VEHIOT: Design and Evaluation of an IoT Architecture Based on Low-Cost Devices to Be Embedded in Production Vehicles

Redondo, Jonatan Pajares; Prieto-González, Lisardo; Guzmán, Javier García; Boada, Beatriz L.; Díaz, Vicente

doi:10.3390/s18020486

Cited by 17 publications

(33 citation statements)

References 31 publications

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“…[9], from Universidad Carlos III in Madrid, Spain, presents an architecture to carry out the estimation and control of vehicle dynamics, which integrates low-cost sensors and embedded hardware. A comparison in terms of accuracy, acquisition time and reliability vs. commercial products, VBOX device from Racelogic, has been made from tests carried out in a real vehicle.…”

Section: Review Of the Contributions In The Special Issuementioning

confidence: 99%

“…The proposed special issue has invited submissions related to sensing and connectivity systems for assisted and autonomous driving and for unmanned aerial, underwater and ground vehicles (namely UAV, UUV and UGV) [1,2,3,4,5,6,7,8,9,10,11,12]. Recent studies on the future of the automotive and robotic industry predict that the borders between the ICT (Information and Communication Technology) industry and the consumer industry will blur, and that vehicles will become consumer-centric [13].…”

Section: Introductionmentioning

confidence: 99%

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Sensing and Connection Systems for Assisted and Autonomous Driving and Unmanned Vehicles

Saponara

2018

Sensors

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The special issue, “Sensors, Wireless Connectivity and Systems for Autonomous Vehicles and Smart Mobility” on MDPI Sensors presents 12 accepted papers, with authors from North America, Asia, Europe and Australia, related to the emerging trends in sensing and navigation systems (i.e., sensors plus related signal processing and understanding techniques in multi-agent and cooperating scenarios) for autonomous vehicles, including also unmanned aerial and underwater ones.

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Section: Review Of the Contributions In The Special Issuementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sensing and Connection Systems for Assisted and Autonomous Driving and Unmanned Vehicles

Saponara

2018

Sensors

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“…Sensors drivers were developed in C++ due to the recommendations provided in [23], and trying to keep the code as much similar as possible to maximize objectivity in the comparison of performance results against other devices with different hardware architectures (as the Intel Edison).…”

Section: Software Perspectivementioning

confidence: 99%

“…In this test the calculated error is higher than the other two tests. This kind of tests are prone to suffer noise, and as is indicated in [23], the low-cost sensors used are very sensitive to noise. Table 7 shows that the error is higher in Intel Edison than in Raspberry Pi, in this case Intel Edison present some atypical data.…”

Section: Test 3 General Circulationmentioning

confidence: 99%

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Real-time Vehicle Roll Angle Estimation based on IoT low-cost devices and Neural Networks

Garcia-Guzman¹,

Prieto²,

Pajares³

et al. 2018

Preprint

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Abstract:Given the high number of vehicle-crash victims, it has been established as a priority to reduce this figure in the transportation sector. For this reason, many of the recent researches are focused on including control systems in existing vehicles, to improve their stability, comfort and handling. These systems need to know in every moment the behavior of the vehicle (state variables), among others, when the different maneuvers are performed, to actuate by means of the systems in the vehicle (brakes, steering, suspension) and, in this way, to achieve a good behavior. 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 lowcost 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 the reliability and appropriateness requirements for using these technologies to improve overall safety in production vehicles. Because of the increasing of computing power, the reduction of consumption and electric devices size, along with the high variety of communication technologies and networking protocols using Internet have yield to Internet of Things (IoT) development. In order to address this issue, this study has two main goals: 1) Determine the appropriateness and performance of neural networks embedded in low-cost sensors kits to estimate roll angle required to evaluate rollover risk situations. 2) Compare the low-cost control unit devices (Intel Edison and Raspberry Pi 3 Model B), to provide the roll angle estimation with this artificial neural network-based approach. To fulfil these objectives an experimental environment has been set up composed of a van with two set of low-cost kits, one including a Raspberry Pi 3 Model B, low cost Inertial Measurement Unit (BNO055 -37€) and GPS (Mtk3339 -53€) and the other having an Intel Edison System on Chip linked to a SparkFun 9 Degrees of Freedom module. This experimental environment will be tested in different maneuvers for comparison purposes. Neural networks embedded in low-cost sensor kits provide roll angle estimations very approximated to real values. Even more, Intel Edison and Raspberry Pi 3 Model B have enough computing capabilities to successfully run roll angle estimation based on neural networks to determine rollover risks situation fulfilling real-time operation restrictions stated for this problem.

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Computational Intelligence for Simulating a LiDAR Sensor

Castaño

Beruvides

Villalonga

et al. 2019

Sensor Systems Simulations

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In this chapter, an overview of some of the most commonly Computational Intelligence techniques used to provide new capabilities to sensor networks in Cyber-Physical and Internet-of-Things environments, and for verifying and evaluating the reliability issues of sensor networks is presented. Nowadays, on-chip Light Detection and Ranging (LiDAR) concept has driven a great technological challenge into sensor networks application for Cyber-Physical and Internet-of-Things systems. Therefore, the modelling and simulation of a LiDAR sensor networks is also included in this chapter that is structured as follows. First, a brief description of the theoretical modelling of the mathematical principle of operation is outlined. Subsequently, a review of the state-of-art of Computational Intelligence techniques in sensor system simulations is explained. Likewise, a use case of applying computational intelligence techniques to LiDAR sensor networks in a Cyber-Physical System environment is presented. In this use case, a model library with four specific Artificial Intelligence-based methods is also designed based on sensory information database provided by the LiDAR simulation. Some of them are multi-layer perceptron neural network, a self-organization map, a support vector machine and a k-nearest neighbours. The results demonstrate the suitability of using Computational Intelligence methods to increase the reliability of sensor networks when addressing the key challenges of safety and security in automotive applications.

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VEHIOT: Design and Evaluation of an IoT Architecture Based on Low-Cost Devices to Be Embedded in Production Vehicles

Cited by 17 publications

References 31 publications

Sensing and Connection Systems for Assisted and Autonomous Driving and Unmanned Vehicles

Sensing and Connection Systems for Assisted and Autonomous Driving and Unmanned Vehicles

Real-time Vehicle Roll Angle Estimation based on IoT low-cost devices and Neural Networks

Computational Intelligence for Simulating a LiDAR Sensor

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