A Multi-Platform Framework for Artificial Intelligence Engines in Automotive Systems

Marina, Liviu; Trasnea, Bogdan; Grigorescu, Sorin Mihai

doi:10.1109/icstcc.2018.8540753

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…There is a wide variety of emerging technologies that allow the hybridization of the physical and digital world, which gives rise to the concept of a cyber-physical system as an object with a double existence (physical and real) [60] and another digital twin (digital and virtual) [61]. From among these, it is worth highlighting the sensors in products and processes [62], embedded systems [63], robotics and automation [63], additive manufacturing [64], artificial vision and intelligence [65], augmented and virtual reality [66], simulation and virtualization of production [65,67], connectivity of the company with the consumer [68], social networks [69,70], electronic commerce [71], cyber-security [72], advanced cloud computing [73], and digital twin and collaborative platforms [74]. Following an overview, we will proceed to establish a review and a study of the main technologies.…”

Section: Emerging Technologies In Industry 40mentioning

confidence: 99%

Standardization Framework for Sustainability from Circular Economy 4.0

et al. 2019

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The circular economy (CE) is widely known as a way to implement and achieve sustainability, mainly due to its contribution towards the separation of biological and technical nutrients under cyclic industrial metabolism. The incorporation of the principles of the CE in the links of the value chain of the various sectors of the economy strives to ensure circularity, safety, and efficiency. The framework proposed is aligned with the goals of the 2030 Agenda for Sustainable Development regarding the orientation towards the mitigation and regeneration of the metabolic rift by considering a double perspective. Firstly, it strives to conceptualize the CE as a paradigm of sustainability. Its principles are established, and its techniques and tools are organized into two frameworks oriented towards causes (cradle to cradle) and effects (life cycle assessment), and these are structured under the three pillars of sustainability, for their projection within the proposed framework. Secondly, a framework is established to facilitate the implementation of the CE with the use of standards, which constitute the requirements, tools, and indicators to control each life cycle phase, and of key enabling technologies (KETs) that add circular value 4.0 to the socio-ecological transition.

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Section: Emerging Technologies In Industry 40mentioning

confidence: 99%

Standardization Framework for Sustainability from Circular Economy 4.0

et al. 2019

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“…There are several model architectures and transfer learning techniques that can be applied to solve different problems in the automotive domain. Nevertheless, more research is needed: to design distributed deep learning systems to improve training times for more complex networks and massive data sets; to determine how to apply deep learning in other areas of automobile control such as lateral stability [ 24 ]; and it is necessary to assess that the fusion of data coming from low-cost devices and estimations provided by deep machine learning algorithms can fulfill the reliability and appropriateness requirements for using these technologies to improve overall vehicular safety. …”

Section: Introductionmentioning

confidence: 99%

“…to determine how to apply deep learning in other areas of automobile control such as lateral stability [ 24 ]; and…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Estimation of Vehicle Roll and Sideslip Angles through a Deep Learning Approach

Prieto-González

Sánchez

García-Guzmán

et al. 2020

Sensors

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Presently, autonomous vehicles are on the rise and are expected to be on the roads in the coming years. In this sense, it becomes necessary to have adequate knowledge about its states to design controllers capable of providing adequate performance in all driving scenarios. Sideslip and roll angles are critical parameters in vehicular lateral stability. The later has a high impact on vehicles with an elevated center of gravity, such as trucks, buses, and industrial vehicles, among others, as they are prone to rollover. Due to the high cost of the current sensors used to measure these angles directly, much of the research is focused on estimating them. One of the drawbacks is that vehicles are strong non-linear systems that require specific methods able to tackle this feature. The evolution in Artificial Intelligence models, such as the complex Artificial Neural Network architectures that compose the Deep Learning paradigm, has shown to provide excellent performance for complex and non-linear control problems. In this paper, the authors propose an inexpensive but powerful model based on Deep Learning to estimate the roll and sideslip angles simultaneously in mass production vehicles. The model uses input signals which can be obtained directly from onboard vehicle sensors such as the longitudinal and lateral accelerations, steering angle and roll and yaw rates. The model was trained using hundreds of thousands of data provided by Trucksim® and validated using data captured from real driving maneuvers using a calibrated ground truth device such as VBOX3i dual-antenna GPS from Racelogic®. The use of both Trucksim® software and the VBOX measuring equipment is recognized and widely used in the automotive sector, providing robust data for the research shown in this article.

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Artificial intelligence: a survey on evolution, models, applications and future trends

2019

Journal of Management Analytics

369

123

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A Multi-Platform Framework for Artificial Intelligence Engines in Automotive Systems

Cited by 3 publications

References 12 publications

Standardization Framework for Sustainability from Circular Economy 4.0

Standardization Framework for Sustainability from Circular Economy 4.0

Simultaneous Estimation of Vehicle Roll and Sideslip Angles through a Deep Learning Approach

Artificial intelligence: a survey on evolution, models, applications and future trends

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