A Sensor Fusion Method Based on an Integrated Neural Network and Kalman Filter for Vehicle Roll Angle Estimation

Vargas-Meléndez, Leandro; Boada, Beatriz L.; Boada, María Jesús López; Gauchía, A.; Díaz, Vicente

doi:10.3390/s16091400

Cited by 51 publications

(72 citation statements)

References 19 publications

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“…The proposed model employs a Back-Propagation (BP) algorithm, which is one of the most widely-used methods for training a neural network. The specific neural network considered has been implemented as described in [6]. The architecture of the BP neural network is shown in Figure 3.…”

Section: Vehicle Roll Angle Estimator Using Neural Networkmentioning

confidence: 99%

“…The complete architecture is packaged in a product that can be integrated in any vehicle, in this particular case, for testing purposes, a Mercedes-Benz van was used. The motivation to use this vehicle was to compare the results obtained during this research with those described in [6].…”

Section: Hardware Perspectivementioning

confidence: 99%

“…To accurately measure the roll angle, the two antennas form a 90-degree angle with respect to the traveling direction. For IoT kits' synchronization and data gathering, the VBOX controller is connected to a laptop embedded in the vehicle [6]. The installed sensors provide measurements for lateral acceleration, aym, longitudinal acceleration, ax, yaw rate, ψ̇, roll rate φ̇ and roll angle, φ.…”

Section: Hardware Perspectivementioning

confidence: 99%

“…The ANN has a single hidden layer of 15 neurons, four inputs (the lateral acceleration, aym, the longitudinal acceleration, ax, the yaw rate, ψ̇ and the roll rate φ̇) and one output (the vehicle roll angle, φ). The contribution provided by this research work to the Neural Network considered in [6] is its implementation in C++ using the FANN framework [45] to obtain an estimator component able to satisfy the real-time restrictions related to embed this estimator in a control unit installed in an operating vehicle.…”

Section: Vehicle Roll Angle Estimator Using Neural Networkmentioning

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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Section: Vehicle Roll Angle Estimator Using Neural Networkmentioning

confidence: 99%

Section: Hardware Perspectivementioning

confidence: 99%

Section: Hardware Perspectivementioning

confidence: 99%

Section: Vehicle Roll Angle Estimator Using Neural Networkmentioning

confidence: 99%

See 2 more Smart Citations

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

Garcia-Guzman¹,

Prieto²,

Pajares³

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…The vehicle roll angle can be directly measured by a dual antenna global positioning system (GPS), but it is expensive. For this reason, many researchers have focused on vehicle roll angle estimation [3,4,5,6,7]. One of the main techniques employed to estimate roll angle is through sensor fusion.…”

Section: Introductionmentioning

confidence: 99%

Sensor Fusion Based on an Integrated Neural Network and Probability Density Function (PDF) Dual Kalman Filter for On-Line Estimation of Vehicle Parameters and States

Vargas-Meléndez

Boada

Gauchía

et al. 2017

Sensors

Self Cite

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Vehicles with a high center of gravity (COG), such as light trucks and heavy vehicles, are prone to rollover. This kind of accident causes nearly 33% of all deaths from passenger vehicle crashes. Nowadays, these vehicles are incorporating roll stability control (RSC) systems to improve their safety. Most of the RSC systems require the vehicle roll angle as a known input variable to predict the lateral load transfer. The vehicle roll angle can be directly measured by a dual antenna global positioning system (GPS), but it is expensive. For this reason, it is important to estimate the vehicle roll angle from sensors installed onboard in current vehicles. On the other hand, the knowledge of the vehicle’s parameters values is essential to obtain an accurate vehicle response. Some of vehicle parameters cannot be easily obtained and they can vary over time. In this paper, an algorithm for the simultaneous on-line estimation of vehicle’s roll angle and parameters is proposed. This algorithm uses a probability density function (PDF)-based truncation method in combination with a dual Kalman filter (DKF), to guarantee that both vehicle’s states and parameters are within bounds that have a physical meaning, using the information obtained from sensors mounted on vehicles. Experimental results show the effectiveness of the proposed algorithm.

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A PID-Based Active Control of Camber Angles for Vehicle Ride Comfort Improvement

Ivanov

Marotta

Strano

et al. 2022

Mechanisms and Machine Science

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A Sensor Fusion Method Based on an Integrated Neural Network and Kalman Filter for Vehicle Roll Angle Estimation

Cited by 51 publications

References 19 publications

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

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

Sensor Fusion Based on an Integrated Neural Network and Probability Density Function (PDF) Dual Kalman Filter for On-Line Estimation of Vehicle Parameters and States

A PID-Based Active Control of Camber Angles for Vehicle Ride Comfort Improvement

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