Design of Low-Cost Vehicle Roll Angle Estimator Based on Kalman Filters and an IoT Architecture

An improper decision for the design, selection and adjustment of the components needed to control a vehicle could generate negative effects and discomfort to the driver, where pedals play a very important role. The aim of the study is to provide a first approach to develop an embedded monitoring device in order to evaluate the posture of the driver, the influence of the clutch pedal and to advise about the possible risk. With that purpose in mind, a testbed was designed and two different sets of tests were carried out. The first test collected information about the volunteers who were part of the experiment, like the applied force on the clutch pedal or the body measurements. The second test was carried out to provide new insight into this matter. One of the more significant findings to emerge from this study is that the force applied on the clutch pedal provides enough information to determine correct driver posture. For this reason, a system composed of a pedal force sensor and an acquisition/processing system can fulfil the requirements to create a healthcare system focused on driver posture.

Section: Discussionmentioning

confidence: 99%

Clutch Pedal Sensorization and Evaluation of the Main Parameters Related to Driver Posture

Olmeda

Toro

Garrosa

et al. 2018

“…Presently, there exist devices that allow the direct measurement of both angles, such as a dual antenna GPS (Global Positioning System) or a Kistler S-Motion device [3]. This type of advanced GPS equipment cannot be included in mass production vehicles due to high-cost issues [4]. Because of that, many of the researchers focus on estimating these angles based on available measurements from on-board embedded sensors but independently [5,6].…”

Section: Introductionmentioning

confidence: 99%

“…The former group includes approaches such as Kalman-filter-based method [9][10][11], nonlinear-observer-based method [11][12][13], and robust observers [14][15][16]. The latter includes Neural Networks, and ANFIS, among others [4,17,18], whose main advantage is that they do not depend on the reference vehicle models. Additionally, vehicles are strong non-linear systems that require specific methods able to tackle this feature.…”

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

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.

“…Vargas-Meléndez et al presented a novel estimator based on sensor fusion, which combined the neural network with a Kalman filter in order to estimate the vehicle roll angle [19]. Garcia et al used MEMS sensors combined with data fusion algorithms to estimate sideslip or inclination angles [20,21]. Based on the multi-sensor system integrating a gyroscope, accelerometer, and magnetometer, Zhang et al designed a novel dual-linear Kalman filter [22].…”

Section: Introductionmentioning

confidence: 99%

An Acquisition Method of Agricultural Equipment Roll Angle Based on Multi-Source Information Fusion

et al. 2020

Accurately obtaining roll angles is one of the key technologies to improve the positioning accuracy and operation quality of agricultural equipment. Given the demand for the acquisition of agricultural equipment roll angles, a roll angle monitoring model based on Kalman filtering and multi-source information fusion was established by using the MTi-300 AHRS inertial sensor (INS) and XW-GI 5630 BeiDou Navigation Satellite System (BDS), which were installed on agricultural equipment. Data of the INS and BDS were fused by MATLAB; then, Kalman filter was used to optimize the data, and the state equation and measurement equation of the integrated system were established. Then, an integrated monitoring terminal man-machine interactive interface was designed on MATLAB GUI, and a roll angle monitoring system based on the INS and BDS was designed and applied into field experiments. The mean absolute error of the integrated monitoring system based on multi-source information fusion during field experiments was 0.72 • , which was smaller compared with the mean absolute errors of roll angle monitored by the INS and BDS independently (0.78 • and 0.75 • , respectively). Thus, the roll angle integrated model improves monitoring precision and underlies future research on navigation and independent operation of agricultural equipment.