Learning Multi-Modal Self-Awareness Models Empowered by Active Inference for Autonomous Vehicles

For autonomous agents to coexist with the real world, it is essential to anticipate the dynamics and interactions in their surroundings. Autonomous agents can use models of the human brain to learn about responding to the actions of other participants in the environment and proactively coordinates with the dynamics. Modeling brain learning procedures is challenging for multiple reasons, such as stochasticity, multi-modality, and unobservant intents. A neglected problem has long been understanding and processing environmental perception data from the multisensorial information referring to the cognitive psychology level of the human brain process. The key to solving this problem is to construct a computing model with selective attention and self-learning ability for autonomous driving, which is supposed to possess the mechanism of memorizing, inferring, and experiential updating, enabling it to cope with the changes in an external world. Therefore, a practical self-driving approach should be open to more than just the traditional computing structure of perception, planning, decision-making, and control. It is necessary to explore a probabilistic framework that goes along with human brain attention, reasoning, learning, and decisionmaking mechanism concerning interactive behavior and build an intelligent system inspired by biological intelligence. This thesis presents a multi-modal self-awareness module for autonomous driving systems. The techniques proposed in this research are evaluated on their ability to model proper driving behavior in dynamic environments, which is vital in autonomous driving for both action planning and safe navigation. First, this thesis adapts generative incremental learning to the problem of imitation learning. It extends the imitation learning framework to work in the multi-agent setting where observations gathered from multiple agents are used to inform the training process of a learning agent, which tracks a dynamic target. Since driving has associated rules, the second part of this thesis introduces a method to provide optimal knowledge to the imitation learning agent through an active inference approach. Active inference is the selective information method gathering during prediction to increase a predictive machine learning model’s prediction performance. Finally, to address the inference complexity and solve the exploration-exploitation dilemma in unobserved environments, an exploring action-oriented model is introduced by pulling together imitation learning and active inference methods inspired by the brain learning procedure

Learning Multi-Modal Self-Awareness Models Empowered by Active Inference for Autonomous Vehicles

Mención Internacional en el título de doctorFor autonomous agents to coexist with the real world, it is essential to anticipate the dynamics and interactions in their surroundings. Autonomous agents can use models of the human brain to learn about responding to the actions of other participants in the environment and proactively coordinates with the dynamics. Modeling brain learning procedures is challenging for multiple reasons, such as stochasticity, multi-modality, and unobservant intents. A neglected problem has long been understanding and processing environmental perception data from the multisensorial information referring to the cognitive psychology level of the human brain process. The key to solving this problem is to construct a computing model with selective attention and self-learning ability for autonomous driving, which is supposed to possess the mechanism of memorizing, inferring, and experiential updating, enabling it to cope with the changes in an external world. Therefore, a practical selfdriving approach should be open to more than just the traditional computing structure of perception, planning, decision-making, and control. It is necessary to explore a probabilistic framework that goes along with human brain attention, reasoning, learning, and decisionmaking mechanism concerning interactive behavior and build an intelligent system inspired by biological intelligence. This thesis presents a multi-modal self-awareness module for autonomous driving systems. The techniques proposed in this research are evaluated on their ability to model proper driving behavior in dynamic environments, which is vital in autonomous driving for both action planning and safe navigation. First, this thesis adapts generative incremental learning to the problem of imitation learning. It extends the imitation learning framework to work in the multi-agent setting where observations gathered from multiple agents are used to inform the training process of a learning agent, which tracks a dynamic target. Since driving has associated rules, the second part of this thesis introduces a method to provide optimal knowledge to the imitation learning agent through an active inference approach. Active inference is the selective information method gathering during prediction to increase a predictive machine learning model’s prediction performance. Finally, to address the inference complexity and solve the exploration-exploitation dilemma in unobserved environments, an exploring action-oriented model is introduced by pulling together imitation learning and active inference methods inspired by the brain learning procedure.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Marco Carli.- Secretario: Víctor González Castro.- Vocal: Nicola Conc

Software architecture for self-driving navigation

Mención Internacional en el título de doctorThis dissertation is based on the development of the navigation software architecture for self-driving vehicles. The goal is very wide in terms of multidisciplinary fields over the different solutions provided, however, functional solutions for the implementation according to the software architecture has been proved and tested in the real research platform iCab (Intelligent Campus Automobile). The problems that the autonomous vehicles have to face are based accordingly as the three questions of navigation that each vehicle has to ask: Where am I, where should I go, and how can I go there. These questions are followed by the corresponding modules to solve that are divided into localization, planning, mapping, perception and control in addition to multitasking allocation, communication and Human-Machine Interaction. One more module is the self-awareness which is an optimal solution for detecting problems in the earliest stage. Throughout this document, the solution provided in form of a complete architecture for navigation describes the modules involved and the importance of software connections between them, generation of trajectories, mapping, localization and low level control. Finally, the results section describes scenarios and vehicle/software performance in terms of CPU for each module involved and the generation of trajectories, maps and control commands needed to move the vehicle from one point to another.Este documento es el resultado de cinco años de trabajo en el campo de los vehículos sin conductor donde, en el, se recoge el desarrollo de una arquitectura software de control para la navegación de este tipo de vehículos. El objetivo es muy ambicioso ya que para su desarrollo ha sido necesario el conocimiento de múltiples disciplinas como ingeniería electrónica, ingeniería informática, ingeniería de control, procesamiento de señales, mecánica y visión por computador. A pesar del vasto conocimiento necesario para lograr un vehículo funcional, se han alcanzado soluciones para cada uno de los problemas en que consiste la navegación autónoma, generando un vehículo autogobernado que toma decisiones por si mismo para evitar obstáculos y alcanzar los puntos de destino deseados. Los problemas principales que los vehículos autónomos tienen que hacer frente, están basados en tres preguntas principales: donde estoy, donde tengo que ir y como voy. Para responder a estas tres preguntas se ha dividido la arquitectura en los módulos siguientes: localización, planificación, mapeado del entorno y control junto con módulos extra para dotar al sistema de mas aptitudes y mejor funcionamiento como por ejemplo la comunicación entre vehículos, peatones e infraestructuras, la interacción humano máquina, la gestión de tareas con múltiples vehículos o la propia consciencia del vehículo en cuanto a su estado de baterías, mantenimiento, sensores conectados o desconectados, etc. A través de este documento, la solución proporcionada a cada uno de los módulos involucrados refleja la importancia de las conexiones de software y la comunicación entre procesos dentro de la arquitectura ya sea para la generación de trayectorias, la creación de los mapas a tiempo, la localización precisa en el entorno, o los comandos generados para gobernar el vehículo. Así mismo, en el apartado de resultados se pone de manifiesto la importancia de cumplir los plazos de compartición de mensajes y optimizar el sistema para no sobrecargar la CPU.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Felipe Jiménez Alonso.- Secretario: Agapito Ismael Ledezma Espino.- Vocal: Alessio Malizi

Generative Models for Novelty Detection Applications in abnormal event and situational changedetection from data series

Novelty detection is a process for distinguishing the observations that differ in some respect from the observations that the model is trained on. Novelty detection is one of the fundamental requirements of a good classification or identification system since sometimes the test data contains observations that were not known at the training time. In other words, the novelty class is often is not presented during the training phase or not well defined. In light of the above, one-class classifiers and generative methods can efficiently model such problems. However, due to the unavailability of data from the novelty class, training an end-to-end model is a challenging task itself. Therefore, detecting the Novel classes in unsupervised and semi-supervised settings is a crucial step in such tasks. In this thesis, we propose several methods to model the novelty detection problem in unsupervised and semi-supervised fashion. The proposed frameworks applied to different related applications of anomaly and outlier detection tasks. The results show the superior of our proposed methods in compare to the baselines and state-of-the-art methods

Dynamic representations for autonomous driving

\u3cp\u3eThis paper presents a method for observational learning in autonomous agents. A formalism based on deep learning implementations of variational methods and Bayesian filtering theory is presented. It is explained how the proposed method is capable of modeling the environment to mimic behaviors in an observed interaction by building internal representations and discovering temporal and causal relations. The method is evaluated in a typical surveillance scenario, i.e., perimeter monitoring. It is shown that the vehicle learns how to drive itself by simultaneously observing its surroundings and the actions taken by a human driver for a given task. That is achieved by embedding knowledge regarding perception-action couplings in dynamic representational states used to produce action flows. Thereby, representations link sensory data to control signals. In particular, the representational states associate visual features to stable action concepts such as turning or going straight.\u3c/p\u3

Dynamic representations for autonomous driving

This paper presents a method for observational learning in autonomous agents. A formalism based on deep learning implementations of variational methods and Bayesian filtering theory is presented. It is explained how the proposed method is capable of modeling the environment to mimic behaviors in an observed interaction by building internal representations and discovering temporal and causal relations. The method is evaluated in a typical surveillance scenario, i.e., perimeter monitoring. It is shown that the vehicle learns how to drive itself by simultaneously observing its surroundings and the actions taken by a human driver for a given task. That is achieved by embedding knowledge regarding perception-action couplings in dynamic representational states used to produce action flows. Thereby, representations link sensory data to control signals. In particular, the representational states associate visual features to stable action concepts such as turning or going straight