Analysis of traffic signs and traffic lights for autonomously driven vehicles

Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e ComputadoresOs Advanced Driver Assistance Systems(ADAS) estão relacionados a vários sistemas nos veículos que se destinam a melhorar a segurança do tráfego rodoviário, ajudando os condutores a terem melhor consciência da estrada e dos seus perigos inerentes, bem como de outros motoristas nas proximidades. A deteção e o reconhecimento de sinais de trânsito são parte integrante do ADAS. Os sinais de trânsito fornecem informações sobre as regras de trânsito, condições das estradas, direções de rotas e auxiliam os motoristas para uma condução segura. Isto garante que o limite de velocidade atual e outros sinais de trânsito sejam exibidos para o motorista continuamente. O projeto de reconhecimento de sinais de trânsito tem sido um problema desafiador por muitos anos e, portanto, tornou-se um tópico de pesquisa importante e ativo na área de sistemas de transporte inteligentes. Esta tecnologia está a ser desenvolvida por uma variedade de fornecedores automóveis. Esta pode usar técnicas de processamento de imagem para detetar sinais de trânsito. Uma abordagem do problema de deteção e reconhecimento de sinais/semáforos usando Su pervised Learning é apresentada nesta dissertação para dois cenários diferentes. Nesta disser tação, para cada objetivo, são apresentadas duas abordagens diferentes de Supervised Learning, bem como um estudo estendido dos hiperparâmetros. Para os dois primeiros objetivos, foram de senvolvidas as abordagens para um robô produzido pela equipa de Condução Autónoma do Labo ratório de Automação e Robótica. O robô deve detetar e classificar corretamente o sinal/semáforo de trânsito apresentado. O terceiro objetivo é para a via pública e a abordagem desenvolvida deve detetar e classificar corretamente o sinal/semáforo de trânsito apresentado no conjunto de dados restritos.Advanced Driver Assistance Systems (ADAS) relate to various in-vehicle systems that are intended to improve road traffic safety by supporting and improve drivers awareness of the road and its dangers as well as other drivers in the vicinity. Traffic sign detection and recognition is part of ADAS. Traffic signs give knowledge about the traffic rules, road conditions, route directions and assist drivers for safe driving. This ensures that the current speed limit and other road signs are displayed to the driver on an ongoing basis. The design of traffic sign recognition has been a challenging problem for many years and therefore became an important and active research topic in the area of intelligent transport systems. This technology is being developed by a variety of automotive suppliers. Typically it uses classical image processing techniques to detect traffic signs. An approach to the problem of Traffic Sign/Light detection and recognition using Supervised Learning is presented in this dissertation for two different scenarios. Two different Supervised Learning approaches are presented for each objective as well as an extended hyperparameter study. For the first two objectives, the approaches were developed for a robot produced by the Autonomous Driving team from the Laboratório de Automação e Robótica fom University of Minho. The robot must correctly detect and classify the presented Traffic Sign/Light. The third objective is for the public road and the developed approach must correctly detect and classify the presented Traffic Sign/Light in the restrained dataset

A STUDY ON AUTONOMOUS DRIVING ADAPTIVE SIMULATION SYSTEM USING DEEP LEARNING MODEL YOLOV3

For the safety of autonomous vehicles, it is not necessary that the human driver does not have much trouble detecting other vehicles and maintaining a certain distance between them, but in the case of autonomous vehicles, that's not an easy task. The problem of detecting and recognizing the front state of autonomous vehicles is known as object detection by Yolov3 bounding boxes. Therefore, we propose this study to avoid accidents before they occur due to autonomous driving on the road and for a better future.  Our purpose in this study is to put autonomous vehicles on the road in practice using Simulink Matlab, and it is a reflection on the ability of autonomous vehicles to ensure curve road safety And to quickly determine responses on curve road situations such as acceleration/deceleration, stopping, and keeping the same speed direction so that better decisions can be made quickly. Simulation represents a possible solution by enabling the creation of reliable bounding boxes, as a first step, in this study, we discuss the feasibility of a simulation framework to detect the speed of different autonomous vehicles using Yolov3 in the real world. We first developed the YOLOV3 algorithm for autonomous vehicle image recognition using the dataset from the Matlab site. The YOLO v3 model, with an optimal performance compared to the performances of deep learning algorithms, is applied. The training parameters are refined through experiments and in the second part we proposed an effective system using "Vision Vehicle Detector test brake adapter" adaptive HighwayLaneFollowingTestBench/Simulation 3D Scenario to prepare Matlab Simulink simulation environment and sensors, Vision Vehicle Detector. The training parameters are refined through experiments. The vehicle detection rate is approximately 95.8% As per our best knowledge, as a result of the experiment, the proposed system has shown favorable results.For the safety of autonomous vehicles, it is not necessary that the human driver does not have much trouble detecting other vehicles and maintaining a certain distance between them, but in the case of autonomous vehicles, that's not an easy task. The problem of detecting and recognizing the front state of autonomous vehicles is known as object detection by Yolov3 bounding boxes. Therefore, we propose this study to avoid accidents before they occur due to autonomous driving on the road and for a better future.  Our purpose in this study is to put autonomous vehicles on the road in practice using Simulink Matlab, and it is a reflection on the ability of autonomous vehicles to ensure curve road safety And to quickly determine responses on curve road situations such as acceleration/deceleration, stopping, and keeping the same speed direction so that better decisions can be made quickly. Simulation represents a possible solution by enabling the creation of reliable bounding boxes, as a first step, in this study, we discuss the feasibility of a simulation framework to detect the speed of different autonomous vehicles using Yolov3 in the real world. We first developed the YOLOV3 algorithm for autonomous vehicle image recognition using the dataset from the Matlab site. The YOLO v3 model, with an optimal performance compared to the performances of deep learning algorithms, is applied. The training parameters are refined through experiments and in the second part we proposed an effective system using "Vision Vehicle Detector test brake adapter" adaptive HighwayLaneFollowingTestBench/Simulation 3D Scenario to prepare Matlab Simulink simulation environment and sensors, Vision Vehicle Detector. The training parameters are refined through experiments. The vehicle detection rate is approximately 95.8% As per our best knowledge, as a result of the experiment, the proposed system has shown favorable results

Over speed detection using Artificial Intelligence

Over speeding is one of the most common traffic violations. Around 41 million people are issued speeding tickets each year in USA i.e one every second. Existing approaches to detect over- speeding are not scalable and require manual efforts. In this project, by the use of computer vision and artificial intelligence, I have tried to detect over speeding and report the violation to the law enforcement officer. It was observed that when predictions are done using YoloV3, we get the best results

Traffic Participants Detection and Classification Using YOLO Neural Network

One of the most important requirements for the next generation of traffic monitoring systems, autonomous driving technology, and Advanced Driving Assistance Systems (ADAS) is the detection and classification of traffic participants. Although in the areas of object detection and classification research, tremendous progress has been made, we focused on a specific task of detecting and classifying traffic participants from traffic scenarios. In our work, we have chosen a Deep Convolutional Neural Networks-based object detection algorithm – YOLOv4 (You Only Look Once Version 4) to detect and classify traffic participants accurately with fast speed. The main contribution of our work included: firstly, we built a custom image dataset of traffic participants (Car, Bus, Truck, Pedestrian, Traffic light, Traffic sign, Vehicle registration plate, Motorcycle, Ambulance, Bicycle wheel). After that, we run K-means clustering on the dataset to design anchor box, which is utilized to adapt to various small and medium scales. Finally, trained the network for the mentioned objects and tested our network in several driving conditions (daylight, low light, high traffic, foggy, rainy, etc.). We got the results reached a mean Average Precision (mAP) up to 65.95% and the speed was around 0.054 s

Traffic Light Recognition using Convolutional Neural Networks: A Survey

Real-time traffic light recognition is essential for autonomous driving. Yet, a cohesive overview of the underlying model architectures for this task is currently missing. In this work, we conduct a comprehensive survey and analysis of traffic light recognition methods that use convolutional neural networks (CNNs). We focus on two essential aspects: datasets and CNN architectures. Based on an underlying architecture, we cluster methods into three major groups: (1) modifications of generic object detectors which compensate for specific task characteristics, (2) multi-stage approaches involving both rule-based and CNN components, and (3) task-specific single-stage methods. We describe the most important works in each cluster, discuss the usage of the datasets, and identify research gaps.Comment: Accepted for publication at ITSC202

Traffic Sign Detection and Recognition with Voice Assistant

here are multitude of applications for detection and recognition of images across different fields. There are some specific applications for these systems used to help people to drive for example in autonomous driving as well as other applications. There has been another focus in the use of classification models used to help drivers providing details about their surrounding while driving. In places like Guadalajara, such models are a valuable tool to reduce traffic accidents. This document will explain the development of a detection and recognition of traffic signs model. This model has the intention of providing details about the meaning of the traffic signs. All this will happen close to real time and will be an additional information to the driver. This whole system could be used by anyone but specifically aimed to people with visual deficiencies. With the use of a robust machine learning and the use of Deep Learning (DL), the expectative is to achieve high accuracy levels on the traffic sign detection and recognition. This system is expected to be available and affordable for most of the drivers in Guadalajara.ITESO, A. C