Measured Vehicle Center-of-Gravity Locations - Including NHTSA's Data Through 2008 NCAP

Bixel, Ronald A.; Heydinger, Gary J.; Guenther, Dennis A.

doi:10.4271/2010-01-0086

Cited by 7 publications

(3 citation statements)

References 2 publications

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“…The low seating position also restricts the driver's own view. The observed COG heights for the velomobiles (vehicles 1-10) were in the range of 0.28 m to 0.47 m. According to the data in [30], the COG heights for passenger cars (sedans) typically range from about 0.5 m to 0.58 m. Velomobiles, therefore, showed significantly lower center of gravity heights. The COG heights of the three trikes (vehicles 11-13) were in a comparable range to those of passenger cars.…”

Section: Center Of Gravity Height and Rollover Stabilitymentioning

confidence: 94%

Mass Data Measurement, Approximation and Influence on Vehicle Stability for Ultra-Light Human-Powered Vehicles

2021

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The mass properties of a vehicle play a decisive role in its dynamics and characteristics and are fundamental for vehicle dynamics models and controllers. These values are not yet known for the vehicle class of the ultra-light velomobiles and similar multi-track bicycle vehicles. In the future, however, such vehicles could play a role in reducing the CO2 emissions generated by individual transportation. As a basis for vehicle dynamics modeling, accident reconstruction, and controller development for this vehicle class, this paper investigated ranges of mass properties and their influence on vehicle stability considering driver influence. In total, 13 vehicles (10 velomobiles and 3 trikes) were examined using different experimental setups. It was shown that most vehicles exhibited understeering behavior based on the center of gravity position and calculations of the static stability factor showed significantly lower rollover stability compared with conventional vehicles. The measured moments of inertia were used to develop and examine different approximation approaches for the yaw moment of inertia using conventional approaches from the passenger car sector and stepwise regression. This created the basis for parameter estimation from easily measurable vehicle parameters and provided the possibility to generate realistic parameter sets for vehicle dynamic models. Existing tests do not consider the influence of driver movements, such as pedaling movements or possible inclination of the upper body. This offers the potential for further investigations of the dynamic influences on the investigated variables.

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Section: Center Of Gravity Height and Rollover Stabilitymentioning

confidence: 94%

Mass Data Measurement, Approximation and Influence on Vehicle Stability for Ultra-Light Human-Powered Vehicles

2021

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“…COGs for compact cars are mainly between 500 and 560 mm above ground. For SUVs they go up to 700 mm [1,9]. The height of the longitudinal beams depends on the particular design.…”

Section: Reasons For Pitching and Vertical Translationmentioning

confidence: 99%

Influence of pitching and yawing during frontal passenger vehicle crash tests on driver occupant's kinematics and injury

Woitsch

Sinz

2013

International Journal of Crashworthiness

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Vehicle safety has become a major task in the development process of passenger vehicles. Measures for occupant safety are developed by reference to different tests including legal tests and consumer tests. The different load cases for the considered frontal crash tests cause different vehicle kinematics involving all six degrees of freedom. Besides restraint systems, vehicle kinematics shows distinct influence on occupant loading. A sensitivity analysis of the dummy responses due to varied motion fractions has been performed by finite elements simulations of a well-validated simulation model. The influences of particular motion components are determined by objective assessment variables. These are well-known injury criteria as well as geometrical criteria such as distances between dummy and interior. The results of this sensitivity study can be used for additional measures in terms of occupant protection.

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“…The National Highway Traffic Safety Administration (NHTSA) is a valuable resource in this regard. We utilized the database of 528 passenger vehicles presented in the technical report from Bixel, Heydinger [40]. Wheelbase and gross axle weights of different models from 36 car manufacturers between the years 2000-2008 are considered as shown in Figure 5.…”

Section: Vehicle Passage Datasetmentioning

confidence: 99%

Trident: A Deep Learning Framework for High-Resolution Bridge Vibration Monitoring

Sajedi

Liang

2022

Applied Sciences

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Bridges are the essential components in lifeline transportation systems, and their safe operation is of great importance. Information on structural damage could assist in timely repairs and reduce downtime. With the latest advancements in sensing technology, collecting vibration data from bridges has become more accessible. However, effective vibration processing is still a challenge, given the high dimensionality and massive size of vibration data. Existing studies have shown that machine/deep learning techniques can be valuable tools for this task. However, the learning and computational capacities of these models are challenged in the presence of large sensor arrays. We propose Trident as a novel deep learning framework that enables automatic damage feature extraction by simultaneously learning from temporal and three-dimensional (3D) spatial variations of 6D input data in instrumented bridges. Trident is equipped with 3 ConvLSTM3D branches to achieve this goal. A 3D steel truss bridge subject to dynamic traffic loads is monitored for its vibrations to evaluate Trident’s robustness in finding damaged elements. A damage dataset of 52,800 vehicle passing simulations is generated leveraging a database of 528 passenger vehicles in the United States, obtained from the National Highway and Traffic Safety Administration. Bayesian optimization is utilized to tune the model’s hyperparameters, achieving a test Node Average Geometric Mean Accuracy of 86%. This level of performance is promising given the high dimensionality and complexities of the output space in vibration-based monitoring. Trident’s concept can be extended to other vibration monitoring tasks with different time series data and damage labeling strategies.

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Measured Vehicle Center-of-Gravity Locations - Including NHTSA's Data Through 2008 NCAP

Cited by 7 publications

References 2 publications

Mass Data Measurement, Approximation and Influence on Vehicle Stability for Ultra-Light Human-Powered Vehicles

Mass Data Measurement, Approximation and Influence on Vehicle Stability for Ultra-Light Human-Powered Vehicles

Influence of pitching and yawing during frontal passenger vehicle crash tests on driver occupant's kinematics and injury

Trident: A Deep Learning Framework for High-Resolution Bridge Vibration Monitoring

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