This paper proposes the use of the Statistical Process Control (SPC), more specifically, the Exponentially Weighted Moving Average method, for the monitoring of drivers using approaches based on the vehicle and the driver’s behavior. Based on the SPC, we propose a method for the lane departure detection; a method for detecting sudden driver movements; and a method combined with computer vision to detect driver fatigue. All methods consider information from sensors scattered by the vehicle. The results showed the efficiency of the methods in the identification and detection of unwanted driver actions, such as sudden movements, lane departure, and driver fatigue. Lane departure detection obtained results of up to 76.92% (without constant speed) and 84.16% (speed maintained at ≈60). Furthermore, sudden movements detection obtained results of up to 91.66% (steering wheel) and 94.44% (brake). The driver fatigue has been detected in up to 94.46% situations.
The relationship between Design and Engineering has not its genesis in the 20th century. But it was in this century that the advantage of this association was perceived and came into existence. From the beginning of mass production in the early twentieth century came the need for bringing the two disciplines together: Design and Engineering.
Although Design and Engineering are two separate and independent disciplines, much of their applied field overlaps. The automotive product design and industry is a common ground where Design and Engineering strive to achieve, together, a common final product: the automobile.
This article aims to discuss how the development of the automobile throughout the 20th and 21st centuries was driven by Design and Engineering, opening the way for a deeper debate on the definition of this interaction.
A definition for the Product Development methodology from the Engineering and Design point of view is described, as well as the role of the Design and Engineering disciplines within the automotive product development. Methodologically, a historically relevant case study in the development of the automobile product is presented.
SMEs are still able to rise to the top tier of the supply chain, provided they develop specific capabilities that allow them to strategically position themselves to overtake the barriers built by mega suppliers.Design plays an increasingly important role in the automotive industry. Among small suppliers, it can enable them to rise to the top tier of the supply chain.
Recent advancements in technology have enabled the emergence of Industry 4.0, this term is used to describe the integration of various advanced technologies such as artificial intelligence and robotics in industrial settings. The presence of defective products during production incurs additional costs, and traditional manual methods of equipment inspection prove to be inefficient in such a dynamic environment. In this study, we introduce a robot designed specifically for this scenario, capable of performing tasks that require autonomous movement to specific areas of an industrial plant. To achieve this, we employ the concept of Edge AI, applying artificial intelligence on a localized edge computing device. The robot utilizes computer vision through the state-of-the-art YOLOv7 CNN and incorporates feedback control to facilitate its locomotion. The hardware components of this robot include a Jetson Xavier NX, Raspberry Pi 4, a camera, and a LIDAR. Additionally, we conducted a comprehensive performance analysis of the object detection method, measuring metrics such as frames per second (FPS), CPU and GPU consumption, and RAM usage.
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