345 research outputs found

    Deep Causal Learning for Robotic Intelligence

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    This invited review discusses causal learning in the context of robotic intelligence. The paper introduced the psychological findings on causal learning in human cognition, then it introduced the traditional statistical solutions on causal discovery and causal inference. The paper reviewed recent deep causal learning algorithms with a focus on their architectures and the benefits of using deep nets and discussed the gap between deep causal learning and the needs of robotic intelligence

    Applications

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    Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

    DESIGN OF EFFICIENT IN-NETWORK DATA PROCESSING AND DISSEMINATION FOR VANETS

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    By providing vehicle-to-vehicle and vehicle-to-infrastructure wireless communications, vehicular ad hoc networks (VANETs), also known as the “networks on wheels”, can greatly enhance traffic safety, traffic efficiency and driving experience for intelligent transportation system (ITS). However, the unique features of VANETs, such as high mobility and uneven distribution of vehicular nodes, impose critical challenges of high efficiency and reliability for the implementation of VANETs. This dissertation is motivated by the great application potentials of VANETs in the design of efficient in-network data processing and dissemination. Considering the significance of message aggregation, data dissemination and data collection, this dissertation research targets at enhancing the traffic safety and traffic efficiency, as well as developing novel commercial applications, based on VANETs, following four aspects: 1) accurate and efficient message aggregation to detect on-road safety relevant events, 2) reliable data dissemination to reliably notify remote vehicles, 3) efficient and reliable spatial data collection from vehicular sensors, and 4) novel promising applications to exploit the commercial potentials of VANETs. Specifically, to enable cooperative detection of safety relevant events on the roads, the structure-less message aggregation (SLMA) scheme is proposed to improve communication efficiency and message accuracy. The scheme of relative position based message dissemination (RPB-MD) is proposed to reliably and efficiently disseminate messages to all intended vehicles in the zone-of-relevance in varying traffic density. Due to numerous vehicular sensor data available based on VANETs, the scheme of compressive sampling based data collection (CS-DC) is proposed to efficiently collect the spatial relevance data in a large scale, especially in the dense traffic. In addition, with novel and efficient solutions proposed for the application specific issues of data dissemination and data collection, several appealing value-added applications for VANETs are developed to exploit the commercial potentials of VANETs, namely general purpose automatic survey (GPAS), VANET-based ambient ad dissemination (VAAD) and VANET based vehicle performance monitoring and analysis (VehicleView). Thus, by improving the efficiency and reliability in in-network data processing and dissemination, including message aggregation, data dissemination and data collection, together with the development of novel promising applications, this dissertation will help push VANETs further to the stage of massive deployment

    Applications

    Get PDF
    Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

    Learned perception systems for self-driving vehicles

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    2022 Spring.Includes bibliographical references.Building self-driving vehicles is one of the most impactful technological challenges of modern artificial intelligence. Self-driving vehicles are widely anticipated to revolutionize the way people and freight move. In this dissertation, we present a collection of work that aims to improve the capability of the perception module, an essential module for safe and reliable autonomous driving. Specifically, it focuses on two perception topics: 1) Geo-localization (mapping) of spatially-compact static objects, and 2) Multi-target object detection and tracking of moving objects in the scene. Accurately estimating the position of static objects, such as traffic lights, from the moving camera of a self-driving car is a challenging problem. In this dissertation, we present a system that improves the localization of static objects by jointly optimizing the components of the system via learning. Our system is comprised of networks that perform: 1) 5DoF object pose estimation from a single image, 2) association of objects between pairs of frames, and 3) multi-object tracking to produce the final geo-localization of the static objects within the scene. We evaluate our approach using a publicly available data set, focusing on traffic lights due to data availability. For each component, we compare against contemporary alternatives and show significantly improved performance. We also show that the end-to-end system performance is further improved via joint training of the constituent models. Next, we propose an efficient joint detection and tracking model named DEFT, or "Detection Embeddings for Tracking." The proposed approach relies on an appearance-based object matching network jointly learned with an underlying object detection network. An LSTM is also added to capture motion constraints. DEFT has comparable accuracy and speed to the top methods on 2D online tracking leaderboards while having significant advantages in robustness when applied to more challenging tracking data. DEFT raises the bar on the nuScenes monocular 3D tracking challenge, more than doubling the performance of the previous top method (3.8x on AMOTA, 2.1x on MOTAR). We analyze the difference in performance between DEFT and the next best-published method on nuScenes and find that DEFT is more robust to occlusions and large inter-frame displacements, making it a superior choice for many use-cases. Third, we present an end-to-end model to solve the tasks of detection, tracking, and sequence modeling from raw sensor data, called Attention-based DEFT. Attention-based DEFT extends the original DEFT by adding an attentional encoder module that uses attention to compute tracklet embedding that 1) jointly reasons about the tracklet dependencies and interaction with other objects present in the scene and 2) captures the context and temporal information of the tracklet's past observations. The experimental results show that Attention-based DEFT performs favorably against or comparable to state-of-the-art trackers. Reasoning about the interactions between the actors in the scene allows Attention-based DEFT to boost the model tracking performance in heavily crowded and complex interactive scenes. We validate the sequence modeling effectiveness of the proposed approach by showing its superiority for velocity estimation task over other baseline methods on both simple and complex scenes. The experiments demonstrate the effectiveness of Attention-based DEFT for capturing spatio-temporal interaction of the crowd for velocity estimation task, which helps it to be more robust to handle complexities in densely crowded scenes. The experimental results show that all the joint models in this dissertation perform better than solving each problem independently

    Prediction-based techniques for the optimization of mobile networks

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    Mención Internacional en el título de doctorMobile cellular networks are complex system whose behavior is characterized by the superposition of several random phenomena, most of which, related to human activities, such as mobility, communications and network usage. However, when observed in their totality, the many individual components merge into more deterministic patterns and trends start to be identifiable and predictable. In this thesis we analyze a recent branch of network optimization that is commonly referred to as anticipatory networking and that entails the combination of prediction solutions and network optimization schemes. The main intuition behind anticipatory networking is that knowing in advance what is going on in the network can help understanding potentially severe problems and mitigate their impact by applying solution when they are still in their initial states. Conversely, network forecast might also indicate a future improvement in the overall network condition (i.e. load reduction or better signal quality reported from users). In such a case, resources can be assigned more sparingly requiring users to rely on buffered information while waiting for the better condition when it will be more convenient to grant more resources. In the beginning of this thesis we will survey the current anticipatory networking panorama and the many prediction and optimization solutions proposed so far. In the main body of the work, we will propose our novel solutions to the problem, the tools and methodologies we designed to evaluate them and to perform a real world evaluation of our schemes. By the end of this work it will be clear that not only is anticipatory networking a very promising theoretical framework, but also that it is feasible and it can deliver substantial benefit to current and next generation mobile networks. In fact, with both our theoretical and practical results we show evidences that more than one third of the resources can be saved and even larger gain can be achieved for data rate enhancements.Programa Oficial de Doctorado en Ingeniería TelemáticaPresidente: Albert Banchs Roca.- Presidente: Pablo Serrano Yañez-Mingot.- Secretario: Jorge Ortín Gracia.- Vocal: Guevara Noubi

    A survey on privacy in human mobility

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    In the last years we have witnessed a pervasive use of location-aware technologies such as vehicular GPS-enabled devices, RFID based tools, mobile phones, etc which generate collection and storing of a large amount of human mobility data. The powerful of this data has been recognized by both the scientific community and the industrial worlds. Human mobility data can be used for different scopes such as urban traffic management, urban planning, urban pollution estimation, etc. Unfortunately, data describing human mobility is sensitive, because people's whereabouts may allow re-identification of individuals in a de-identified database and the access to the places visited by indi-viduals may enable the inference of sensitive information such as religious belief, sexual preferences, health conditions, and so on. The literature reports many approaches aimed at overcoming privacy issues in mobility data, thus in this survey we discuss the advancements on privacy-preserving mo-bility data publishing. We first describe the adversarial attack and privacy models typically taken into consideration for mobility data, then we present frameworks for the privacy risk assessment and finally, we discuss three main categories of privacy-preserving strategies: methods based on anonymization of mobility data, methods based on the differential privacy models and methods which protect privacy by exploiting generative models for synthetic trajectory generation
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