Search Results

One of the primary issues in the interpretation of vehicle impact response data, observed from vehicle crash test events, is coping with variability. This vehicle response inconsistency generally causes test results to be unpredictable and makes CAE test validation work difficult as well. This paper, considering the uncertain characteristics of vehicle impact events, has implemented a stochastic assessment of vehicle NCAP response variation through a CAE vehicle impact model, and it has accomplished the three primary study objectives as stated follows: 1) Identify the response variation causing factors stochastically from various structural and environmental factor candidates and quantify the degree of their influences on crash response, 2) Develop a methodology for interpreting the significance of the factor effects in conjunction with vehicle impact mechanics and physics, and 3) Implement a stochastic improvement of the vehicle NCAP responses and their repeatability

Lane line detection is a very critical element for Advanced Driver Assistance Systems (ADAS). Although, there has been significant amount of research dedicated to the detection and localization of lane lines in the past decade, there is still a gap in the robustness of the implemented systems. A major challenge to the existing lane line detection algorithms stems from coping with bad weather conditions (e.g. rain, snow, fog, haze, etc.). Snow offers an especially challenging environment, where lane marks and road boundaries are completely covered by snow. In these scenarios, on-board sensors such as cameras, LiDAR, and radars are of very limited benefit. In this research, the focus is on solving the problem of improving robustness of lane line detection in adverse weather conditions, especially snow. A framework is proposed that relies on using Vehicle-to-Infrastructure (V2I) communication to access reference images stored in the cloud.

In stereo photogrammetry, the accuracy of calculating the location of a point in space, decreases as the angle between the two cameras decreases. For vehicle crash testing, the need for accurate 3D data conflicts with the need for flexible positioning of the cameras, to enable unobstructed views of the targets inside the vehicle throughout the impact event. This paper discusses a method for increasing the quantity and quality of film analysis data when small subtended angles are used. The method uses the 3D information developed through triangulation of two cameras as input to a single camera analysis.

The Frequency Response Function (FRF) is a fundamental component to identifying the dynamic characteristics of a system. FRF's have a significant impact on modal analysis and root cause analysis of NVH issues. In most cases the FRF can be easily measured, but there are instances when the measurement is unobtainable due to spatial constraints. This paper outlines a simple experimental method for obtaining a high quality input-output FRF of a structure in areas where an impact hammer can not reach during impact testing. Traditionally, the FRF in such an area is obtained by using a load cell extender with a hammer impact excitation. A common problem with this device is a double hit, that yields unacceptable results.

In the dense fabric of urban areas, electric scooters have rapidly become a preferred mode of transportation. As they cater to modern mobility demands, they present significant safety challenges, especially when interacting with pedestrians. In general, e-scooters are suggested to be ridden in bike lanes/sidewalks or share the road with cars at the maximum speed of about 15-20 mph, which is more flexible and much faster than pedestrians and bicyclists. Accurate prediction of pedestrian movement, coupled with assistant motion control of scooters, is essential in minimizing collision risks and seamlessly integrating scooters in areas dense with pedestrians. Addressing these safety concerns, our research introduces a novel e-Scooter collision avoidance system (eCAS) with a method for predicting pedestrian trajectories, employing an advanced Long short-term memory (LSTM) network integrated with a state refinement module.

The Hybrid III family of dummies is used to estimate the response of an occupant during a crash. One recent area of interest is the response of the neck during air bag loading. The biomechanical response of the Hybrid III dummy's neck was based on inertial loading during crash events, when the dummy is restrained by a seat belt and/or seat back. Contact loading resulting from an air bag was not considered when the Hybrid III dummy was designed. This paper considers the effect of air bag loading on the 5th percentile female Hybrid III dummies. The response of the neck is presented in comparison to currently accepted biomechanical corridors. The Hybrid III dummy neck was designed with primary emphasis on appropriate flexion and extension responses using the corridors proposed by Mertz and Patrick. They formulated the mechanical performance requirements of the neck as the relationship between the moment at the occipital condyles and the rotation of the head relative to the torso.

Vehicle thermal protection is an important aspect of the overall vehicle development process. It involves optimizing the exhaust system routing and designing heat shields to protect various components that are in near proximity to the exhaust system. Reduced time to market necessitates an efficient process for thermal protection development. A robust procedure that utilizes state of the art CFD simulation techniques proactively during the design phase is described. Simulation allows for early detection of thermal issues and development of countermeasures several months before prototype vehicles are built. Physical testing is only used to verify the thermal protection package rather than to develop heat shields. The new procedure reduces the number of physical tests and results in a robust, efficient methodology.

This paper deals with the multi-parameter and boundary mannequin techniques in creating human models in automotive applications. The concepts and applications of single-parameter, multiple parameter and boundary mannequin method are discussed respectively to clarify certain confusion. Emphasis is put on how to create boundary mannequins for a specific application, which may have been puzzling many engineers in practical applications. The authors would like to share their experience in using the digital human modeling software and make discussions on some common issues. A number of case studies from typical automotive manufacturing assembly operations are also presented to demonstrate the usage of the multi-parameter and boundary mannequin techniques.

This paper contains a review of methods for deriving risk curves from biomechanical data obtained from impact experiments on human surrogates. It covers many of the problems and pitfalls of obtaining realistic human risk curves from impact experiments. The strength and weakness of both parametric and non-parametric methods are evaluated. The limitations of standard analysis of censored impact test data are presented. Methods are given for determining risk curves from both doubly censored data and data obtained from impacts to body regions in which there are more than one mechanism of injury. A detailed set of examples is presented in which different experimental data are analyzed using the Consistent Threshold method and the logistic approach. Finally risk curves for published data are presented for the femur, head, thorax, and neck.

Developing an effective side impact ATD for assessing vehicle impact responses requires a method for evaluating that ATD's bio-fidelity. ISO/TR9790 has been in existence for some years to serve that purpose. Recently, NHTSA sponsored a research project on the post-mortem human subjects (PMHS) responses subjected to side impact conditions. Based on those newly available PMHS data, Maltese generated a new approach for creating bio-fidelity corridors for human surrogates. The approach incorporates the time factor into the evaluation equation and automates the process (Maltese et al. 2002). This paper serves as the first attempt to look closely at the new bio-fidelity corridor generation process (hereafter referred as the Maltese approach) with respect to its validity, effectiveness, as well as its practicality. The effect of mass scaling was first examined in order to ensure the integrity of the data. The time alignment scheme and the formation of the corridors were then tested.

Digital shearography has many advantages, such as full-field, non-contact, high sensitivity, and good robustness. It was widely used to measure the deformation and strain of materials, also to the application of nondestructive testing (NDT). However, most digital sherography applications can only work in one field of view per measurement, and some small defects may not be detected as a result. Multiple measurements of different fields of view are needed to solve this issue, which will increase the measurement time and cost. The difficulty in performing multiple measurements may also increase for cases where the loading is not repeatable. Therefore, a system capable of measuring dual fields of view at the same time is necessary. The carrier frequency spatial phase shift method may be a good candidate to reach this goal because it can simultaneously record phase information of multiple images, e.g. two speckle interferograms with different fields of view.

An existing challenge in current state-of-the-art autonomous vehicles is the process of safely transferring control from autonomous driving mode to manual mode because the driver may be distracted with secondary tasks. Such distractions may impair a driver’s situational awareness of the driving environment which will lead to fatal outcomes during a handover. Current state-of-the-art vehicles notify a user of an imminent handover via auditory, visual, and physical alerts but are unable to improve a driver’s situational awareness before a handover is executed. The overall goal of our research team is to address the challenge of providing a driver with relevant information to regain situational awareness of the driving task. In this paper, we introduce a novel approach to estimating a driver’s visual focus of attention using a 2D RGB camera as input to a Multi-Input Convolutional Neural Network with shared weights. The system was validated in a realistic driving scenario.

For the purposes of analyzing and understanding the general effects of a set of different vehicle attributes on overall crash outcome a fleet model is used. It represents the impact response, in a one-dimensional sense, of two vehicle frontal crashes, across the frontal crash velocity spectrum. The parameters studied are vehicle mass, stiffness, intrusion, pulse shape and seatbelt usage. The vehicle impact response parameters are obtained from the NCAP tests. The fatality risk characterization, as a function of the seatbelt use and vehicle velocity, is obtained from the NASS database. The fatality risk is further mapped into average acceleration to allow for evaluation of the different vehicle impact response parameters. The results indicate that the effects of all the parameters are interconnected and none of them is independent. For example, the effect of vehicle mass on fatality risk depends on seatbelt use, vehicle stiffness, available crush, intrusion and pulse shape.

One way to improve vehicle's fuel economy is to reduce its weight. Reducing weight, however has other consequences. One of these is reduced vehicle size. Almost invariably, lighter vehicles are smaller. Reducing vehicle weight has also been associated with a reduction in occupant protection; the lighter the vehicle, the greater the chance of injury when a crash occurs. For this study, a data-based model is used to evaluate the independent effects of size and weight. This model is constructed using the NASS database and information obtained from NCAP tests. The results indicate that although mass is the dominant factor, size also has an effect; some of the observed reduction in safety benefits associated with mass reduction is actually an effect of size reduction. The model is also used to evaluate the effects of varying stiffness.

The pulse severities from various vehicle impact tests need to be assessed during the impact structure development and targeting stage to assure that the occupants can meet the injury criteria as required. The conventional method using TTZV (time to zero velocity), TDC (total dynamic crush), and G1/G2 (two stage averaged pulse) is often unable to give a quick and clear answer to the question being raised. A simple numerical tool is developed here to assess the pulse severity with a single parameter in which the severity is expressed as the amount of chest travel under a certain target restraint curve or chest A-D curve. The tool is applied to several front impact vehicle pulses to show the effectiveness. The new method developed here can be used to assess the pulse severity in an easy and objective way along with conventional parameters.

The development of efficient algorithms has been the focus of automobile engineers since self-driving cars become popular. This is due to the potential benefits we can get from self-driving cars and how they can improve safety on our roads. Despite the good promises that come with self-driving cars development, it is way behind being a perfect system because of the complexity of our environment. A self-driving car must understand its environment before it makes decisions on how to navigate, and this might be difficult because the changes in our environment is non-deterministic. With the development of computer vision, some key problems in intelligent driving have been active research areas. The advances made in the field of artificial intelligence made it possible for researchers to try solving these problems with artificial intelligence. Lane detection and tracking is one of the critical problems that need to be effectively implemented.

A statistical theory is used to quantify the amount of information transmitted from a transducer (i.e., accelerometer) to the air bag controller during a vehicle crash. The amount of information relevant to the assessment of the crash severity is evaluated. This quantification procedure helps determine the effectiveness of different testing conditions for the calibration of sensor algorithms. The amount of information in an acceleration signal is interpreted as a measure of the ability to separate signals based on parameters that are used to assess the severity of an impact. Applications to a linear spring-mass model and to actual crash signals from a development vehicle are presented. In particular, the comparison of rigid barrier (RB) and offset deformable barrier (ODB) testing modes is analyzed. Also, the performance of front-mounted and passenger compartment accelerometers are compared.

Today, commercially available drones have limited use-cases in the rapidly evolving community. However, with advances in drone and software technology, it is possible to utilize these aerial machines to solve problems in a variety of industries such as mining, medical, construction, and law enforcement. For example, in order to reduce time of investigation, Indiana State Police are currently utilizing ad-hoc commercial drones to reconstruct crash scenes for insurance and legal purposes. In this paper, we illustrate how to effectively integrate drones for in-vehicle services and real-time prediction for automotive applications. In order to accomplish this, we first integrate simpler controls such as voice-commands to control the drone from the vehicle. Next, we build smart prediction software that monitors vehicle behavior and reacts in real-time to collisions.

In 1998, Rouhana et al. described development of a new device, called the IR-TRACC (InfraRed - Telescoping Rod for Assessment of Chest Compression). In its original concept, the IR-TRACC uses two infrared LEDs inside of a telescoping rod to measure deflection. One LED serves as a light transmitter and the other as a light receiver. The output from the receiver LED is converted to a linear function of chest compression using an analog circuit. Tests have been performed with IR-TRACC units at various labs around the world since 1998. A first-generation IR-TRACC system was retrofit into a Q3 dummy by TNO. Similarly, a mid sized male Hybrid III dummy thorax and a small female Hybrid III dummy thorax have been designed by First Technology Safety Systems (FTSS) such that each contains 4 second-generation IR-TRACC units. The second-generation IR-TRACC is the result of continued development by FTSS, especially in the areas of the analysis circuit, manufacturing and calibration methods.

Digital Image Correlation (DIC) technology is a powerful tool in the field of experimental mechanics to obtain the full-field deformation/strain information of an object. It has been rapidly applied in industry in recent years. However, for the large-angle full-field strain measurement of small-sized cylindrical objects, it’s still a challenge to the DIC accurate measurement due to its small size and curved surface. In this paper, a measurement method based on the multi-camera DIC system is proposed to study the compressive performance of small-sized cylindrical materials. Three cameras form two stereo DIC measurement systems (1 and 2 cameras, and 2 and 3 cameras), each of which measures a part of the object. By calibrating three cameras at the same time, two stereos DIC coordinate systems can be unified to one coordinate system. Then match the two sets of DIC measurement data together to achieve large-angle measurement of the cylindrical surface.