Advances in vision-based lane detection: algorithms, integration, assessment, and perspectives on ACP-based parallel vision

Lane detection is a fundamental aspect of most current advanced driver assistance systems (ADASs). A large number of existing results focus on the study of vision-based lane detection methods due to the extensive knowledge background and the low-cost of camera devices. In this paper, previous vision-based lane detection studies are reviewed in terms of three aspects, which are lane detection algorithms, integration, and evaluation methods. Next, considering the inevitable limitations that exist in the camera-based lane detection system, the system integration methodologies for constructing more robust detection systems are reviewed and analyzed. The integration methods are further divided into three levels, namely, algorithm, system, and sensor. Algorithm level combines different lane detection algorithms while system level integrates other object detection systems to comprehensively detect lane positions. Sensor level uses multi-modal sensors to build a robust lane recognition system. In view of the complexity of evaluating the detection system, and the lack of common evaluation procedure and uniform metrics in past studies, the existing evaluation methods and metrics are analyzed and classified to propose a better evaluation of the lane detection system. Next, a comparison of representative studies is performed. Finally, a discussion on the limitations of current lane detection systems and the future developing trends toward an Artificial Society, Computational experiment-based parallel lane detection framework is proposed

Vision-based ego-lane analysis system : dataset and algorithms

A detecção e análise da faixa de trânsito são tarefas importantes e desafiadoras em sistemas avançados de assistência ao motorista e direção autônoma. Essas tarefas são necessárias para auxiliar veículos autônomos e semi-autônomos a operarem com segurança. A queda no custo dos sensores de visão e os avanços em hardware embarcado impulsionaram as pesquisas relacionadas a faixa de trânsito –detecção, estimativa, rastreamento, etc. – nas últimas duas décadas. O interesse nesse tópico aumentou ainda mais com a demanda por sistemas avançados de assistência ao motorista (ADAS) e carros autônomos. Embora amplamente estudado de forma independente, ainda há necessidade de estudos que propõem uma solução combinada para os vários problemas relacionados a faixa do veículo, tal como aviso de saída de faixa (LDW), detecção de troca de faixa, classificação do tipo de linhas de divisão de fluxo (LMT), detecção e classificação de inscrições no pavimento, e detecção da presença de faixas ajdacentes. Esse trabalho propõe um sistema de análise da faixa do veículo (ELAS) em tempo real capaz de estimar a posição da faixa do veículo, classificar as linhas de divisão de fluxo e inscrições na faixa, realizar aviso de saída de faixa e detectar eventos de troca de faixa. O sistema proposto, baseado em visão, funciona em uma sequência temporal de imagens. Características das marcações de faixa são extraídas tanto na perspectiva original quanto em images mapeadas para a vista aérea, que então são combinadas para aumentar a robustez. A estimativa final da faixa é modelada como uma spline usando uma combinação de métodos (linhas de Hough, filtro de Kalman e filtro de partículas). Baseado na faixa estimada, todos os outros eventos são detectados. Além disso, o sistema proposto foi integrado para experimentação em um sistema para carros autônomos que está sendo desenvolvido pelo Laboratório de Computação de Alto Desempenho (LCAD) da Universidade Federal do Espírito Santo (UFES). Para validar os algorítmos propostos e cobrir a falta de base de dados para essas tarefas na literatura, uma nova base dados com mais de 20 cenas diferentes (com mais de 15.000 imagens) e considerando uma variedade de cenários (estrada urbana, rodovias, tráfego, sombras, etc.) foi criada. Essa base de dados foi manualmente anotada e disponilizada publicamente para possibilitar a avaliação de diversos eventos que são de interesse para a comunidade de pesquisa (i.e. estimativa, mudança e centralização da faixa; inscrições no pavimento; cruzamentos; tipos de linhas de divisão de fluxo; faixas de pedestre e faixas adjacentes). Além disso, o sistema também foi validado qualitativamente com base na integração com o veículo autônomo. O sistema alcançou altas taxas de detecção em todos os eventos do mundo real e provou estar pronto para aplicações em tempo real.Lane detection and analysis are important and challenging tasks in advanced driver assistance systems and autonomous driving. These tasks are required in order to help autonomous and semi-autonomous vehicles to operate safely. Decreasing costs of vision sensors and advances in embedded hardware boosted lane related research – detection, estimation, tracking, etc. – in the past two decades. The interest in this topic has increased even more with the demand for advanced driver assistance systems (ADAS) and self-driving cars. Although extensively studied independently, there is still need for studies that propose a combined solution for the multiple problems related to the ego-lane, such as lane departure warning (LDW), lane change detection, lane marking type (LMT) classification, road markings detection and classification, and detection of adjacent lanes presence. This work proposes a real-time Ego-Lane Analysis System (ELAS) capable of estimating ego-lane position, classifying LMTs and road markings, performing LDW and detecting lane change events. The proposed vision-based system works on a temporal sequence of images. Lane marking features are extracted in perspective and Inverse Perspective Mapping (IPM) images that are combined to increase robustness. The final estimated lane is modeled as a spline using a combination of methods (Hough lines, Kalman filter and Particle filter). Based on the estimated lane, all other events are detected. Moreover, the proposed system was integrated for experimentation into an autonomous car that is being developed by the High Performance Computing Laboratory of the Universidade Federal do Espírito Santo. To validate the proposed algorithms and cover the lack of lane datasets in the literature, a new dataset with more than 20 different scenes (in more than 15,000 frames) and considering a variety of scenarios (urban road, highways, traffic, shadows, etc.) was created. The dataset was manually annotated and made publicly available to enable evaluation of several events that are of interest for the research community (i.e. lane estimation, change, and centering; road markings; intersections; LMTs; crosswalks and adjacent lanes). Furthermore, the system was also validated qualitatively based on the integration with the autonomous vehicle. ELAS achieved high detection rates in all real-world events and proved to be ready for real-time applications.FAPE

Driver lane change intention inference using machine learning methods.

Lane changing manoeuvre on highway is a highly interactive task for human drivers. The intelligent vehicles and the advanced driver assistance systems (ADAS) need to have proper awareness of the traffic context as well as the driver. The ADAS also need to understand the driver potential intent correctly since it shares the control authority with the human driver. This study provides a research on the driver intention inference, particular focus on the lane change manoeuvre on highways. This report is organised in a paper basis, where each chapter corresponding to a publication, which is submitted or to be submitted. Part Ⅰ introduce the motivation and general methodology framework for this thesis. Part Ⅱ includes the literature survey and the state-of-art of driver intention inference. Part Ⅲ contains the techniques for traffic context perception that focus on the lane detection. A literature review on lane detection techniques and its integration with parallel driving framework is proposed. Next, a novel integrated lane detection system is designed. Part Ⅳ contains two parts, which provides the driver behaviour monitoring system for normal driving and secondary tasks detection. The first part is based on the conventional feature selection methods while the second part introduces an end-to-end deep learning framework. The design and analysis of driver lane change intention inference system for the lane change manoeuvre is proposed in Part Ⅴ. Finally, discussions and conclusions are made in Part Ⅵ. A major contribution of this project is to propose novel algorithms which accurately model the driver intention inference process. Lane change intention will be recognised based on machine learning (ML) methods due to its good reasoning and generalizing characteristics. Sensors in the vehicle are used to capture context traffic information, vehicle dynamics, and driver behaviours information. Machine learning and image processing are the techniques to recognise human driver behaviour.PhD in Transpor

Search for supersymmetric particles from Z decays

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Physics, 1995.Includes bibliographical references (p. 91-95).by Susan Mary Gascon-Shotkin.Ph.D

Sistema avanzado de asistencia a la conducción para entornos interurbanos

Los Sistemas de Asistencia a la Conducción, conocidos como ADAS (Advanced Driver Assistant Systems), desde su aparición en el año 2000, han permitido que las cifras de muertes en accidentes de tráfico disminuyan hasta 8.7 por cada 100.000 habitantes, según la OMS (Organización Mundial de la Salud), en su informe anual 2013. Esta tendencia se ha dado en economías de alto ingreso a pesar del crecimiento sostenido en las últimas dos décadas del parque automotor a nivel mundial. Algunos de los dispositivos ADAS más conocidos como la alerta por salida de carril, LDW (Lane Departure Warning), el asistente para mantenimiento de carril, LKA (Lane Keeping Assistance) o el asistente para cambio de carril, LCA (Lane Change Assistance), han hecho de la conducción una tarea cada vez más segura. La presente investigación doctoral propone un sistema ADAS diseñado para entornos de carretera tipo autopista interurbana, que clasifica carriles según la norma Española IC 8.2. La información visual se obtiene por medio de una cámara estéreo, con la cual es posible auto-calibrar los parámetros extrínsecos en tiempo de aplicación, para cambiar la perspectiva de las imágenes a vista superior o bird view, alcanzando una precisión de decimas de grado en orientación y mil ‘sismas de metro en posición vertical. El funcionamiento del ADAS propuesto para la plataforma IVVI 2.0 (Intelligent Vehicle Based on Visual Information) del laboratorio de Sistemas Inteligentes de la Universidad Carlos III, se compone de cuatro fases. En la primera fase se simplifica la imagen filtrando los objetos que cumplen las características propias de las marcas viales, restringiendo la búsqueda a la zona de la imagen coincidente con el plano tierra. En la segunda fase, el procesamiento continua con la detección de los elementos constitutivos de la carretera, las líneas de carril y los correspondientes carriles que se forman con ellas. Posteriormente, en la tercera fase se realiza la detección parcial o total de la carretera, que corresponde con la búsqueda de carriles adyacentes en el espacio. En la última fase, con las detecciones parciales o totales de carretera, formadas por una cadena de carriles, se aplica un proceso de casamiento carril a carril, para actualizar en el tiempo una cadena final completa, que tendrá la cualidad de rechazar oclusiones y errores por sombras y cambios de iluminación. Finalmente, se aborda el estudio de cómo aplicar aprendizaje de maquina en la estructura ADAS desarrollada, logrando buenos resultados en la segmentación de marcas viales sobre carreteras donde las líneas se han degradado visualmente, en esta fase, sobre una secuencia de referencia de 75 imágenes, se observó como con el uso de un clasificador basado en máquinas de soporte vectorial, SVM (Support Vector Machine), logro la segmentación de marcas viales con un índice de sensibilidad de 70 %, 44% por encima del siguiente mejor método analizado. Asímismo, al integrar la estrategia SVM para detección de marcas viales en el algoritmo general ADAS propuesto, se obtuvo una mejora en la detección de líneas viales hasta de un 83% en relación al 77% obtenido en la versión desarrollada inicialmente sin aprendizaje de máquina.The Advanced Driver Assistant Systems, ADAS, have allowed to reduce deaths on the road up to 8.7 per 100.000 inhabitants since they were presented at the first time in the year 2000, according to World Health Organization, WHO, in its Global status report on road safety 2013. This is a trend that is characteristic of the high-income economies beside the continuous automotive fleet growing in the last two decades. Some of the most well-known ADAS applications, such as LDW (Lane Departure Warning), LKA (Lane Keeping Assistance) or LCA (Lane Change Assistance), have made driving a safer task. This research proposes an ADAS application designed for interurban highways, classifying lanes according to the Spain standard IC 8.2. The visual information is captured from stereo camera, this camera retrives 3D information from the visual pattern. This makes it possible for the application to be self-tuning by automatically calculating its extrinsic parameters up to tenths of a degree accuracy for orientation and millimeters for camera’s height. This process allows to change the perspective of the images to bird view in application time. The proposed ADAS was designed for the platform IVVI 2.0 (Intelligent Vehicle Based on Visual Information) from Intelligent Systems Lab at Carlos III University, is carried out in four stages. In the first one, the image is simplified by filtering objects that meet the road markings features, limiting the search area to the ground plane. In the second stage, the detection of the basic elements of the road is made, i.e. the road lines and the lanes formed by them. Subsequently, the partial or total road detection is performed by the search of adjacent lanes. In the last stage, a matching process is applied lane by lane, updating in real time a composed final chain, the complete lane chain has the ability to discard errors caused by occlusions and mismatches due to shadows and lighting changes. Finally, feasibility study is presented for machine learning usability in the ADAS application developed, achieving good results in the segmentation of road markings on highways where lines have been deteriorated and in the classification of lane’s lines. The road mark segmentation results, obtained over a secuence of 75 images and applying SVM (Support Vector Machine) classifier, exhibit a sensibility rate of 70 %, 44% above the next better method analyzed. Furthermore, by integrating the SVM strategy to detection of road markings in the proposed general algorithm, an improvement in the road lines detection was obtained, reaching to 83 %, considerable higher than the 77% obtained by the algorithm without Machine Learning.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Pascual Campoy Cervera.- Secretario: Fernando García Fernández.- Vocal: Pedro Javier Navarro Lorent

A colorful view of planet formation:A multi-wavelength study of planet-disk interactions

The broad variety of planets and planetary systems discovered in the last couple of decades around other stars has taught us that these can be, and more often than not tend to be, very different from our own. In order to explain this variety, we must gain a better understanding of the physical processes leading to planet formation, and of the formation environments themselves. Protoplanetary disks are large, flat structures of gas and dust surrounding and orbiting young stars, and they are both the location and the material from which planets are formed. In order to learn more about the planet formation process, we need to observe it in action. Unfortunately, forming planets are extremely hard to detect with current instruments. However, the interaction between a forming planet and its disk can lead to the formation of structures and even misalignments in the disk. This can in theory result in observable features we should be able to detect with current instrumentation, and would explain a large number of features seen in observations of protoplanetary disks at multiple wavelengths. This thesis presents research on three individual sources observed in both near-infrared (NIR) and (sub-)millimeter wavelengths with the SPHERE and ALMA instruments, with a focus on observable disk features that could be linked to planet formation, as well as a set of hydrodynamical models of planet-disk interactions and their NIR images obtained through radiative transfer modeling, with the aim of studying the observable signatures arising from these interactions

Search for new physics with a same-sign dilepton pair at a center-of-mass energy of 13 TeV at the CMS detector

An inclusive search for new physics is performed with a 13 TeV dataset from the CMS detector (May-November 2015, corresponding to 2.32/fb) using events with at least two leptons with the same electrical charge, missing transverse energy, and significant hadronic activity. No significant excesses are reported above the Standard Model background predictions. Constraints are set on a number of new physics models. As motivation, a thorough pedagogical review of the Standard Model and its shortcomings is presented in the first two chapters