With the advent of autonomous vehicles, in particular its adaptability to harsh conditions, the research and development of autonomous vehicles attract significant attention by not only academia but also practitioners. Due to the high risk, high cost, and difficulty to test autonomous vehicles under harsh conditions, the hardware-in-the-loop (HIL) scaled platform has been proposed as it is a safe, inexpensive, and effective test method. This platform system consists of scaled autonomous vehicle, scaled roadway, monitoring center, transmission device, positioning device, and computers. This paper uses a case of the development process of tracking control for high-speed U-turn to build the tracking control function. Further, a simplified vehicle dynamics model and a trajectory tracking algorithm have been considered to build the simulation test. The experiment results demonstrate the effectiveness of the HIL scaled platform.

/pdf/patavtt-a-hardware-in-the-loop-scaled-platform-for-testing-23kz94w497.pdf

PaTAVTT: A Hardware-in-the-Loop Scaled Platform for Testing Autonomous Vehicle Trajectory Tracking

Context-aware system for pre-triggering irreversible vehicle safety actuators

The Internet-of-Things (IoT) is an evolutionary paradigm seamlessly integrating an enormous number of smart objects within the Internet. Recently, with the rapid growth and universality of wearable technology, novel security threats are emerging at the system level as well as at edge nodes in IoT-based networks. In this study, we envision a future IoT scenario in which end-users are with smart wearable objects related to human brainwave retrieval. A novel transparent authentication system using brainwaves as bio-features for IoT-based networks is proposed. In brief, this study first provides a comprehensive review of transparent authentication in recent years and presents the state of the art of this important research field. Second, we investigate the feasibility of extracting long-term memory ability from users’ brainwaves. Third, we conduct the bio-features identified in the brainwaves of users as authentication tokens in the proposed authentication system which transparently performs continuous (or real-time) entity verification in the background without the need for direct input from the user. Experiment results demonstrate the efficacy of the proposed authentication system in achieving high verification accuracy.

You Think, Therefore You Are: Transparent Authentication System with Brainwave-Oriented Bio-Features for IoT Networks

Inherited retinal diseases cause severe visual deficits in children. They are classified in outer and inner retina diseases, and often cause blindness in childhood. The diagnosis for this type of illness is challenging, given the wide range of clinical and genetic causes (with over 200 causative genes). It is routinely based on a complex pattern of clinical tests, including invasive ones, not always appropriate for infants or young children. A different approach is thus needed, that exploits Chromatic Pupillometry, a technique increasingly used to assess outer and inner retina functions. This paper presents a novel Clinical Decision Support System (CDSS), based on Machine Learning using Chromatic Pupillometry in order to support diagnosis of Inherited retinal diseases in pediatric subjects. An approach that combines hardware and software is proposed: a dedicated medical equipment (pupillometer) is used with a purposely designed custom machine learning decision support system. Two distinct Support Vector Machines (SVMs), one for each eye, classify the features extracted from the pupillometric data. The designed CDSS has been used for diagnosis of Retinitis Pigmentosa in pediatric subjects. The results, obtained by combining the two SVMs in an ensemble model, show satisfactory performance of the system, that achieved 0.846 accuracy, 0.937 sensitivity and 0.786 specificity. This is the first study that applies machine learning to pupillometric data in order to diagnose a genetic disease in pediatric age.

/pdf/automatic-detection-of-genetic-diseases-in-pediatric-age-1l1bb9ahgk.pdf

Automatic Detection of Genetic Diseases in Pediatric Age Using Pupillometry

Biometric authentication involves various technologies to identify individuals by exploiting their unique, measurable physiological and behavioral characteristics. However, traditional biometric authentication systems (e.g., face recognition, iris, retina, voice, and fingerprint) are facing an increasing risk of being tricked by biometric tools such as anti-surveillance masks, contact lenses, vocoder, or fingerprint films. In this paper, we design a multimodal biometric authentication system named Deepkey, which uses both Electroencephalography (EEG) and gait signals to better protect against such risk. Deepkey consists of two key components: an Invalid ID Filter Model to block unauthorized subjects and an identification model based on attention-based Recurrent Neural Network (RNN) to identify a subject`s EEG IDs and gait IDs in parallel. The subject can only be granted access while all the components produce consistent evidence to match the user`s proclaimed identity. We implement Deepkey with a live deployment in our university and conduct extensive empirical experiments to study its technical feasibility in practice. DeepKey achieves the False Acceptance Rate (FAR) and the False Rejection Rate (FRR) of 0 and 1.0%, respectively. The preliminary results demonstrate that Deepkey is feasible, show consistent superior performance compared to a set of methods, and has the potential to be applied to the authentication deployment in real world settings.

DeepKey: An EEG and Gait Based Dual-Authentication System

Biometrics-based authentication is a method of personal identification that has some advantages over the password and object-based ones, mainly for the user, who doesn't need to carry or memorize anything. However, this kind of identification is also subject to problems. Besides the technology-related possibilities of fraud, such as system invasion, database corruption or algorithm injection, some of the common used bio-metric features can be faked. Furthermore, most cases of false rejection are related to the quality of the acquired sample. This paper proposes a multimodal bio-metric authentication method which incorporates the use of dynamic features of the human reflex and the iris pattern recognition for a better performance. A prototype system has been implemented and tested with 59 volunteers. Experimental results presented an EER of 2.44%.

Multimodal biometric authentication based on iris pattern and pupil light reflex

The paper discusses a new approach in contactless crash detection combining measurements of vehicle dynamics, exteroceptive sensors and vehicle-to-vehicle (V2V) communication data. The proposed architecture aims to activate vehicle safety functions prior an imminent collision to minimize the risk of suffering a major injury. An activation needs a precise prediction of time to collision (TTC), the crash severity (Cs) and other relevant crash parameters. This paper studies the contribution of V2V communication data to predict potential collisions and to realize a reliable activation. An algorithm is presented, that merges fused measurements of a video camera, a laser range finder (LRF) and ego vehicle motion sensors with V2V communication data to predict collisions. The benefit using V2V communication is demonstrated by evaluating collision prediction errors. This analysis is carried out based on experimental data produced by two scale model vehicles.

Advantages in Crash Severity Prediction Using Vehicle to Vehicle Communication

Compared to the state-of-the-art on integrated safety systems, earlier activated safety systems can further reduce the risk of suffering a major injury. Activation of such systems prior to a collision can be realized by analysing measurements of exteroceptive sensors (pre-crash data). An algorithm for estimating collisions in real-time using fused measurements of a video camera, a laser range finder (LRF), and ego vehicle motion sensors is presented. The threat posed by the actual driving situation is assessed by calculating a certain risk value, which is determined by combining the collision probability and crash severity estimations in a comprehensive way. A scale model vehicle is introduced to capture characteristics of the proposed system experimentally. First test runs show that the object width measurement is very accurate (absolute error of 5%) and the maximum time to collision (TTC) estimation error is around 17%, about 300ms before the impact. Comparing different obstacles and impact scenarios (e.g. small overlap vs. full frontal collision), the calculated risk is a promising new measure to early discriminate crash types.

A novel approach for intelligent pre-crash threat assessment systems

Many different biometric traits can be used for human identification, and each one has specific advantages over the others. However, there is a common problem for most of them: the spoofing vulnerability of the features. The authentication based on human reflexes is a recent proposal against frauds at the biometric level. The involuntary responses of the body cannot be removed from the person, naturally mimicked or reproduced by artificial means. This paper presents a proposal for the use of the features of the Pupillary Light Reflex for user authentication, using artificial neural networks for classification.

Biometric Authentication Based on Pupillary Light Reflex Using Neural Networks

Técnicas de identificação automática de indivíduos baseadas em características biométricas facilitam o controle de sistemas de segurança, além de reduzir o risco de falhas. Utilizando processamento de imagens capturadas à distância, métodos de reconhecimento facial ou por íris apresentam a vantagem de utilizar processos não invasivos. Ao mesmo tempo, os algoritmos mais usados nestas áreas baseiam-se apenas em características estáticas, o que facilita a ocorrência de fraudes. O uso de reflexos humanos para identificação impede a cópia de características, uma vez que são respostas dinâmicas involuntárias, além de garantir que a pessoa fisicamente presente apresenta sinal de vida. Este artigo apresenta um estudo preliminar da possibilidade de utilização de pupilometria dinâmica, isto é, da medição da variação do raio da pupila, para a identificação de indivíduos através do reflexo pupilar.

Utilização da Pupilometria Dinâmica para Identificação Pessoal Baseada em Reflexos Humanos

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