9 research outputs found
Sensor fusion in driving assistance systems
Mención Internacional en el título de doctorLa vida diaria en los países desarrollados y en vías de desarrollo depende en
gran medida del transporte urbano y en carretera. Esta actividad supone un
coste importante para sus usuarios activos y pasivos en términos de polución
y accidentes, muy habitualmente debidos al factor humano. Los nuevos desarrollos
en seguridad y asistencia a la conducción, llamados Advanced Driving
Assistance Systems (ADAS), buscan mejorar la seguridad en el transporte, y
a medio plazo, llegar a la conducción autónoma.
Los ADAS, al igual que la conducción humana, están basados en sensores
que proporcionan información acerca del entorno, y la fiabilidad de los sensores
es crucial para las aplicaciones ADAS al igual que las capacidades
sensoriales lo son para la conducción humana. Una de las formas de aumentar
la fiabilidad de los sensores es el uso de la Fusión Sensorial, desarrollando
nuevas estrategias para el modelado del entorno de conducción gracias al uso
de diversos sensores, y obteniendo una información mejorada a partid de los
datos disponibles.
La presente tesis pretende ofrecer una solución novedosa para la detección
y clasificación de obstáculos en aplicaciones de automoción, usando fusión
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sensorial con dos sensores ampliamente disponibles en el mercado: la cámara
de espectro visible y el escáner láser. Cámaras y láseres son sensores
comúnmente usados en la literatura científica, cada vez más accesibles y listos
para ser empleados en aplicaciones reales. La solución propuesta permite la
detección y clasificación de algunos de los obstáculos comúnmente presentes
en la vía, como son ciclistas y peatones.
En esta tesis se han explorado novedosos enfoques para la detección y clasificación,
desde la clasificación empleando clusters de nubes de puntos obtenidas
desde el escáner láser, hasta las técnicas de domain adaptation para la creación
de bases de datos de imágenes sintéticas, pasando por la extracción inteligente
de clusters y la detección y eliminación del suelo en nubes de puntos.Life in developed and developing countries is highly dependent on road and
urban motor transport. This activity involves a high cost for its active and passive
users in terms of pollution and accidents, which are largely attributable to
the human factor. New developments in safety and driving assistance, called
Advanced Driving Assistance Systems (ADAS), are intended to improve
security in transportation, and, in the mid-term, lead to autonomous driving.
ADAS, like the human driving, are based on sensors, which provide information
about the environment, and sensors’ reliability is crucial for ADAS
applications in the same way the sensing abilities are crucial for human driving.
One of the ways to improve reliability for sensors is the use of Sensor
Fusion, developing novel strategies for environment modeling with the help of
several sensors and obtaining an enhanced information from the combination
of the available data.
The present thesis is intended to offer a novel solution for obstacle detection
and classification in automotive applications using sensor fusion with two
highly available sensors in the market: visible spectrum camera and laser
scanner. Cameras and lasers are commonly used sensors in the scientific
literature, increasingly affordable and ready to be deployed in real world
applications. The solution proposed provides obstacle detection and classification
for some obstacles commonly present in the road, such as pedestrians and bicycles.
Novel approaches for detection and classification have been explored in this
thesis, from point cloud clustering classification for laser scanner, to domain
adaptation techniques for synthetic dataset creation, and including intelligent
clustering extraction and ground detection and removal from point clouds.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Cristina Olaverri Monreal.- Secretario: Arturo de la Escalera Hueso.- Vocal: José Eugenio Naranjo Hernánde
Motorcycle detection for ADAS through camera and V2V communication, a comparative analysis of two modern technologies
Motorcycles are one of the most dangerous means of transportation. Its death toll is higher than in others, due to the inherent vulnerability of motorcycle drivers. The latest strategies in Advanced Driving Assistance Systems (ADAS) are trying to mitigate this problem by applying the advances of modern technologies to the road transport. This paper presents two different approaches on motorcycle protection, based on two of the most modern available technologies in ADAS, i.e. Computer Vision and Vehicle to Vehicle Communication (V2V). The first approach is based on data fusion of Laser Scanner and Computer Vision, providing accurate obstacle detection and localization based on laser scanner, and obstacle classification using computer vision and laser. The second approach is based on ad-hoc V2V technology and provides detection in case of occlusion for visual sensors. Both technologies have been tested in the presented work, and a performance comparison is given. Tests performed in different driving situations allows to measure the performance of every algorithm and the limitations of each of them based on empirical and scientific foundations. The conclusions of the presented work help foster of expert systems in the automotive sector by providing further discussion of the viability and impact from each of these systems in real scenarios
Context aided pedestrian detection for danger estimation based on laser scanner and computer vision
Road safety applications demand the most reliable sensor systems. In recent years, the advances in information technologies have led to more complex road safety applications able to cope with a high variety of situations. These applications have strong sensing requirements that a single sensor, with the available technology, cannot attain. Recent researches in Intelligent Transport Systems (ITS) try to overcome the limitations of the sensors by combining them. But not only sensor information is crucial to give a good and robust representation of the road environment; context information has a key role for reliable safety applications to provide reliable detection and complete situation assessment. This paper presents a novel approach for pedestrian detection using sensor fusion of laser scanner and computer vision. The application also takes advantage of context information, providing danger estimation for the pedestrians detected. Closing the loop, the danger estimation is later used, together with context information, as feedback to enhance the pedestrian detection process.This work was supported by the Spanish Government through the Cicyt projects (GRANT TRA2010-20225-C03-01), (TEC2011-28626-C02-02) and (GRANT TRA 2011-29454-C03-02), CAM through SEGVAUTO-II (S2009/DPI-1509) and mobility program of ‘‘Fundación Caja Madrid’’.Publicad
Computer vision and laser scanner road environment perception
Data fusion procedure is presented to enhance classical Advanced Driver Assistance Systems (ADAS). The novel vehicle safety approach, combines two classical sensors: computer vision and laser scanner. Laser scanner algorithm performs detection of vehicles and pedestrians based on pattern matching algorithms. Computer vision approach is based on Haar-Like features for vehicles and Histogram of Oriented Gradients (HOG) features for pedestrians. The high level fusion procedure uses Kalman Filter and Joint Probabilistic Data Association (JPDA) algorithm to provide high level detection. Results proved that by means of data fusion, the performance of the system is enhanced.This work was supported by the Spanish Government
through the Cicyt projects (GRANT TRA2010-20225-C03-01)
and (GRANT TRA 2011-29454-C03-02). CAM through
SEGAUTO-II (S2009IDPI-1509)
Automatic laser and camera extrinsic calibration for data fusion using road plane
Driving Assistance Systems and Autonomous Driving applications require trustable detections. These demanding requirements need sensor fusion to provide information reliable enough. But data fusion presents the problem of data alignment in both rotation and translation. Laser scanner and video cameras are widely used in sensor fusion. Laser provides operation in darkness, long range detection and accurate measurement but lacks the means for reliable classification due to the limited information provided. The camera provides classification thanks to the amount of data provided but lacks accuracy for measurements and is sensitive to illumination conditions. Data alignment processes require supervised and accurate measurements, that should be performed by experts, or require specific patterns or shapes. This paper presents an algorithm for inter-calibration between the two sensors of our system, requiring only a flat surface for pitch and roll calibration and an obstacle visible for both sensors for determining the yaw. The advantage of this system is that it does not need any particular shape to be located in front of the vehicle apart from a flat surface, which is usually the road. This way, calibration can be achieved at virtually any time without human intervention.This work was supported by Automation Engineering
Department from de La Salle University, Bogotá-Colombia;
Administrative Department of Science, Technology and
Innovation (COLCIENCIAS), Bogotá-Colombia and the
Spanish Government through the Cicyt projects (GRANT
TRA2010-20225-C03-01) and (GRANT TRA 2011-29454-
C03-02)
IVVI 2.0: An intelligent vehicle based on computational perception
This paper presents the IVVI 2.0 a smart research platform to foster intelligent systems in vehicles. Computational perception in intelligent transportation systems applications has advantages, such as huge data from vehicle environment, among others, so computer vision systems and laser scanners are the main devices that accomplish this task. Both have been integrated in our intelligent vehicle to develop cutting-edge applications to cope with perception difficulties, data processing algorithms, expert knowledge, and decision-making. The long-term in-vehicle applications, that are presented in this paper, outperform the most significant and fundamental technical limitations, such as, robustness in the face of changing environmental conditions. Our intelligent vehicle operates outdoors with pedestrians and others vehicles, and outperforms illumination variation, i.e.: shadows, low lighting conditions, night vision, among others. So, our applications ensure the suitable robustness and safety in case of a large variety of lighting conditions and complex perception tasks. Some of these complex tasks are overcome by the improvement of other devices, such as, inertial measurement units or differential global positioning systems, or perception architectures that accomplish sensor fusion processes in an efficient and safe manner. Both extra devices and architectures enhance the accuracy of computational perception and outreach the properties of each device separately.This work was supported by the Spanish Government through the CICYT projects (GRANT TRA2010 20225 C03 01) and (GRANT TRA 2011 29454 C03 02)
Clasificación automática de obstáculos empleando escáner láser y visión por ordenador
[Resumen] Muchos sistemas enmarcados en el estado del arte del campo de los sistemas avanzados de asistencia a la conducción (ADAS) y de la conducción autónoma emplean fusión sensorial con el fin de conseguir detección y clasificación de obstáculos fiable en cualquier condición meteorológica y de iluminación. La fusión entre escáner láser y cámara se usa habitualmente en aplicaciones ADAS para mitigar las limitaciones inherentes a cada uno de los sensores empleados. En el sistema presentado se emplean algunas técnicas novedosas para alineamiento de datos y se aplican técnicas de inteligencia artificial (IA) en el tratamiento de las nubes de puntos para mejorar la fiabilidad de la clasificación de obstáculos. En este documento se presentan nuevos enfoques para la obtención de clústeres en nubes de puntos dispersas, maximizando la información obtenida desde escáneres láser de baja resolución. Tras la mejora de la detección de clústeres, se emplean técnicas de IA para clasificar el obstáculo no solo empleando visión por computador, sino también con información del láser. La fusión de la información obtenida desde ambos sensores, con la adición de la capacidad de clasificación del láser, mejoran la fiabilidad del sistema.Comisión Interministerial de Ciencia y Tecnología; TRA2013-48314-C3-1-RComisión Interministerial de Ciencia y Tecnología; TRA2011-29454-C03-0
Laser scanner and camera fusion for automatic obstacle classification in ADAS application
Reliability and accuracy are key in state of the art Driving Assistance Systems and Autonomous Driving applications. These applications make use of sensor fusion for trustable obstacle detection and classification in any meteorological and illumination condition. Laser scanner and camera are widely used as sensors to fuse because of its complementary capabilities. This paper presents some novel techniques for automatic and unattended data alignment between sensors, and Artificial Intelligence techniques are used to use laser point clouds not only for obstacle detection but also for classification.. Information fusion with classification information from both laser scanner and camera improves overall system reliability