Efficient Traffic Signs Recognition Based on CNN Model for Self-Driving Cars

Author(s):  
Said Gadri ◽  
Nour ElHouda Adouane
Author(s):  
Manjiri Bichkar ◽  
Suyasha Bobhate ◽  
Prof. Sonal Chaudhari

This paper presents an effective solution to detecting traffic signs on road by first classifying the traffic sign images us-ing Convolutional Neural Network (CNN) on the German Traffic Sign Recognition Benchmark (GTSRB)[1] and then detecting the images of Indian Traffic Signs using the Indian Dataset which will be used as testing dataset while building classification model. Therefore this system helps electric cars or self driving cars to recognise the traffic signs efficiently and correctly. The system involves two parts, detection of traffic signs from the environment and classification based on CNN thereby recognising the traffic sign. The classification involves building a CNN model of different filters of dimensions 3 × 3, 5 × 5, 9 × 9, 13 × 13, 15 × 15,19 × 19, 23 × 23, 25 × 25 and 31 ×31 from which the most efficient filter is chosen for further classifying the image detected. The detection involves detecting the traffic sign using YOLO v3-v4 and BLOB detection. Transfer Learning is used for using the trained model for detecting Indian traffic sign images.


Author(s):  
Akash gupta ◽  
Rahat Ali ◽  
Abhay Pratap Singh ◽  
P.Raja Kumar

Nowdays we are witnessing the technology transforming everything the way we used to do things and how the automobile industry is transforming itself with the use of technology IOT,Artificial intelligence,Machine learning.Companies shifting its products and its utilities in diferent way and they now want to acquire and introduce level-5 autonomous to future generation and big automobile companies are trying to achieve autonomous vechicles and we have researhed about the model that will help in assisting autonomous vechicles and trying to achieve that.We will develop this model with help of technologies like Artificial intelligence,Machine learning,Deep learning.Autonomous vehcicles will become a reality on our roads in the near future. However, the absence of a human driver requires technical solutions for a range of issues, and these are still being developed and optimised. It is a great contribution for the automotive industry which is going towards innovation and economic growth. If we talking about some past decade the momentum of new research and the world is now at the very advanced stage of technological revolution. “Autonomous-driving” vehicles. The term Self-driving cars, autonomous car, or the driverless cars have different name with common objective. The main focus is to keep the human being out of the vehicle control loop and to relieve them from the task of driving. Everyday automotive technology researchers solve challenges. In the future, without human assistance, robots will produce autonomous vehicles using IoT technology based on customer needs and prefer that these vehicles are more secure and comfortable in mobility systems such as the movement of people or goods. We will build a deep neural network model that can classify traffic signs present in the image into different categories. With this model, we are able to read and understand traffic signs which are a very important task for all autonomous vehicles .This model we have tested it and resulted in 95% accuracy.


2007 ◽  
Author(s):  
Cristina Vargas ◽  
Sergio Moreno-Rios ◽  
Candida Castro ◽  
Geoffrey Underwood

2004 ◽  
Author(s):  
Kate I. Podany ◽  
Michael S. Wogalter ◽  
Christopher B. Mayhorn

2019 ◽  
Vol 12 (1) ◽  
pp. 47-60
Author(s):  
László Kota

The artificial intelligence undergoes an enormous development since its appearance in the fifties. The computing power has grown exponentially since then, enabling the use of artificial intelligence applications in different areas. Since then, artificial intelligence applications are not only present in the industry, but they have slowly conquered households as well. Their use in logistics is becoming more and more widespread, just think of self-driving cars and trucks. In this paper, the author attempts to summarize and present the artificial intelligence logistical applications, its development and impact on logistics.


2018 ◽  
Vol 58 (1) ◽  
pp. 53-60
Author(s):  
Bartosz Czarnecki

Abstract The paper discusses the spatial consequences of the widespread use of self-driving cars and the resulting changes in the structure of urban areas. Analysing present knowledge on the technology, functionality and future forms of organisation of mobility with this type of means of transportation, conclusions are presented concerning the expected changes in the organisation of space in urban areas. The main achievement of the investigation is an outline of the fields of future research on the spatial consequences of a transportation system with a large share of self-driving cars.


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