Real-Time Vehicle Roll Angle Estimation Based on Neural Networks in IoT Low-Cost Devices

The high rate of vehicle-crash victims has a fatal economic and social impact in today's societies. In particular, road crashes where heavy vehicles are involved cause more severe damage because they are prone to rollover. For this reason, many researches are focused on developing RSC Roll Stability Control (RSC) systems. Concerning the design of RSC systems with an adequate performance, it is mandatory to know the dynamics of the vehicle. The main problem arises from the lack of ability to directly capture several required dynamic vehicle variables, such as roll angle, from low-cost sensors. Previous studies demonstrate that low-cost sensors can provide data in real-time with the required precision and reliability. Even more, other research works indicate that neural networks are efficient mechanisms to estimate roll angle. Nevertheless, it is necessary to assess that the fusion of data coming from low-cost devices and estimations provided by neural networks can fulfill hard real-time processing constraints, achieving high level of accuracy during circulation of a vehicle in real situations. In order to address this issue, this study has two main goals: (1) Design and develop an IoT based architecture, integrating ANN in low cost kits with different hardware architectures in order to estimate under real-time constraints the vehicle roll angle. This architecture is able to work under high dynamic conditions, by following specific best practices and considerations during its design; (2) assess that the IoT architecture deployed in low-cost experimental kits achieve the hard real-time performance constraints estimating the roll angle with the required calculation accuracy. To fulfil these objectives, an experimental environment was set up, composed of a van with two set of low-cost kits, one including a Raspberry Pi 3 Model Band the other having an Intel Edison System on Chip linked to a SparkFun 9 Degrees of Freedom module.This research was funded by Spanish Government through the projects TRA2013-48030-C2-1-R and TRA2008-05373/AUT

Vehicle Dynamics, Lateral Forces, Roll Angle, Tire Wear and Road Profile States Estimation - A Review

Estimation of vehicle dynamics, tire wear, and road profile are indispensable prefaces in the development of automobile manufacturing due to the growing demands for vehicle safety, stability, and intelligent control, economic and environmental protection. Thus, vehicle state estimation approaches have captured the great interest of researchers because of the intricacy of vehicle dynamics and stability control systems. Over the last few decades, great enhancement has been accomplished in the theory and experiments for the development of these estimation states. This article provides a comprehensive review of recent advances in vehicle dynamics, tire wear, and road profile estimations. Most relevant and significant models have been reviewed in relation to the vehicle dynamics, roll angle, tire wear, and road profile states. Finally, some suggestions have been pointed out for enhancing the performance of the vehicle dynamics models

Evaluation of strategies for the development of efficient code for Raspberry Pi devices

La Internet de las cosas (IO) se enfrenta a desafíos que requieren soluciones ecológicas y paradigmas de eficiencia energética. Las arquitecturas (como el ARM) han evolucionado significativamente en los últimos años, con mejoras en la eficiencia de los procesadores, esenciales para los dispositivos de conexión permanente, como punto focal. Sin embargo, en lo que respecta al software, pocos enfoques analizan las ventajas de escribir un código eficiente al programar dispositivos de IO. Por consiguiente, esta propuesta tiene por objeto mejorar la optimización del código fuente para lograr mejores tiempos de ejecución. Además, se analiza la importancia de diversas técnicas para escribir código eficiente para los dispositivos Pi de Frambuesa, con el objetivo de aumentar la velocidad de ejecución. Se ha desarrollado un conjunto completo de pruebas exclusivamente para analizar y medir las mejoras logradas al aplicar cada una de estas técnicas. De esta manera se toma conciencia del importante impacto que pueden tener las técnicas recomendadas.The Internet of Things (IoT) is faced with challenges that require green solutions and energy-efficient paradigms. Architectures (such as ARM) have evolved significantly in recent years, with improvements to processor efficiency, essential for always-on devices, as a focal point. However, as far as software is concerned, few approaches analyse the advantages of writing efficient code when programming IoT devices. Therefore, this proposal aims to improve source code optimization to achieve better execution times. In addition, the importance of various techniques for writing efficient code for Raspberry Pi devices is analysed, with the objective of increasing execution speed. A complete set of tests have been developed exclusively for analysing and measuring the improvements achieved when applying each of these techniques. This will raise awareness of the significant impact the recommended techniques can have.• Ministerio de Economía y Competitividad y Fondos FEDER. Proyecto TIN2015-69957-R (I+D+i) • Unión Europea. Programa de Desarrollo Regional Europeo y Programa del Fondo Europeo de Desarrollo (FEDER): Programa Operativo Extremadura 2014-2020. Ref. 2018.14.02.332A.444.00peerReviewe