Implicit personalization in driving assistance: State-of-the-art and open issues

In recent decades, driving assistance systems have been evolving towards personalization for adapting to different drivers. With the consideration of driving preferences and driver characteristics, these systems become more acceptable and trustworthy. This article presents a survey on recent advances in implicit personalized driving assistance. We classify the collection of work into three main categories: 1) personalized Safe Driving Systems (SDS), 2) personalized Driver Monitoring Systems (DMS), and 3) personalized In-vehicle Information Systems (IVIS). For each category, we provide a comprehensive review of current applications and related techniques along with the discussion of industry status, benefits of personalization, application prospects, and future focal points. Both relevant driving datasets and open issues about personalized driving assistance are discussed to facilitate future research. By creating an organized categorization of the field, we hope that this survey could not only support future research and the development of new technologies for personalized driving assistance but also facilitate the application of these techniques within the driving automation community</h2

Spinoff, 1976

This report is divided into three sections: 1. The Research Payoff, 2. Technology Twice Used, and 3. Technology Utilization at Work. The first describes a wide variety of current space spinoffs of use in business or personal life, as well as the space explorations from which they have been derived. The second provides information on specific examples of technology transfer that are typical of the spinoffs resulting from NASA's Technology Utilization Program. The third briefly describes the different activities of the Technology Utilization Office, all of which have as their purpose the profitable utilization of aerospace technology

Aeronautical engineering: A continuing bibliography with indexes (supplement 318)

This bibliography lists 217 reports, articles, and other documents introduced into the NASA scientific and technical information system in June 1995. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment, and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

Impact Of Semantics, Physics And Adversarial Mechanisms In Deep Learning

Deep learning has greatly advanced the performance of algorithms on tasks such as image classification, speech enhancement, sound separation, and generative image models. However many current popular systems are driven by empirical rules that do not fully exploit the underlying physics of the data. Many speech and audio systems fix STFT preprocessing before their networks. Hyperspectral Image (HSI) methods often don't deliberately consider the spectral spatial trade off that is not present in normal images. Generative Adversarial Networks (GANs) that learn a generative distribution of images don't prioritize semantic labels of the training data. To meet these opportunities we propose to alter known deep learning methods to be more dependent on the semantic and physical underpinnings of the data to create better performing and more robust algorithms for sound separation and classification, image generation, and HSI segmentation. Our approaches take inspiration from from Harmonic Analysis, SVMs, and classical statistical detection theory, and further the state-of-the art in source separation, defense against audio adversarial attacks, HSI classification, and GANs. Recent deep learning approaches have achieved impressive performance on speech enhancement and separation tasks. However, these approaches have not been investigated for separating mixtures of arbitrary sounds of different types, a task we refer to as universal sound separation. To study this question, we develop a dataset of mixtures containing arbitrary sounds, and use it to investigate the space of mask-based separation architectures, varying both the overall network architecture and the framewise analysis-synthesis basis for signal transformations. We compare using a short-time Fourier transform (STFT) with a learnable basis at variable window sizes for the feature extraction stage of our sound separation network. We also compare the robustness to adversarial examples of speech classification networks that similarly hybridize established Time-frequency (TF) methods with learnable filter weights. We analyze HSI images for material classification. For hyperspectral image cubes TF methods decompose spectra into multi-spectral bands, while Neural Networks (NNs) incorporate spatial information across scales and model multiple levels of dependencies between spectral features. The Fourier scattering transform is an amalgamation of time-frequency representations with neural network architectures. We propose and test a three dimensional Fourier scattering method on hyperspectral datasets, and present results that indicate that the Fourier scattering transform is highly effective at representing spectral data when compared with other state-of-the-art methods. We study the spectral-spatial trade-off that our Scattering approach allows.We also use a similar multi-scale approach to develop a defense against audio adversarial attacks. We propose a unification of a computational model of speech processing in the brain with commercial wake-word networks to create a cortical network, and show that it can increase resistance to adversarial noise without a degradation in performance. Generative Adversarial Networks are an attractive approach to constructing generative models that mimic a target distribution, and typically use conditional information (cGANs) such as class labels to guide the training of the discriminator and the generator. We propose a loss that ensures generator updates are always class specific, rather than training a function that measures the information theoretic distance between the generative distribution and one target distribution, we generalize the successful hinge-loss that has become an essential ingredient of many GANs to the multi-class setting and use it to train a single generator classifier pair. While the canonical hinge loss made generator updates according to a class agnostic margin a real/fake discriminator learned, our multi-class hinge-loss GAN updates the generator according to many classification margins. With this modification, we are able to accelerate training and achieve state of the art Inception and FID scores on Imagenet128. We study the trade-off between class fidelity and overall diversity of generated images, and show modifications of our method can prioritize either each during training. We show that there is a limit to how closely classification and discrimination can be combined while maintaining sample diversity with some theoretical results on K+1 GANs

Spinoff 2007

In accordance with congressional mandates cited in the National Aeronautics and Space Act of 1958 and the Technology Utilization Act of 1962, NASA was directed to encourage greater use of the Agency's knowledge by providing a link between the NASA research community and those who might use the research for commercial or industrial products. For more than 40 years, NASA has nurtured partnerships with the private sector to facilitate the transfer of NASA-developed technologies. The benefits of these partnerships have reached throughout the economy and around the globe, as the resulting commercial products contributed to the development of services and technologies in the fields of health and medicine, transportation, public safety, consumer goods, environmental resources, computer technology, and industry. Since 1976, NASA Spinoff has profiled more than 1,500 of the most compelling of these technologies, annually highlighting the best and brightest of partnerships and innovations. Building on this dynamic history, NASA partnerships with the private sector continue to seek avenues by which technological achievements and innovations gleaned among the stars can be brought down to benefit our lives on Earth. NASA Spinoff highlights the Agency's most significant research and development activities and the successful transfer of NASA technology, showcasing the cutting-edge research being done by the Nation's top technologies and the practical benefits that come back down to Earth in the form of tangible products that make our lives better

Truck Trailer Classification Using Side-Fire Light Detection And Ranging (LiDAR) Data

Classification of vehicles into distinct groups is critical for many applications, including freight and commodity flow modeling, pavement management and design, tolling, air quality monitoring, and intelligent transportation systems. The Federal Highway Administration (FHWA) developed a standardized 13-category vehicle classification ruleset, which meets the needs of many traffic data user applications. However, some applications need high-resolution data for modeling and analysis. For example, the type of commodity being carried must be known in the freight modeling framework. Unfortunately, this information is not available at the state or metropolitan level, or it is expensive to obtain from current resources. Nevertheless, using current emerging technologies such as Light Detection and Ranging (LiDAR) data, it may be possible to predict commodity type from truck body types or trailers. For example, refrigerated trailers are commonly used to transport perishable produce and meat products, tank trailers are for fuel and other liquid products, and specialized trailers carry livestock. The main goal of this research is to develop methods using side-fired LiDAR data to distinguish between specific types of truck trailers beyond what is generally possible with traditional vehicle classification sensors (e.g., piezoelectric sensors and inductive loop detectors). A multi-array LiDAR sensor enables the construction of 3D-profiles of vehicles since it measures the distance to the object reflecting its emitted light. In this research 16-beam LiDAR sensor data are processed to estimate vehicle speed and extract useful information and features to classify semi-trailer trucks hauling ten different types of trailers: a reefer and non-reefer dry van, 20 ft and 40 ft intermodal containers, a 40 ft reefer intermodal container, platforms, tanks, car transporters, open-top van/dump and aggregated other types (i.e., livestock, logging, etc.). In addition to truck-trailer classification, methods are developed to detect empty and loaded platform semi-trailers. K-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), Adaptive Boosting (AdaBoost), and Support Vector Machines (SVM) supervised machine learning algorithms are implemented on the field data collected on a freeway segment that includes over seven-thousand trucks. The results show that different trailer body types and empty and loaded platform semi-trailers can be classified with a very high level of accuracy ranging from 85% to 98% and 99%, respectively. To enhance the accuracy by which multiple LiDAR frames belonging to the same truck are merged, a new algorithm is developed to estimate the speed while the truck is within the field of view of the sensor. This algorithm is based on tracking tires and utilizes line detection concepts from image processing. The proposed algorithm improves the results and allows creating more accurate 2D and 3D truck profiles as documented in this thesis

Estimation de complexité et localisation de véhicules à l'aide de l'apprentissage profond

L'analyse de la circulation routière est un domaine du génie civil permettant d'optimiser le déplacement des véhicules sur un système routier. Une étape importante de tout système d'analyse de trafic routier est la localisation des véhicules. Cette étape est effectuée à l'aide d'algorithmes d'apprentissage automatique, les réseaux de neurones à convolution. Ce mémoire présente deux nouvelles bases de données de localisation et classification de véhicules permettant l'évaluation de techniques d'apprentissage modernes. Celles-ci contiennent plus de 648 959 véhicules classifiés parmi 11 classes. Un atout majeur de la base de données de localisation est la très grande variété de scènes permettant une meilleure évaluation des techniques dans plusieurs contextes différents. Par la suite, on présente une technique d'estimation de la complexité d'une base de données. Cette technique permet d'analyser une base de données en un temps raisonnable et en apprendre plus sur sa composition. Elle permet d'estimer les performances atteignables par un algorithme d'apprentissage automatique sur cette base de données et d'en apprendre plus sur les relations entre les classes. Finalement, une ébauche d'article sur une technique d'apprentissage automatique pour l'estimation d'orientation des véhicules est présentée en annexe. Cette méthode propose l'ajout d'un composant permettant l'«apprentissage en ligne» du modèle ce qui permet d'adapter le modèle à la scène proposée. Malgré cet ajout, ce modèle reste fiable pour la localisation et la classification tout en gardant sa rapidité d'exécution

Spinoff 1976: A Bicentennial Report

Today we educate the world via communications satellites. We prospect for oil with land-resource satellites. We keep the tundra frozen with spacecraft-derived heat pipes, making the Alaskan pipeline possible. Our damaged hearts are run by pacemakers, our ailments diagnosed by computer. Highways are grooved to prevent skidding. Bridges soon may be protected from corrosion. Better lubricants, more powerful solar cells, more efficiently designed railroad cars have been spun from space technology. Thousands of technical innovations are the payoff after 18 years in space. Examples of how our national investment in space research and technology pays off will be described here, first as social, political, and economic stimuli and then in the exploration of space for its own purposes. The research payoff continues with current cases of space spinoffs that affect your job, your health, your mobility, your home, your environment, and your future

Electrification of Smart Cities

Electrification plays a key role in decarbonizing energy consumption for various sectors, including transportation, heating, and cooling. There are several essential infrastructures for a smart city, including smart grids and transportation networks. These infrastructures are the complementary solutions to successfully developing novel services, with enhanced energy efficiency and energy security. Five papers are published in this Special Issue that cover various key areas expanding the state-of-the-art in smart cities’ electrification, including transportation, healthcare, and advanced closed-circuit televisions for smart city surveillance

Near Sensor Artificial Intelligence on IoT Devices for Smart Cities

The IoT is in a continuous evolution thanks to new technologies that open the doors to various applications. While the structure of the IoT network remains the same over the years, specifically composed of a server, gateways, and nodes, their tasks change according to new challenges: the use of multimedia information and the large amount of data created by millions of devices forces the system to move from the cloud-centric approach to the thing-centric approach, where nodes partially process the information. Computing at the sensor node level solves well-known problems like scalability and privacy concerns. However, this study’s primary focus is on the impact that bringing the computation at the edge has on energy: continuous transmission of multimedia data drains the battery, and processing information on the node reduces the amount of data transferred to event-based alerts. Nevertheless, most of the foundational services for IoT applications are provided by AI. Due to this class of algorithms’ complexity, they are always delegated to GPUs or devices with an energy budget that is orders of magnitude more than an IoT node, which should be energy-neutral and powered only by energy harvesters. Enabling AI on IoT nodes is a challenging task. From the software side, this work explores the most recent compression techniques for NN, enabling the reduction of state-of-the-art networks to make them fit in microcontroller systems. From the hardware side, this thesis focuses on hardware selection. It compares the AI algorithms’ efficiency running on both well-established microcontrollers and state-of-the-art processors. An additional contribution towards energy-efficient AI is the exploration of hardware for acquisition and pre-processing of sound data, analyzing the data’s quality for further classification. Moreover, the combination of software and hardware co-design is the key point of this thesis to bring AI to the very edge of the IoT network