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 vii 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

Region of Interest Generation for Pedestrian Detection using Stereo Vision

Pedestrian detection is an active research area in the field of computer vision. The sliding window paradigm is usually followed to extract all possible detector windows, however, it is very time consuming. Subsequently, stereo vision using a pair of camera is preferred to reduce the search space that includes the depth information. Disparity map generation using feature correspondence is an integral part and a prior task to depth estimation. In our work, we apply the ORB features to fasten the feature correspondence process. Once the ROI generation phase is over, the extracted detector window is represented by low level histogram of oriented gradient (HOG) features. Subsequently, Linear Support Vector Machine (SVM) is applied to classify them as either pedestrian or non-pedestrian. The experimental results reveal that ORB driven depth estimation is at least seven times faster than the SURF descriptor and ten times faster than the SIFT descriptor

Vehicular Instrumentation and Data Processing for the Study of Driver Intent

The primary goal of this thesis is to provide processed experimental data needed to determine whether driver intentionality and driving-related actions can be predicted from quantitative and qualitative analysis of driver behaviour. Towards this end, an instrumented experimental vehicle capable of recording several synchronized streams of data from the surroundings of the vehicle, the driver gaze with head pose and the vehicle state in a naturalistic driving environment was designed and developed. Several driving data sequences in both urban and rural environments were recorded with the instrumented vehicle. These sequences were automatically annotated for relevant artifacts such as lanes, vehicles and safely driveable areas within road lanes. A framework and associated algorithms required for cross-calibrating the gaze tracking system with the world coordinate system mounted on the outdoor stereo system was also designed and implemented, allowing the mapping of the driver gaze with the surrounding environment. This instrumentation is currently being used for the study of driver intent, geared towards the development of driver maneuver prediction models

leave a trace - A People Tracking System Meets Anomaly Detection

Video surveillance always had a negative connotation, among others because of the loss of privacy and because it may not automatically increase public safety. If it was able to detect atypical (i.e. dangerous) situations in real time, autonomously and anonymously, this could change. A prerequisite for this is a reliable automatic detection of possibly dangerous situations from video data. This is done classically by object extraction and tracking. From the derived trajectories, we then want to determine dangerous situations by detecting atypical trajectories. However, due to ethical considerations it is better to develop such a system on data without people being threatened or even harmed, plus with having them know that there is such a tracking system installed. Another important point is that these situations do not occur very often in real, public CCTV areas and may be captured properly even less. In the artistic project leave a trace the tracked objects, people in an atrium of a institutional building, become actor and thus part of the installation. Visualisation in real-time allows interaction by these actors, which in turn creates many atypical interaction situations on which we can develop our situation detection. The data set has evolved over three years and hence, is huge. In this article we describe the tracking system and several approaches for the detection of atypical trajectories

Object Detection Using LiDAR and Camera Fusion in Off-road Conditions

Seoses hüppelise huvi kasvuga autonoomsete sõidukite vastu viimastel aastatel on suurenenud ka vajadus täpsemate ja töökindlamate objektituvastuse meetodite järele. Kuigi tänu konvolutsioonilistele närvivõrkudele on palju edu saavutatud 2D objektituvastuses, siis võrreldavate tulemuste saavutamine 3D maailmas on seni jäänud unistuseks. Põhjuseks on mitmesugused probleemid eri modaalsusega sensorite andmevoogude ühitamisel, samuti on 3D maailmas märgendatud andmestike loomine aeganõudvam ja kallim. Sõltumata sellest, kas kasutame objektide kauguse hindamiseks stereo kaamerat või lidarit, kaasnevad andmevoogude ühitamisega ajastusprobleemid, mis raskendavad selliste lahenduste kasutamist reaalajas. Lisaks on enamus olemasolevaid lahendusi eelkõige välja töötatud ja testitud linnakeskkonnas liikumiseks.Töös pakutakse välja meetod 3D objektituvastuseks, mis põhineb 2D objektituvastuse tulemuste (objekte ümbritsevad kastid või segmenteerimise maskid) projitseerimisel 3D punktipilve ning saadud punktipilve filtreerimisel klasterdamismeetoditega. Tulemusi võrreldakse lihtsa termokaamera piltide filtreerimisel põhineva lahendusega. Täiendavalt viiakse läbi põhjalikud eksperimendid parimate algoritmi parameetrite leidmiseks objektituvastuseks maastikul, saavutamaks suurimat võimalikku täpsust reaalajas.Since the boom in the industry of autonomous vehicles, the need for preciseenvironment perception and robust object detection methods has grown. While we are making progress with state-of-the-art in 2D object detection with approaches such as convolutional neural networks, the challenge remains in efficiently achieving the same level of performance in 3D. The reasons for this include limitations of fusing multi-modal data and the cost of labelling different modalities for training such networks. Whether we use a stereo camera to perceive scene’s ranging information or use time of flight ranging sensors such as LiDAR, ​ the existing pipelines for object detection in point clouds have certain bottlenecks and latency issues which tend to affect the accuracy of detection in real time speed. Moreover, ​ these existing methods are primarily implemented and tested over urban cityscapes.This thesis presents a fusion based approach for detecting objects in 3D by projecting the proposed 2D regions of interest (object’s bounding boxes) or masks (semantically segmented images) to point clouds and applies outlier filtering techniques to filter out target object points in projected regions of interest. Additionally, we compare it with human detection using thermal image thresholding and filtering. Lastly, we performed rigorous benchmarks over the off-road environments to identify potential bottlenecks and to find a combination of pipeline parameters that can maximize the accuracy and performance of real-time object detection in 3D point clouds