD) into CNN to produce the detection model by reason that it contains traffic signs that are similar to Indonesian traffic signs. The solution adapts Faster R-CNN model which has been developed for detecting foreign traffic sign. The CNN model is coded with Python 3 using Keras-Tenso...
Autonomous Vehicle Using Machine Learning and Computer Vision
In this proposed work, a prototype of autonomous vehicle is designed and developed which uses raspberry pi as the core functioning and uses OpenCV and machine learning technology. The vehicle uses the core processing unit as Raspberry pi, which is interfaced with the Pi camera module, which feeds the captured images to the raspberry pi for image processing. Based on which detection like road lanes, traffic signals, obstacles are done and commands are sent to arduino to operate the car.