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The world is facing a great technological transformation towards fully autonomous vehicles, where optimists predict that by 2030 autonomous vehicles will be sufficiently reliable, affordable, and common to displace most human driving. To cope with these trends, reliable perception systems must enable vehicles to hear and see all their surroundings, with light detection and ranging (LiDAR) sensors being a key instrument for recreating a 3D visualization of the world in real time. However, perception systems must rely on accurate measurements of the environment. Thus, these intelligent sensors must be calibrated and benchmarked before being placed on the market or assembled in a car. This article presents an Evaluation and Testing Platform for Automotive LiDAR sensors, with the main goal of testing both commercially available sensors and new sensor prototypes currently under development in Bosch Car Multimedia Portugal. The testing system can benchmark any LiDAR sensor under different conditions, recreating the expected driving environment in which such devices normally operate. To characterize and validate the sensor under test, the platform evaluates several parameters, such as the field of view (FoV), angular resolution, sensor’s range, etc., based only on the point cloud output. This project is the result of a partnership between the University of Minho and Bosch Car Multimedia Portugal., This work was supported by the European Structural and Investment Funds in the FEDER component through the Operational Competitiveness and Internationalization Programme (COM-PETE 2020), Project nº 037902, Funding Reference POCI-01-0247-FEDER-037902.

The automotive industry is facing an unprecedented technological transformation towards fully autonomous vehicles. Optimists predict that, by 2030, cars will be sufficiently reliable, affordable, and common to displace most current human driving tasks. To cope with these trends, autonomous vehicles require reliable perception systems to hear and see all the surroundings, being light detection and ranging (LiDAR) sensors a key instrument for recreating a 3D visualization of the world. However, for a reliable operation, such systems require LiDAR sensors to provide high-resolution 3D representations of the car’s vicinity, which results in millions of data points to be processed in real-time. With this article we propose the ALFA-Pi, a data packet decoder and reconstruction system fully deployed on an embedded reconfigurable hardware platform. By resorting to field-programmable gate array (FPGA) technology, ALFAPi is able to interface different LiDAR sensors at the same time, while providing custom representation outputs to high-level perception systems. By accelerating the LiDAR interface, the proposed system outperforms current software-only approaches, achieving lower latency in the data acquisition and data decoding tasks while reaching high performance ratios., (undefined)

The automotive industry keeps pushing technology towards fully autonomous vehicles. In the near future, cars are expected to be sufficiently reliable, affordable, and common to simplify several driving tasks. To recreate the surrounding environment in real-time, an autonomous vehicle requires reliable and accurate multi-sensor perception systems, which include RADARs, Cameras, and LiDAR sensors. LiDAR sensors are becoming very popular since they are able to create 2D/3D representations of the car’s vicinity in different light and weather conditions, even at high speeds. However, to achieve highresolution visualizations, a LiDAR sensor must provide millions of data points to be processed, which often requires powerful processing systems. With this article, we propose an acceleratedinterface for LiDAR sensors composed of a data packet decoder and a reconstruction system, fully deployed on an embedded reconfigurable hardware platform with field-programmable gate array (FPGA) technology. The proposed FPGA-based solution is able to interface different LiDAR sensors at the same time, outperforming software-only approaches by reaching lower latency and high performance results.

The interest in developing and deploying fully autonomous vehicles on our public roads has come to a full swing. Driverless capabilities, widely spread in modern vehi cles through advanced driver-assistance systems (ADAS), require highly reliable perception features to navigate the environment, being light detection and ranging (LiDAR) sen sors a key instrument in detecting the distance and speed of nearby obstacles and in providing high-resolution 3D rep resentations of the surroundings in real-time. However, and despite being assumed as a game-changer in the autonomous driving paradigm, LiDAR sensors can be very sensitive to adverse weather conditions, which can severely affect the vehicle’s perception system behavior. Aiming at improving the LiDAR operation in challenging weather conditions, which contributes to achieving higher driving automation levels defined by the Society of Automotive Engineers (SAE), this article proposes a weather denoising method called Dynamic light-Intensity Outlier Removal (DIOR). DIOR combines two approaches of the state-of-the-art, the dynamic radius outlier removal (DROR) and the low-intensity outlier removal (LIOR) algorithms, supported by an embedded reconfigurable hardware platform. By resorting to field-programmable gate array (FPGA) technology, DIOR can outperform state-of-the-art outlier removal solutions, achieving better accuracy and performance while guaranteeing the real-time requirements., This work was supported by the Fundacao para a Ciencia e Tecnologia (FCT) within the Research and Development Units Project Scope under Grant UIDB/00319/2020.

Nowadays, and more than a decade after the first steps towards autonomous driving, we keep heading to achieve fully autonomous vehicles on our roads, with LiDAR sensors being a key instrument for the success of this technology. Such advances trigger the emergence of new players in the automotive industry, and along with car manufacturers, this sector represents a multibillion-dollar market where everyone wants to take a share. To understand recent advances and technologies behind LiDAR, this article presents a survey on LiDAR sensors for the automotive industry. With this work, we show the measurement principles and imaging techniques currently being used, going through a review of commercial systems and development solutions available in the market today. Furthermore, we highlight the current and future challenges, providing insights on how both research and industry can step towards better LiDAR solutions., This work was supported by the Fundação para a Ciência e a Tecnologia (FCT) within the Research and Development Units Project Scope under Grant UIDB/00319/2020.

Dissertação de mestrado em Engenharia Eletrónica Industrial e Computadores, Embedded system’s evolution is notorious and due to the complexity growth, these systems possess more general purpose behaviour instead of its original single purpose features. Naturally, virtualization started to impact this matter. This technology decreases the hardware costs since it allows to run several software components on the same hardware. Although virtualization begun as a pure software layer, many companies started to provide hardware solutions to assist it. Despite ARM TrustZone technology being a security extension, many developers realized that it was possible to use this extension to support development of hypervisors. With TrustZone, hypervisors can ensure one of the most important features in virtualization: isolation between guests. However, this hardware technology revealed some vulnerabilities and since the whole system is TrustZone dependent, the virtualization can be compromised. To address this problem, this thesis proposes an hybrid software/hardware mechanism to handle failures of TrustZone-based hypervisors. By using the processor’s abort exceptions and hash keys, this project detects system malfunctions caused by imperfect designs or even deliberate attacks. Additionally, it provides a restoration model by checkpoints which allows a system recovery without major throwbacks. The implemented solution was deployed on TrustZone-based LTZVisor, an open-source and in-house hypervisor, and the revealed results are appealing. With a 6.5% memory footprint increase and in the worst case scenario, an increment of 23% in context switching time, it is possible to detect secure memory invasions and recover the system. Despite of the hypervisor memory footprint increment and latency addition, the reliability and availability that the system bring to the LTZVisor are unquestionable., A evolução dos sistemas embebidos é notória e, devido ao aumento da sua complexidade, estes sistemas cada vez mais possuem um comportamento de propósito geral, em vez das suas características originais de propósito único. Naturalmente, a virtualização começou a ter impacto sobre este meio, uma vez que permite executar vários componentes de software no mesmo hardware, diminuindo os custos de hardware. Embora a virtualização tenha começado como uma camada de software pura, muitas empresas começaram a fornecer soluções de hardware para auxiliá-lo. Apesar da TrustZone ter sido projetada pela ARM para ser uma extensão de segurança, muitos desenvolvedores perceberam que era possível usá-la para suporte ao desenvolvimento de hipervisores. Com a TrustZone, os hipervisores podem garantir uma das premissas mais importantes da virtualização: isolamento entre hóspedes. No entanto, esta tecnologia de hardware revelou algumas vulnerabilidades e, sendo todo o sistema dependente da TrustZone, a virtualização pode ficar comprometida. Para solucionar o problema, esta tese propõe um mecanismo híbrido de software/ hardware para lidar com as falhas em hipervisores baseados em TrustZone. Usando as excepções do processador e chaves de hash, este projecto detecta defeitos no sistema causados por imperfeições no design e também ataques intencionais. Além disso, este fornece um modelo de restauração por pontos de verificação, permitindo uma recuperação do sistema sem grandes retrocessos. A solução foi implementada no LTZVisor, um hipervisor em código aberto e desenvolvido no ESRG, sendo que os resultados revelados são satisfatórios. Com um aumento de 6,5% da memória usada e um incremento, no pior caso, de 23% no tempo de troca de contexto, é possível detectar invasões de memória segura e recuperar o sistema. Apesar do incremento de memória do hypervisor e da adição de latência, a confiabilidade e a disponibilidade que o sistema oferece ao LTZVisor são inquestionáveis.

International audience; Transcatheter aortic valve replacement (TAVR) has revolutionized management of high-risk patients with severe aortic stenosis. However, survival and the incidence of severe complications have been assessed in relatively small populations and/or with limited follow-up.; This report details late clinical outcome and its determinants in the FRANCE-2 (FRench Aortic National CoreValve and Edwards) registry.; The FRANCE-2 registry prospectively included all TAVRs performed in France. Follow-up was scheduled at 30 days, at 6 months, and annually from 1 to 5 years. Standardized VARC (Valve Academic Research Consortium) outcome definitions were used.; A total of 4,201 patients were enrolled between January 2010 and January 2012 in 34 centers. Approaches were transarterial (transfemoral 73%, transapical 18%, subclavian 6%, and transaortic or transcarotid 3%) or, in 18% of patients, transapical. Median follow-up was 3.8 years. Vital status was available for 97.2% of patients at 3 years. The 3-year all-cause mortality was 42.0% and cardiovascular mortality was 17.5%. In a multivariate model, predictors of 3-year all-cause mortality were male sex (p < 0.001), low body mass index, (p < 0.001), atrial fibrillation (p < 0.001), dialysis (p < 0.001), New York Heart Association functional class III or IV (p < 0.001), higher logistic EuroSCORE (p