Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples

Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). arXiv admin note: substantial text overlap with arXiv:1610.0770

SafeRNet: Safe Transportation Routing in the era of Internet of Vehicles and Mobile Crowd Sensing

World wide road traffic fatality and accident rates are high, and this is true even in technologically advanced countries like the USA. Despite the advances in Intelligent Transportation Systems, safe transportation routing i.e., finding safest routes is largely an overlooked paradigm. In recent years, large amount of traffic data has been produced by people, Internet of Vehicles and Internet of Things (IoT). Also, thanks to advances in cloud computing and proliferation of mobile communication technologies, it is now possible to perform analysis on vast amount of generated data (crowd sourced) and deliver the result back to users in real time. This paper proposes SafeRNet, a safe route computation framework which takes advantage of these technologies to analyze streaming traffic data and historical data to effectively infer safe routes and deliver them back to users in real time. SafeRNet utilizes Bayesian network to formulate safe route model. Furthermore, a case study is presented to demonstrate the effectiveness of our approach using real traffic data. SafeRNet intends to improve drivers safety in a modern technology rich transportation system.Comment: Paper was accepted at the 14th IEEE Consumer Communications & Networking Conference (CCNC 2017

Design and Evaluation of a Traffic Safety System based on Vehicular Networks for the Next Generation of Intelligent Vehicles

La integración de las tecnologías de las telecomunicaciones en el sector del automóvil permitirá a los vehículos intercambiar información mediante Redes Vehiculares, ofreciendo numerosas posibilidades. Esta tesis se centra en la mejora de la seguridad vial y la reducción de la siniestralidad mediante Sistemas Inteligentes de Transporte (ITS). El primer paso consiste en obtener una difusión eficiente de los mensajes de advertencia sobre situaciones potencialmente peligrosas. Hemos desarrollado un marco para simular el intercambio de mensajes entre vehículos, utilizado para proponer esquemas eficientes de difusión. También demostramos que la disposición de las calles tiene gran influencia sobre la eficiencia del proceso. Nuestros algoritmos de difusión son parte de una arquitectura más amplia (e-NOTIFY) capaz de detectar accidentes de tráfico e informar a los servicios de emergencia. El desarrollo y evaluación de un prototipo demostró la viabilidad del sistema y cómo podría ayudar a reducir el número de víctimas en carretera

Response-based methods to measure road surface irregularity: a state-of-the-art review

"jats:sec" "jats:title"Purpose"/jats:title" "jats:p"With the development of smart technologies, Internet of Things and inexpensive onboard sensors, many response-based methods to evaluate road surface conditions have emerged in the recent decade. Various techniques and systems have been developed to measure road profiles and detect road anomalies for multiple purposes such as expedient maintenance of pavements and adaptive control of vehicle dynamics to improve ride comfort and ride handling. A holistic review of studies into modern response-based techniques for road pavement applications is found to be lacking. Herein, the focus of this article is threefold: to provide an overview of the state-of-the-art response-based methods, to highlight key differences between methods and thereby to propose key focus areas for future research."/jats:p" "/jats:sec" "jats:sec" "jats:title"Methods"/jats:title" "jats:p"Available articles regarding response-based methods to measure road surface condition were collected mainly from “Scopus” database and partially from “Google Scholar”. The search period is limited to the recent 15 years. Among the 130 reviewed documents, 37% are for road profile reconstruction, 39% for pothole detection and the remaining 24% for roughness index estimation."/jats:p" "/jats:sec" "jats:sec" "jats:title"Results"/jats:title" "jats:p"The results show that machine-learning techniques/data-driven methods have been used intensively with promising results but the disadvantages on data dependence have limited its application in some instances as compared to analytical/data processing methods. Recent algorithms to reconstruct/estimate road profiles are based mainly on passive suspension and quarter-vehicle-model, utilise fewer key parameters, being independent on speed variation and less computation for real-time/online applications. On the other hand, algorithms for pothole detection and road roughness index estimation are increasingly focusing on GPS accuracy, data aggregation and crowdsourcing platform for large-scale application. However, a novel and comprehensive system that is comparable to existing International Roughness Index and conventional Pavement Management System is still lacking."/jats:p" "/jats:sec Document type: Articl

A Context Aware Classification System for Monitoring Driver’s Distraction Levels

Understanding the safety measures regarding developing self-driving futuristic cars is a concern for decision-makers, civil society, consumer groups, and manufacturers. The researchers are trying to thoroughly test and simulate various driving contexts to make these cars fully secure for road users. Including the vehicle’ surroundings offer an ideal way to monitor context-aware situations and incorporate the various hazards. In this regard, different studies have analysed drivers’ behaviour under different case scenarios and scrutinised the external environment to obtain a holistic view of vehicles and the environment. Studies showed that the primary cause of road accidents is driver distraction, and there is a thin line that separates the transition from careless to dangerous. While there has been a significant improvement in advanced driver assistance systems, the current measures neither detect the severity of the distraction levels nor the context-aware, which can aid in preventing accidents. Also, no compact study provides a complete model for transitioning control from the driver to the vehicle when a high degree of distraction is detected. The current study proposes a context-aware severity model to detect safety issues related to driver’s distractions, considering the physiological attributes, the activities, and context-aware situations such as environment and vehicle. Thereby, a novel three-phase Fast Recurrent Convolutional Neural Network (Fast-RCNN) architecture addresses the physiological attributes. Secondly, a novel two-tier FRCNN-LSTM framework is devised to classify the severity of driver distraction. Thirdly, a Dynamic Bayesian Network (DBN) for the prediction of driver distraction. The study further proposes the Multiclass Driver Distraction Risk Assessment (MDDRA) model, which can be adopted in a context-aware driving distraction scenario. Finally, a 3-way hybrid CNN-DBN-LSTM multiclass degree of driver distraction according to severity level is developed. In addition, a Hidden Markov Driver Distraction Severity Model (HMDDSM) for the transitioning of control from the driver to the vehicle when a high degree of distraction is detected. This work tests and evaluates the proposed models using the multi-view TeleFOT naturalistic driving study data and the American University of Cairo dataset (AUCD). The evaluation of the developed models was performed using cross-correlation, hybrid cross-correlations, K-Folds validation. The results show that the technique effectively learns and adopts safety measures related to the severity of driver distraction. In addition, the results also show that while a driver is in a dangerous distraction state, the control can be shifted from driver to vehicle in a systematic manner

Optimization of deep learning algorithms for an autonomous RC vehicle

Dissertação de mestrado em Engenharia InformáticaThis dissertation aims to evaluate and improve the performance of deep learning (DL) algorithms to autonomously drive a vehicle, using a Remo Car (an RC vehicle) as testbed. The RC vehicle was built with a 1:10 scaled remote controlled car and fitted with an embedded system and a video camera to capture and process real-time image data. Two different embedded systems were comparatively evaluated: an homogeneous system, a Raspberry Pi 4, and an heterogeneous system, a NVidia Jetson Nano. The Raspberry Pi 4 with an advanced 4-core ARM device supports multiprocessing, while the Jetson Nano, also with a 4-core ARM device, has an integrated accelerator, a 128 CUDA-core NVidia GPU. The captured video is processed with convolutional neural networks (CNNs), which interpret image data of the vehicle’s surroundings and predict critical data, such as lane view and steering angle, to provide mechanisms to drive on its own, following a predefined path. To improve the driving performance of the RC vehicle, this work analysed the programmed DL algorithms, namely different computer vision approaches for object detection and image classification, aiming to explore DL techniques and improve their performance at the inference phase. The work also analysed the computational efficiency of the control software, while running intense and complex deep learning tasks in the embedded devices, and fully explored the advanced characteristics and instructions provided by the two embedded systems in the vehicle. Different machine learning (ML) libraries and frameworks were analysed and evaluated: TensorFlow, TensorFlow Lite, Arm NN, PyArmNN and TensorRT. They play a key role to deploy the relevant algorithms and to fully engage the hardware capabilities. The original algorithm was successfully optimized and both embedded systems could perfectly handle this workload. To understand the computational limits of both devices, an additional and heavy DL algorithm was developed that aimed to detect traffic signs. The homogeneous system, the Raspberry Pi 4, could not deliver feasible low-latency values, hence the detection of traffic signs was not possible in real-time. However, a great performance improvement was achieved using the heterogeneous system, Jetson Nano, enabling their CUDA-cores to process the additional workload.Esta dissertação tem como objetivo avaliar e melhorar o desempenho de algoritmos de deep learning (DL) orientados à condução autónoma de veículos, usando um carro controlado remotamente como ambiente de teste. O carro foi construído usando um modelo de um veículo de controlo remoto de escala 1:10, onde foi colocado um sistema embebido e uma câmera de vídeo para capturar e processar imagem em tempo real. Dois sistemas embebidos foram comparativamente avaliados: um sistema homogéneo, um Raspberry Pi 4, e um sistema heterogéneo, uma NVidia Jetson Nano. O Raspberry Pi 4 possui um processador ARM com 4 núcleos, suportando multiprocessamento. A Jetson Nano, também com um processador ARM de 4 núcleos, possui uma unidade adicional de processamento com 128 núcleos do tipo CUDA-core. O vídeo capturado e processado usando redes neuronais convolucionais (CNN), interpretando o meio envolvente do veículo e prevendo dados cruciais, como a visibilidade da linha da estrada e o angulo de direção, de forma a que o veículo consiga conduzir de forma autónoma num determinado ambiente. De forma a melhorar o desempenho da condução autónoma do veículo, diferentes algoritmos de deep learning foram analisados, nomeadamente diferentes abordagens de visão por computador para detecção e classificação de imagens, com o objetivo de explorar técnicas de CNN e melhorar o seu desempenho na fase de inferência. A dissertação também analisou a eficiência computacional do software usado para a execução de tarefas de aprendizagem profunda intensas e complexas nos dispositivos embebidos, e explorou completamente as características avançadas e as instruções fornecidas pelos dois sistemas embebidos no veículo. Diferentes bibliotecas e frameworks de machine learning foram analisadas e avaliadas: TensorFlow, TensorFlow Lite, Arm NN, PyArmNN e TensorRT. Estes desempenham um papel fulcral no provisionamento dos algoritmos de deep learning para tirar máximo partido das capacidades do hardware usado. O algoritmo original foi otimizado com sucesso e ambos os sistemas embebidos conseguiram executar os algoritmos com pouco esforço. Assim, para entender os limites computacionais de ambos os dispositivos, um algoritmo adicional mais complexo de deep learning foi desenvolvido com o objetivo de detectar sinais de transito. O sistema homogéneo, o Raspberry Pi 4, não conseguiu entregar valores viáveis de baixa latência, portanto, a detecção de sinais de trânsito não foi possível em tempo real, usando este sistema. No entanto, foi alcançada uma grande melhoria de desempenho usando o sistema heterogeneo, Jetson Nano, que usaram os seus núcleos CUDA adicionais para processar a carga computacional mais intensa

Smart automotive technology adherence to the law: (de)constructing road rules for autonomous system development, verification and safety

Driving is an intuitive task that requires skill, constant alertness and vigilance for unexpected events. The driving task also requires long concentration spans, focusing on the entire task for prolonged periods, and sophisticated negotiation skills with other road users including wild animals. Modern motor vehicles include an array of smart assistive and autonomous driving systems capable of subsuming some, most, or in limited cases, all of the driving task. Building these smart automotive systems requires software developers with highly technical software engineering skills, and now a lawyer’s in-depth knowledge of traffic legislation as well. This article presents an approach for deconstructing the complicated legalese of traffic law and representing its requirements and flow. Our approach (de)constructs road rules in legal terminology and specifies them in ‘structured English logic’ that is expressed as ‘Boolean logic’ for automation and ‘Lawmaps’ for visualization. We demonstrate an example using these tools leading to the construction and validation of a ‘Bayesian Network model’. We strongly believe these tools to be approachable by programmers and the general public, useful in development of Artificial Intelligence to underpin motor vehicle smart systems, and in validation to ensure these systems are considerate of the law when making decisions.fals

ICWIM8 - 8th Conference on Weigh-in-Motion - Book of proceedings

ICWIM8, 8th International Conference on Weigh-in-Motion, PRAGUE, TCHÈQUE, RÉPUBLIQUE, 20-/05/2019 - 24/05/2019The conference addresses the broad range of topics related to on-road and in-vehicle WIM technology, its research, installation and operation and use of mass data across variable end-uses. Innovative technologies and experiences of WIM system implementation are presented. Application of WIM data to infrastructure, mainly bridges and pavements, is among the main topics. However, the most demanding application is now WIM for enforcement, and the greatest challenge is WIM for direct enforcement. Most of the countries and road authorities should ensure a full compliance of heavy vehicle weights and dimensions with the current regulations. Another challenging objective is to extend the lifetimes of existing road assets, despite of increasing heavy vehicle loads and flow, and without compromising with the structural safety. Fair competition and road charging also require accurately monitoring commercial vehicle weights by WIM. WIM contributes to a global ITS (Intelligent Transport System) providing useful data on heavy good vehicles to implement Performance Based Standards (PBS) and Intelligent Access Programme (IAP, Australia) or Smart Infrastructure Access Programme (SIAP). The conference reports the latest research and developments since the last conference in 2016, from all around the World. More than 150 delegates from 33 countries and all continents are attending ICWIM8, mixing academics, end users, decision makers and WIM vendors. An industrial exhibition is organized jointly with the conference