Crash Detection Using IMU Sensors

Cismas, Alexandru; Matei, Ioana Adriana; Ciobanu, Vlad; Casu, George

doi:10.1109/cscs.2017.103

Cited by 18 publications

(4 citation statements)

References 1 publication

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“…We have verified the proposed method through simulation experiments. To ensure that the experiments are as close to reality as possible, the system parameters considered in this paper are taken from the official datasheet of the sensor and previous research [32,[38][39][40]. However, there is still a certain gap between simulation and reality, mainly in the following aspects:…”

Section: Discussionmentioning

confidence: 99%

“…[2, 2, 2deg, 0.1, 0.1, 0.1, 0.1] T Explanations for certain simulation parameters in Table 4 are provided below. The gyroscope and accelerometer parameters are typical electrical parameters of commercial IMU inertial sensors (such as MPU6050) commonly available on the market [38]. The error of the barometric altimeter was referenced from [39], where an ultrahigh resolution pressure sensor based on percolative metal nanoparticle arrays was designed.…”

Section: Methodsmentioning

confidence: 99%

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A Novel Ranging and IMU-Based Method for Relative Positioning of Two-MAV Formation in GNSS-Denied Environments

Cheng

Ren

Deng

2023

Sensors

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Global Navigation Satellite Systems (GNSS) with weak anti-jamming capability are vulnerable to intentional or unintentional interference, resulting in difficulty providing continuous, reliable, and accurate positioning information in complex environments. Especially in GNSS-denied environments, relying solely on the onboard Inertial Measurement Unit (IMU) of the Micro Aerial Vehicles (MAVs) for positioning is not practical. In this paper, we propose a novel cooperative relative positioning method for MAVs in GNSS-denied scenarios. Specifically, the system model framework is first constructed, and then the Extended Kalman Filter (EKF) algorithm, which is introduced for its ability to handle nonlinear systems, is employed to fuse inter-vehicle ranging and onboard IMU information, achieving joint position estimation of the MAVs. The proposed method mainly addresses the problem of error accumulation in the IMU and exhibits high accuracy and robustness. Additionally, the method is capable of achieving relative positioning without requiring an accurate reference anchor. The system observability conditions are theoretically derived, which means the system positioning accuracy can be guaranteed when the system satisfies the observability conditions. The results further demonstrate the validity of the system observability conditions and investigate the impact of varying ranging errors on the positioning accuracy and stability. The proposed method achieves a positioning accuracy of approximately 0.55 m, which is about 3.89 times higher than that of an existing positioning method.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A Novel Ranging and IMU-Based Method for Relative Positioning of Two-MAV Formation in GNSS-Denied Environments

Cheng

Ren

Deng

2023

Sensors

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“…IMU sensors are generally used in unmanned aerial vehicles (UAVs) [28]. In [29], it is explained how to use a very cheap MPU6050 IMU sensor in an embedded device to measure Euler angles (roll, pitch, yaw) and also G forces on different axes. This solution is designed for motorcycle riders to detect accidents, taking into account very high measurement noise and drift.…”

Section: Acceleration Measurement Devicesmentioning

confidence: 99%

Embedded System for Learning Smooth and Energy-Efficient Tram Driving Techniques

Konieczka,

Stachowiak,

Feliński

et al. 2023

Energies

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Driving a tram in city traffic is a challenging task. It is especially difficult to drive smoothly (without unnecessary jerks) when the route runs through streets with many other vehicles, pedestrians, and traffic lights. A smooth driving style of the tram driver not only has a significant impact on the comfort of passengers being transported, but also affects the energy consumption of the tram. The paper focuses on the analysis of the tram driver’s way of driving and the resulting energy savings. The energy consumption of the tram was measured depending on the driver’s driving technique. For the analysis of the driving technique, an innovative electronic device was proposed to be installed on the tram. It detects jerks in the lateral and longitudinal directions. Based on vibration analysis, it evaluates the driver’s driving technique on an ongoing basis and displays the result of this assessment. The device is cheap and uses a popular minicomputer, a GPS system receiver, an IMU accelerometer, and a screen. It is independent of the electronic systems of the tram. Due to this, it is possible to increase passenger comfort and reduce electricity consumption. It can be useful when learning to drive a tram. Preliminary tests of this device were carried out on a real tram during rides with passengers in city traffic. Tests have confirmed its effectiveness.

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“…Aiming at the best cost benefit, some related works adopts the use of sensor fusion, using optimization algorithms. They show the fusion of sensors for position estimation, and orientation using accelerometers and gyroscopes [10]. By adopting algorithms such as the Kalman filter or complementary filter, it is possible to merge the data collected by the sensors and estimate more reliable data.…”

Section: Introductionmentioning

confidence: 99%

A Prototype for Monitoring Railway Vehicle Dynamics Using Inertial Measurement Units

Medeiros

Silva

Valente

et al. 2018

2018 13th IEEE International Conference on Industry Applications (INDUSCON)

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The dynamics of railway vehicles have changed from an essentially mechanical application into one that requires knowledge of sensors, electronics and computational processing. Industry 4.0 is a term that describes industrial activity by intelligent systems and solutions established on the concept of the Internet of Things (IoT). The present work illustrates the application of a conceptual cycle of an Internet of Things building a prototype, consisting on acquisition, processing and communication of data in the industrial context. Specifically, the monitoring of the dynamics of wagons using inertial sensors and algorithms that establish the fusion of sensors. The use of refined methods shows more reliable results in the use of low-cost sensors. Furthermore, the implementation of wireless communication enables the use of data in the creation of more complex analyses that can be applied in computational and instrumentation tools.

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Crash Detection Using IMU Sensors

Cited by 18 publications

References 1 publication

A Novel Ranging and IMU-Based Method for Relative Positioning of Two-MAV Formation in GNSS-Denied Environments

A Novel Ranging and IMU-Based Method for Relative Positioning of Two-MAV Formation in GNSS-Denied Environments

Embedded System for Learning Smooth and Energy-Efficient Tram Driving Techniques

A Prototype for Monitoring Railway Vehicle Dynamics Using Inertial Measurement Units

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