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Sea clutter has been a long-standing issue in old and modern radars. Under this context, the K-distribution has emerged as a promising clutter model to accurately mimic sea signal variations in a large variety of radar systems. To guarantee an adequate radar performance in the presence of sea clutter, the family of constant false-alarm rate (CFAR) detectors has been commonly used. In particular, due to its adequate balance between performance and implementation, the cell-averaging CFAR (CA-CFAR) detector has been considered an attractive detection mechanism to enhance radar performance over various clutter environments. In this work, we assess radar performance considering a CA-CFAR detector operating over K-distributed sea clutter with fully correlated texture. More precisely, we derive novel closed-form expressions for the probability of detection ($P_{\text {D}}$) and the probability of false alarm ($P_{\text {FA}}$) that can be readily evaluated using any mathematical software. Monte-Carlo (MC) simulations corroborate our analytical findings. [ABSTRACT FROM AUTHOR]

Most sensor setups for onboard autonomous perception are composed of LiDARs and vision systems, as they provide complementary information that improves the reliability of the different algorithms necessary to obtain a robust scene understanding. However, the effective use of information from different sources requires an accurate calibration between the sensors involved, which usually implies a tedious and burdensome process. We present a method to calibrate the extrinsic parameters of any pair of sensors involving LiDARs, monocular or stereo cameras, of the same or different modalities. The procedure is composed of two stages: first, reference points belonging to a custom calibration target are extracted from the data provided by the sensors to be calibrated, and second, the optimal rigid transformation is found through the registration of both point sets. The proposed approach can handle devices with very different resolutions and poses, as usually found in vehicle setups. In order to assess the performance of the proposed method, a novel evaluation suite built on top of a popular simulation framework is introduced. Experiments on the synthetic environment show that our calibration algorithm significantly outperforms existing methods, whereas real data tests corroborate the results obtained in the evaluation suite. Open-source code is available at https://github.com/beltransen/velo2cam_calibration. [ABSTRACT FROM AUTHOR]

This article presents the results of a two-week robot taxi service demonstration in peri-urban and rural areas. A fully robotized Renault Zoe was available for general public use in Rambouillet, France. The driving zone included several complex scenarios, such as a narrow two-way road, a tunnel where the lanes reduced from two to one, and roundabouts, enabling an evaluation of the vehicle’s capabilities. The article describes the scientific and technical development of the car’s perception, navigation, and control to carry out the experiment. The results indicate that even though the vehicle was able to autonomously navigate suburban areas and the countryside, technical challenges remain that limit the car’s integration with the transport system. [ABSTRACT FROM AUTHOR]

Modern radar systems use square-law detectors to search and track fluctuating targets embedded in Weibull-distributed ground clutter. However, the theoretical performance analysis of square-law detectors in the presence of Weibull clutter leads to cumbersome mathematical formulations. Some studies have circumvented this problem by using approximations or mathematical artifacts to simplify calculations. In this work, we derive a closed-form and exact expression for the probability of detection (PD) of a square-law detector in the presence of exponential targets and Weibull-distributed ground clutter, given in terms of the Fox H-function. Unlike previous studies, no approximations nor simplifying assumptions are made throughout our analysis. Furthermore, we derive a fast convergent series for the referred PD by exploiting the orthogonal selection of poles in Cauchy’s residue theorem. In passing, we also obtain closed-form solutions and series representations for the probability density function and the cumulative distribution function of the sum statistics that govern the output of a square-law detector. Numerical results and Monte Carlo simulations corroborate the validity of our expressions. [ABSTRACT FROM AUTHOR]

Recently, the Brazilian Electricity Regulatory Agency (ANEEL) regularized the electricity prepayment system and opened a general discussion about the technologies, resources, and implementations for the prepayment metering system. In several countries, the consumer charges the value equivalent to the kilowatt-hours that he wishes to buy for use. However, some consumers purchase credit with the expectation that the charge remains for a specific time, but they may be surprised with the credit end before the expected time. For this reason, this paper proposes automatic consumption management for prepaid meters that analyzes the consumer appliance behavior, performs the temporal analysis of consumption by appliances, defines the cost management per load with high energy consumption and sends sound signals to the consumer ends the appliance usage. The objective of this management system is to ensure that the credit will be used for the consumer expected time. [ABSTRACT FROM AUTHOR]

This paper presents an embedded NILM engine to enable load disaggregation intelligence and explore its potential application as an energy management system. In this sense, the power meter is upgraded to a novel category called cognitive power meter. Therefore, this paper discloses a heterogeneous multiprocessing approach to attend NILM prerequisites and increase household interactivity. The proposed NILM performs the microscopic analysis using the Conservative Power Theory (CPT) for feature extraction; k-Nearest Neighbors (k-NN) for the appliance classification; and the Power Signature Blob (PSB) for energy disaggregation. Results show NILM can be performed on-site, embedded into modern cognitive power meters, and it may support households on providing valuable information concerning appliances' usage for energy management systems. [ABSTRACT FROM AUTHOR]

After more than 20 years of research, ADAS are common in modern vehicles available in the market. Automated Driving systems, still in research phase and limited in their capabilities, are starting early commercial tests in public roads. These systems rely on the information provided by on-board sensors, which allow to describe the state of the vehicle, its environment and other actors. Selection and arrangement of sensors represent a key factor in the design of the system. This survey reviews existing, novel and upcoming sensor technologies, applied to common perception tasks for ADAS and Automated Driving. They are put in context making a historical review of the most relevant demonstrations on Automated Driving, focused on their sensing setup. Finally, the article presents a snapshot of the future challenges for sensing technologies and perception, finishing with an overview of the commercial initiatives and manufacturers alliances that will show the intention of the market in sensors technologies for Automated Vehicles. [ABSTRACT FROM AUTHOR]