Lidar-based scene understanding for autonomous driving using deep learning

With over 1.35 million fatalities related to traffic accidents worldwide, autonomous driving was foreseen at the beginning of this century as a feasible solution to improve security in our roads. Nevertheless, it is meant to disrupt our transportation paradigm, allowing to reduce congestion, pollution, and costs, while increasing the accessibility, efficiency, and reliability of the transportation for both people and goods. Although some advances have gradually been transferred into commercial vehicles in the way of Advanced Driving Assistance Systems (ADAS) such as adaptive cruise control, blind spot detection or automatic parking, however, the technology is far from mature. A full understanding of the scene is actually needed so that allowing the vehicles to be aware of the surroundings, knowing the existing elements of the scene, as well as their motion, intentions and interactions. In this PhD dissertation, we explore new approaches for understanding driving scenes from 3D LiDAR point clouds by using Deep Learning methods. To this end, in Part I we analyze the scene from a static perspective using independent frames to detect the neighboring vehicles. Next, in Part II we develop new ways for understanding the dynamics of the scene. Finally, in Part III we apply all the developed methods to accomplish higher level challenges such as segmenting moving obstacles while obtaining their rigid motion vector over the ground. More specifically, in Chapter 2 we develop a 3D vehicle detection pipeline based on a multi-branch deep-learning architecture and propose a Front (FR-V) and a Bird’s Eye view (BE-V) as 2D representations of the 3D point cloud to serve as input for training our models. Later on, in Chapter 3 we apply and further test this method on two real uses-cases, for pre-filtering moving obstacles while creating maps to better localize ourselves on subsequent days, as well as for vehicle tracking. From the dynamic perspective, in Chapter 4 we learn from the 3D point cloud a novel dynamic feature that resembles optical flow from RGB images. For that, we develop a new approach to leverage RGB optical flow as pseudo ground truth for training purposes but allowing the use of only 3D LiDAR data at inference time. Additionally, in Chapter 5 we explore the benefits of combining classification and regression learning problems to face the optical flow estimation task in a joint coarse-and-fine manner. Lastly, in Chapter 6 we gather the previous methods and demonstrate that with these independent tasks we can guide the learning of higher challenging problems such as segmentation and motion estimation of moving vehicles from our own moving perspective.Con más de 1,35 millones de muertes por accidentes de tráfico en el mundo, a principios de siglo se predijo que la conducción autónoma sería una solución viable para mejorar la seguridad en nuestras carreteras. Además la conducción autónoma está destinada a cambiar nuestros paradigmas de transporte, permitiendo reducir la congestión del tráfico, la contaminación y el coste, a la vez que aumentando la accesibilidad, la eficiencia y confiabilidad del transporte tanto de personas como de mercancías. Aunque algunos avances, como el control de crucero adaptativo, la detección de puntos ciegos o el estacionamiento automático, se han transferido gradualmente a vehículos comerciales en la forma de los Sistemas Avanzados de Asistencia a la Conducción (ADAS), la tecnología aún no ha alcanzado el suficiente grado de madurez. Se necesita una comprensión completa de la escena para que los vehículos puedan entender el entorno, detectando los elementos presentes, así como su movimiento, intenciones e interacciones. En la presente tesis doctoral, exploramos nuevos enfoques para comprender escenarios de conducción utilizando nubes de puntos en 3D capturadas con sensores LiDAR, para lo cual empleamos métodos de aprendizaje profundo. Con este fin, en la Parte I analizamos la escena desde una perspectiva estática para detectar vehículos. A continuación, en la Parte II, desarrollamos nuevas formas de entender las dinámicas del entorno. Finalmente, en la Parte III aplicamos los métodos previamente desarrollados para lograr desafíos de nivel superior, como segmentar obstáculos dinámicos a la vez que estimamos su vector de movimiento sobre el suelo. Específicamente, en el Capítulo 2 detectamos vehículos en 3D creando una arquitectura de aprendizaje profundo de dos ramas y proponemos una vista frontal (FR-V) y una vista de pájaro (BE-V) como representaciones 2D de la nube de puntos 3D que sirven como entrada para entrenar nuestros modelos. Más adelante, en el Capítulo 3 aplicamos y probamos aún más este método en dos casos de uso reales, tanto para filtrar obstáculos en movimiento previamente a la creación de mapas sobre los que poder localizarnos mejor en los días posteriores, como para el seguimiento de vehículos. Desde la perspectiva dinámica, en el Capítulo 4 aprendemos de la nube de puntos en 3D una característica dinámica novedosa que se asemeja al flujo óptico sobre imágenes RGB. Para ello, desarrollamos un nuevo enfoque que aprovecha el flujo óptico RGB como pseudo muestras reales para entrenamiento, usando solo information 3D durante la inferencia. Además, en el Capítulo 5 exploramos los beneficios de combinar los aprendizajes de problemas de clasificación y regresión para la tarea de estimación de flujo óptico de manera conjunta. Por último, en el Capítulo 6 reunimos los métodos anteriores y demostramos que con estas tareas independientes podemos guiar el aprendizaje de problemas de más alto nivel, como la segmentación y estimación del movimiento de vehículos desde nuestra propia perspectivaAmb més d’1,35 milions de morts per accidents de trànsit al món, a principis de segle es va predir que la conducció autònoma es convertiria en una solució viable per millorar la seguretat a les nostres carreteres. D’altra banda, la conducció autònoma està destinada a canviar els paradigmes del transport, fent possible així reduir la densitat del trànsit, la contaminació i el cost, alhora que augmentant l’accessibilitat, l’eficiència i la confiança del transport tant de persones com de mercaderies. Encara que alguns avenços, com el control de creuer adaptatiu, la detecció de punts cecs o l’estacionament automàtic, s’han transferit gradualment a vehicles comercials en forma de Sistemes Avançats d’Assistència a la Conducció (ADAS), la tecnologia encara no ha arribat a aconseguir el grau suficient de maduresa. És necessària, doncs, una total comprensió de l’escena de manera que els vehicles puguin entendre l’entorn, detectant els elements presents, així com el seu moviment, intencions i interaccions. A la present tesi doctoral, explorem nous enfocaments per tal de comprendre les diferents escenes de conducció utilitzant núvols de punts en 3D capturats amb sensors LiDAR, mitjançant l’ús de mètodes d’aprenentatge profund. Amb aquest objectiu, a la Part I analitzem l’escena des d’una perspectiva estàtica per a detectar vehicles. A continuació, a la Part II, desenvolupem noves formes d’entendre les dinàmiques de l’entorn. Finalment, a la Part III apliquem els mètodes prèviament desenvolupats per a aconseguir desafiaments d’un nivell superior, com, per exemple, segmentar obstacles dinàmics al mateix temps que estimem el seu vector de moviment respecte al terra. Concretament, al Capítol 2 detectem vehicles en 3D creant una arquitectura d’aprenentatge profund amb dues branques, i proposem una vista frontal (FR-V) i una vista d’ocell (BE-V) com a representacions 2D del núvol de punts 3D que serveixen com a punt de partida per entrenar els nostres models. Més endavant, al Capítol 3 apliquem i provem de nou aquest mètode en dos casos d’ús reals, tant per filtrar obstacles en moviment prèviament a la creació de mapes en els quals poder localitzar-nos millor en dies posteriors, com per dur a terme el seguiment de vehicles. Des de la perspectiva dinàmica, al Capítol 4 aprenem una nova característica dinàmica del núvol de punts en 3D que s’assembla al flux òptic sobre imatges RGB. Per a fer-ho, desenvolupem un nou enfocament que aprofita el flux òptic RGB com pseudo mostres reals per a entrenament, utilitzant només informació 3D durant la inferència. Després, al Capítol 5 explorem els beneficis que s’obtenen de combinar els aprenentatges de problemes de classificació i regressió per la tasca d’estimació de flux òptic de manera conjunta. Finalment, al Capítol 6 posem en comú els mètodes anteriors i demostrem que mitjançant aquests processos independents podem abordar l’aprenentatge de problemes més complexos, com la segmentació i estimació del moviment de vehicles des de la nostra pròpia perspectiva

Agent problem solving by inductive and deductive program synthesis

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (p. 203-206).How do people learn abstract concepts unsupervised? Psychologists broadly recognize two types of concepts, declarative knowledge and procedural knowledge: know-what and know-how. While much work has focused on unsupervised learning of declarative concepts as clusters of features, there is much less clarity on the representation for procedural concepts and the methods for learning them. In this thesis, I claim that programs are a good representation for procedural knowledge, and that program synthesis is a promising mechanism for procedural learning. Prior attempts at AI program synthesis have taken a purely deductive approach to building provably corrent programs. This approach requires many axioms and non-trivial interaction with a human programmer. In contrast, this thesis introduces a new approach called SSGP (Sample Solve Generalize Prove), which combines inductive and deductive synthesis to autonomously synthesize programs with no extra knowledge outside of the program specification. The approach is to generate examples, solve the examples, generalize from the solutions, and then prove the generalization correct.This thesis presents two systems, Spec2Action and HELPS. Given a logical specification, Spec2Action determines the relations to change to perform simple operations on data structures. The main part of its task is to uncover the recursive structure of the domain from the purely logical input spec. HELPS generates sequential programs with loops and branches using STRIPS actions as the primitive statements. It solves generalizations of classic AI tasks like BlocksWorld. The two systems use SAT solving and other grounded reasoning techniques to solve the examples and generalize the solutions. To prove the abstracted hypotheses, the systems use a novel theorem prover for doing recursive proofs without an explicit induction axiom.by Harold Fox.Ph.D

Evolution of Cyberspace as a Landscape in Cyberpunk Novels

Millions of people enter cyberspace on some level daily. This new technology has infiltrated society rapidly since the first computers were networked. Interestingly, cyberpunk, a sub-genre of science fiction, depicted cyberspace many years before mainstream society had ever conceived of it. This thesis explores the changes in science fictional representations of cyberspace by examining William Gibson\u27s Neuromancer and Neal Stephenson\u27s Snow crash. In this work I contrast the metaphysical, found nature of the first cyberpunk representation of cyberspace with the homogenized, commodified reality of the last cyberpunk representation

Three perspectives on ukuthwasa: the view from traditional beliefs, western psychiatry and transpersonal psychology

Among the Xhosas, the healing sickness called intwaso is interptreted as a call by the ancestors to become a healer. Transpersonalists also see these initiatory illnesses as spiritual crises, while according to the widely accepted Western psychiatric view, illness is purely perceived in physical and psychological terms. A case study was conducted where a single participant who has undergone the process of ukuthwasa and is functioning as a traditional healer was interviewed. A series of interviews were done where information was gathered about significant experiences related to ukuthwasa process. Tapes were transcribed and a case narrative was written and interpreted using the traditional Xhosa beliefs, the western psychiatric and the transpersonal psychology perspectives. Strengths and weaknesses of each perspective were then examined