Modern road vehicles are employing features of driver assistance systems (DAS) to improve drivability performance, comfort, and safety. In the future perspective, the advances in this field will lead these systems to the level of autonomous and cooperative driving, based on sensors networks and sensor fusion. This paper aims to present the readers a novel strategy for lane detection and tracking, which fits as a functional requirement to deploy DAS features like Lane Departure Warning and Lane Keeping Assist. To achieve the presented results, the digital image processing was divided into three levels. At the low-level, the input image dimensionality is reduced from three to one layer, the sharpness is improved, and region of interest is defined based on the minimum safe distance from the vehicle ahead. The feature extractor for lane edges detection design is part of the mid-level processing. The lane tracking strategy development is discussed in the high-level stage; Hough Transform and a shape-preserving spline interpolation are used to achieve a smooth lane fitting. The experimental outcomes were qualitatively and quantitatively evaluated using a ground truth comparison. The strategy shows good accuracy levels, including scenarios with shadows, curves, and road slope.

The recent development of deep neural networks attempts to go deeper through the layered architecture to solve complex problems. As expected, the deepening impacts the processing times for inferences in tasks like object classification and localization. This demands solutions for balancing computational resource requirements and extend the versatility of a given model to suit a variety of applications. Often the solutions rely on architectural modifications to ease training times while achieving more accurate results. In this work, we propose a multistage algorithm with an initial stage adopting the YOLOv3 network for object detection and HART approach for object tracking. This is followed by an adaptive post-localization stage shift system taking into consideration the processing times of stage inferences, which are the number of located objects in image sequences. The goal is to change the localization strategy to achieve optimal processing time performance. For evaluation, we present results in terms of precision and processing times in varying traffic conditions. The results demonstrate the effectiveness of the proposed adaptive multistage model in comparison to other real-time state-of-theart detection strategies, as it achieves a frame rate gain of 49% over YOLOv3 while maintaining competitive task accuracy.

Low Power Wide Area Networks (LPWAN) enable a growing number of Internet-of-Things (IoT) applications with large geographical coverage, low bit-rate, and long lifetime requirements. LoRa (Long Range) is a well-known LPWAN technology that uses a proprietary Chirp Spread Spectrum (CSS) physical layer, while the upper layers are defined by an open standard—LoRaWAN. In this paper, we propose a simple yet effective method to improve the Quality-of-Service (QoS) of LoRaWAN networks by fine-tuning specific radio parameters. Through a Mixed Integer Linear Programming (MILP) problem formulation, we find optimal settings for the Spreading Factor (SF) and Carrier Frequency (CF) radio parameters, considering the network traffic specifications as a whole, to improve the Data Extraction Rate (DER) and to reduce the packet collision rate and the energy consumption in LoRa networks. The effectiveness of the optimization procedure is demonstrated by simulations, using LoRaSim for different network scales. In relation to the traditional LoRa radio parameter assignment policies, our solution leads to an average increase of 6% in DER, and a number of collisions 13 times smaller. In comparison to networks with dynamic radio parameter assignment policies, there is an increase of 5%, 2.8%, and 2% of DER, and a number of collisions 11, 7.8 and 2.5 times smaller than equal-distribution, Tiurlikova’s (SOTA), and random distribution, respectively. Regarding the network energy consumption metric, the proposed optimization obtained an average consumption similar to Tiurlikova’s, and 2.8 times lower than the equal-distribution and random dynamic allocation policies. Furthermore, we approach the practical aspects of how to implement and integrate the optimization mechanism proposed in LoRa, guaranteeing backward compatibility with the standard protocol.

Controller Area Network (CAN) is a widely-used bus protocol in automotive distributed embedded systems, but its limited communication bandwidth (up to 1 Mbps) and payload size (up to 8 Bytes) limit its applicability in today’s increasingly complex automotive Electrical/Electronic systems. Controller Area Network with Flexible Data Rate (CAN-FD) is an improved CAN-based communication protocol, with higher communication bandwidth (up to 8 Mbps for the payload) and increased payload size (up to 64 Bytes). In this paper, we perform analytical and experimental performance comparisons of CAN-FD bus with conventional CAN bus. We consider a message set obtained by reverse-engineering a real CAN -based system, with additional high-priority interference messages for stress-testing the system with different bus loads. We also consider a networked control system based on the message set, and analyze the control system performance measured by Step Responses under different bus loads. Experimental results validate the performance advantages of CAN-FD over conventional CAN bus.

The Industry 4.0 concept inception definitively provided a disruptive glimpse in terms of possibilities in a multidisciplinary spectrum. The convergence and synergy from technologies generated quite high expectations. In terms of advancements, the efficiency increase is one of the key factors. Smart factories must be capable of producing more and in an ecologically cleaner manner, by benefiting from less environmentally impacting energy sources. The present moment is for digital transformation in the diverse enabling transverse areas. For that to be feasible, the next steps are towards standardization, which brings viability to expand and integrate the assorted devices and processes, bringing intelligence closer to all the involved equipment by capitalization. The gap that this research aims to explore is in the assessment of the ongoing industries, focusing on the current status of the operational plants technologically. In other words, by mapping the present, as-is, situation, the stakeholders obtain a valuable guidance resource to go through the journey upon permeating the Industry 4.0 status, the greater objective. Undoubtedly, a path of considerable length, but with well-proportioned outcomes. The solution’s ambition is to rate, grade, and calculate the readiness and maturity levels of the institution accordingly to that scope. To achieve that, methods will be developed and applied to gather information and subsequently threaten it. Finally, the results will be confronted versus the fourth industrial revolution preconized capabilities, as defined by the Rami 4.0 Reference Architecture Model. Having presented the information, there is a possibility to establish a road-map to achieve the desired level.