Total Recall: Understanding Traffic Signs using Deep Hierarchical Convolutional Neural Networks

Recognizing Traffic Signs using intelligent systems can drastically reduce the number of accidents happening world-wide. With the arrival of Self-driving cars it has become a staple challenge to solve the automatic recognition of Traffic and Hand-held signs in the major streets. Various machine learning techniques like Random Forest, SVM as well as deep learning models has been proposed for classifying traffic signs. Though they reach state-of-the-art performance on a particular data-set, but fall short of tackling multiple Traffic Sign Recognition benchmarks. In this paper, we propose a novel and one-for-all architecture that aces multiple benchmarks with better overall score than the state-of-the-art architectures. Our model is made of residual convolutional blocks with hierarchical dilated skip connections joined in steps. With this we score 99.33% Accuracy in German sign recognition benchmark and 99.17% Accuracy in Belgian traffic sign classification benchmark. Moreover, we propose a newly devised dilated residual learning representation technique which is very low in both memory and computational complexity

Usage of convolutional neural network ensemble for traffic sign recognition

Предлагается для распознавания дорожных знаков использовать ансамбль сверточных нейронных сетей, который является модификацией робастного метода распознавания на основе нейронных сетей глубокого обучения. Данный ансамбль повышает скорость работы робастного метода распознавания, а также позволяет увеличить быстродействие с сохранением высокой точности распознавания за счет удаления из набора данных значений, которые не представляют полезной нагрузки

Recognition of retroreflective traffic signs by a vehicle camera system

The systems of traffic sign recognition are based on the evaluation of three components of every sign: shape, colour and pictogram. There are different factors that can have an influence on the efficiency of detection and recognition of these components. One of the most important factors is the quality of the retroreflective sign surface. Retroreflective sheeting improves the readability of colour and pictogram of traffic sign by increasing brightness of its background and/or legend elements. The aim of the paper is to provide a comprehensive survey of the efficiency of sign’s recognition by a modern vehicle camera system. The traffic sign sheeting was measured by the handled retroreflectometer. Then this measurement was repeated by the modern camera system used for recognition of traffic signs in the vehicle. The results of this paper present the analysis of the recognition efficiency of traffic signs and the overview of other factors that can have a significant impact on sign detection and recognition distance. The results can be used for creation a traffic sign database for learning-based recognition techniques to vehicle camera systems

A Quantitative and Qualitative Review of the Role of Intelligent Transportation Systems in Road Safety Studies through Three Decades

Road safety is an important subject to study in both the technical and academic fields of road transportation. In recent years, there has been a significant rise in the number of studies that look at how intelligent transportation systems (ITS) can be used and what role it plays in making roads safer in different countries. Nevertheless, there are still relatively few in-depth quantitative and qualitative analyses published on the topic of ITS's role in ensuring road safety. For this purpose, the main goal of this study is to look at a thorough quantitative and qualitative analysis of how ITS is used in road safety as a part of transportation engineering. In this study, we reviewed the scientific studies done on the use of ITS in studies of road safety from 1990s to 2022. These studies were published in journals or presented at conferences that were part of the Web of Science (WoS) Index. The analysis in this study gives a thorough map of the field, showing how it has changed over time and pointing the way to new areas of research

Eigen-Gradientes contra Histograma de Orientación de Gradientes para Reconocimiento de Señalamientos Viales de Límite de Velocidad

El reconocimiento automático de señalamientos viales tiene como principales aplicaciones el inventariado de carreteras, los sistemas de asistencia al conductor y la implementación de automóviles autónomos. El reconocimiento de señalamientos viales de límite de velocidad presenta su mayor importancia debido a que, gran parte de los accidentes carreteros con consecuencias mortales ocurren mientras se maneja a gran velocidad. El presente artículo tiene por objetivo realizar la clasificación de señales de tránsito de límite de velocidad, utilizando para ello dos distintos atributos: los mapas de orientación de gradientes y los histogramas de orientación de gradientes. Los resultados experimentales muestran que ambos atributos presentan eficiencias similares, utilizando diferente número de características

Security attacks on RECKLESS-APPs: Remote car keyless applications for new semi autonomous vehicles

Rapid technological advancements of vehicle manufacturing and the modern wireless technology opens the door for several new Intelligent Transportation applications. Remote Keyless system in vehicles is considered one of the famous applications that has been developed recently, which is susceptible to many cyberattacks. Remote Keyless applications on smartphones were developed in the past few years to perform the functionality of keyless fob and are expected to replace the physical keyless fobs in the next few years, which can open the door to many cyberattacks. In this research, we implemented a simulation that represents the REmote Car KeyLESS Applications (RECKLESS-apps) on the smartphones. In this thesis we demonstrate the types of cyber-attacks these applications could face. Our research serves as a proof that remote car keyless applications that would replace the physical keys could be vulnerable to various malicious cyber-attacks. We study these attacks and provide remedies, cyber-hygiene and best practices to remedy some of these attacks

TRAFFIC SCENARIOS AND VISION USE CASES FOR THE VISUALLY IMPAIRED

We gather the requirements visually impaired road users have concerning a camera-based assistive system in order to develop a concept for transferring algorithms from Advanced Driver Assistance Systems (ADAS) to this domain. We therefore combine procedures from software engineering, especially requirements engineering, with methods from qualitative social sciences, namely expert interviews by means of problem-centered interview methodology. The evaluation of the interviews results in a total of six traffic scenarios, that are of interest for the assistance of visually impaired road users, clustered into three categories: orientation, pedestrian and public transport scenarios. From each scenario, we derive use cases based on computer vision and state which of them are also addressed in ADAS so that we can examine them further in the future to formulate the transfer concept. We present a general literature review concerning solutions from ADAS for the identified overlapping use cases from both fields and take a closer look on how to adapt ADAS Lane Detection algorithms to the assistance of the visually impaired