Abstract

Abstract
Perception algorithms are essential for autonomous or semi-autonomous vehicles to perceive the semantics of their surroundings, including object detection, panoptic segmentation, and tracking. Decision-making in case of safety-critical situations, like autonomous emergency braking and collision avoidance, relies on the outputs of these algorithms. This makes it essential to correctly assess such perception systems before their deployment and to monitor their performance when in use. It is difficult to test and validate these systems, particularly at runtime, due to the high-level and complex representations of their outputs. This paper presents an overview of different existing metrics used for the evaluation of LiDAR-based perception systems, emphasizing particularly object detection and tracking algorithms due to their importance in the final perception outcome. Along with generally used metrics, we also discuss the impact of Planning KL-Divergence (PKL), Timed Quality Temporal Logic (TQTL), and Spatio-temporal Quality Logic (STQL) metrics on object detection algorithms. In the case of panoptic segmentation, Panoptic Quality (PQ) and Parsing Covering (PC) metrics are analysed resorting to some pretrained models. Finally, it addresses the application of diverse metrics to evaluate different pretrained models with the respective perception algorithms on publicly available datasets. Besides the identification of the various metrics being proposed, their performance and influence on models are also assessed after conducting new tests or reproducing the experimental results of the reference under consideration.

Abstract
The increased demand for and use of autonomous driving and advanced driver assistance systems has highlighted the issue of abnormalities occurring within the perception layers, some of which may result in accidents. Recent publications have noted the lack of standardized independent testing formats and insufficient methods with which to analyze, verify, and qualify LiDAR (Light Detection and Ranging)-acquired data and their subsequent labeling. While camera-based approaches benefit from a significant amount of long-term research, images captured through the visible spectrum can be unreliable in situations with impaired visibility, such as dim lighting, fog, and heavy rain. A redoubled focus upon LiDAR usage would combat these shortcomings; however, research involving the detection of anomalies and the validation of gathered data is few and far between when compared to its counterparts. This paper aims to contribute to expand the knowledge on how to evaluate LiDAR data by introducing a novel method with the ability to detect these patterns and complement other performance evaluators while using a statistical approach. Although it is preliminary, the proposed methodology shows promising results in the evaluation of an algorithm's confidence score, the impact that weather and road conditions may have on data, and fringe cases in which the data may be insufficient or otherwise unusable.

Abstract

Abstract
The use of power Darlington transistors for an inverter of a 40-kVA uninterruptible power supply (UPS) system is discussed, and the storage time, dead time, and switching aid network contributions to the total harmonic content of the output voltage are examined. A review of common switching aid circuits, for nominal and short-circuit conditions, is presented, and their limitations are discussed. A solution, conceived specifically for power Darlington transistors, is presented. The solution that fits the most relevant characteristics of this technology is analyzed regarding commutation under nominal conditions, commutation under short-circuit conditions, and contribution to harmonic distortion in the output voltage. Conclusions are drawn about the optimal switching frequency for this type of application. The discussion and conclusions are supported by experimental and simulation results.

Abstract
Different built-in self testing schemes for RF circuits have been developed resorting to peak voltage detectors. These are simple to implement but provide a conditional RF power measurement accuracy as impedance is assumed to be known. A true power detector is presented which allows obtaining more accurate measurements, namely as far as output load variations are concerned. The theoretical fundaments underlining the power detector operating principle are presented and simulation and experimental results obtained with a prototype chip are described which confirm the benefits of measuring true power, comparing to output peak voltage, when observing output load matching deviations and complex waveforms.