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Crash reconstruction is a scientific process that utilizes principles of physics and empirical data to analyze the physical, electronic, video, audio, and testimonial evidence from a crash to determine how and why the crash occurred. This course will introduce this reconstruction process as it gets applied to various crash types - in-line and intersection collisions, pedestrian collisions, motorcycle crashes, rollover crashes, and heavy truck crashes. Methods of evidence documentation will be covered. Analysis methods will also be presented for electronic data from event data recorders and for video.

Safety continues to be one of the most important factors in motor vehicle design, manufacturing, and marketing.  This course provides a comprehensive overview of these critical automotive safety considerations: injury and anatomy; human tolerance and biomechanics; occupant protection; testing; and federal legislation. The knowledge shared at this course enables participants to be more aware of safety considerations and to better understand and interact with safety experts. This course has been approved by the Accreditation Commission for Traffic Accident Reconstruction (ACTAR) for 18 Continuing Education Units (CEUs).

Convolutional neural networks are the de facto method of processing camera, radar, and lidar data for use in perception in ADAS and L4 vehicles, yet their operation is a black box to many engineers. Unlike traditional rules-based approaches to coding intelligent systems, networks are trained and the internal structure created during the training process is too complex to be understood by humans, yet in operation networks are able to classify objects of interest at error rates better than rates achieved by humans viewing the same input data.

Autonomous Driving is being utilized in various settings, including indoor areas such as industrial halls. Additionally, LIDAR sensors are currently popular due to their superior spatial resolution and accuracy compared to RADAR, as well as their robustness to varying lighting conditions compared to cameras. They enable precise and real-time perception of the surrounding environment. Several datasets for on-road scenarios such as KITTI or Waymo are publicly available. However, there is a notable lack of open-source datasets specifically designed for industrial hall scenarios, particularly for 3D LIDAR data. Furthermore, for industrial areas where vehicle platforms with omnidirectional drive are often used, 360° FOV LIDAR sensors are necessary to monitor all critical objects. Although high-resolution sensors would be optimal, mechanical LIDAR sensors with 360° FOV exhibit a significant price increase with increasing resolution.

Autonomous driving is a hot topic in the automotive domain, and there is an increasing need to prove its reliability. They use machine learning techniques, which are themselves stochastic techniques based on some kind of statistical inference. The occurrence of incorrect decisions is part of this approach and often not directly related to correctable errors. The quality of the systems is indicated by statistical key figures such as accuracy and precision. Numerous driving tests and simulations in simulators are extensively used to provide evidence. However, the basis of all descriptive statistics is a random selection from a probability space. The difficulty in testing or constructing the training and test data set is that this probability space is usually not well defined. To systematically address this shortcoming, ontologies have been and are being developed to capture the various concepts and properties of the operational design domain.

This title includes the technical papers developed for the 2023 Stapp Car Crash Conference, the premier forum for the presentation of research in impact biomechanics, human injury tolerance, and related fields, advancing the knowledge of land-vehicle crash injury protection. The conference provides an opportunity to participate in open discussion about the causes and mechanisms of injury, experimental methods and tools for use in impact biomechanics research, and the development of new concepts for reducing injuries and fatalities in automobile crashes.

Aeroacoustics is important in the automotive industry, as it significantly influences driving comfort. Particularly in the case of battery electric vehicles (BEVs), the flow noise is already crucial at lower driving speeds, since these generate barely any drive noise and the masking effects produced by the engine are eliminated. Due to the increasing importance of drag minimization and elimination of the exhaust system, the underbody of BEVs is typically very streamlined and exhibits a low acoustic interference potential. However, even small geometric modifications to the vehicle can lead to changes in the flow around the vehicle and consequently to significant noise sources. Thus, significant flow resonances in the low frequency range below 30 Hz have been detected on certain vehicle configurations. Initial investigations have shown that the flow around the front wheel spoilers is relevant for the development of the flow phenomenon.

Design verification and quality control of automotive components require the analysis of the source location of ultra-short sound events, for instance the engaging event of an electromechanical clutch or the clicking noise of the aluminium frame of a passenger car seat under vibration. State-of-the-art acoustic cameras allow for a frame rate of about 100 acoustic images per second. Considering that most of the sound events introduced above can be far less than 10ms, an acoustic image generated at this rate resembles an hard-to-interpret overlay of multiple sources on the structure under test along with reflections from the surrounding test environment. This contribution introduces a novel method for visualizing impulse-like sound emissions from automotive components at 10x the frame rate of traditional acoustic cameras.

RMS (Reliability-Maintainability-Safety-Supportability) engineering is emerging as the newest discipline in product development due to new credible, accurate, quantitative methods. Weibull Analysis is foremost among these new tools. New and advanced Weibull techniques are a significant improvement over the original Weibull approach. This workshop, originally developed by Dr. Bob Abernethy, presents special methods developed for these data problems, such as Weibayes, with actual case studies in addition to the latest techniques in SuperSMITH® Weibull for risk forecasts with renewal and optimal component replacement.

Many technical projects, most vehicle and component testing, and all accident reconstructions, product failure analyses, and other forensic investigations, require photographic documentation. Roadway evidence disappears, tested or wrecked vehicles are repaired, disassembled, or scrapped, and components can be tested for failure. Photographs are frequently the only evidence that remains of a wreck, or the only records of subjects before or during tests. Making consistently good images during any inspection is a critical part of the evaluation process. 

This document presents a study on the design and simulation of a high-lift airfoil intended for usage in multielement setups such as the wings present on open-wheel race cars. With the advancement of open-wheel race car aerodynamics, the design of existing high-lift airfoils has been altered to create a more useful and practical general profile. Adjoint optimization tools in CFD (ANSYS Fluent) were employed to increase the airfoil’s performance beyond existing high-lift profiles (Selig S1223). Improvements of up to 20% with a CL of 2.4 were recorded. To further evaluate performance, the airfoil was made the basis of a full three-dimensional aerodynamics package design for an open-wheel Formula Student car. CFD simulations were carried out on the same and revealed performance characteristics of the airfoil in a more practical application. These CFD simulations were calibrated with experimental values from coast-down testing data with an accuracy of 8%.

From SAE International, SAE MobilityRxiv is a free online preprint server and sharing service for English-language preprints in the mobility/transportation field

The ADAS to Automated Driving Digital Summit will virtually bring together engineering professionals from OEMs, suppliers, technology and corporate and academic research. And now, with no travel required, the digital summit will reach an even broader international audience from North America, Europe, and Asia.

Now Available from SAE International, SAE OnQue is a revolutionary digital standards solution that optimizes the way automotive and aerospace engineers access standards.

Contact our Sales Team! The ADAS to Automated Driving Digital Summit will virtually bring together engineering professionals from OEMs, suppliers, technology and corporate and academic research. And now, with no travel required, the digital summit will reach an even broader international audience from North America, Europe, and Asia.

digital standards systems an integrated approach to engineering standards usage

Aluminum and its alloys entered a main role in the engineering sectors because of their applicable characteristics for indispensable applications. To enhance requisite belongings for the components, the composition of variant metal/nonmetal with light metal alloys is essential in the manufacturing industries. To enhance the wear resistance with significant strength property of the aluminum alloy 2024, the reinforcement SiC and fly ash (FA) were added with the designation Al2024 + 10% SiC; Al2024 + 5% SiC + 5% FA; and Al2024 + 10% FA via stir-casting technique. The wear resistance property of the composites was tested in pin-on-disc with a dry-sliding wear test procedure. The experiment trials were designed in Box–Behnken design (BBD) by differing the wear test parameters like % of reinforcement, sliding distance (m), and load (N).

The objectives of this study were to provide insights on how injury risk is influenced by occupant demographics such as sex, age, and size; and to quantify differences within the context of commonly-occurring real-world crashes. The analyses were confined to either single-event collisions or collisions that were judged to be well-defined based on the absence of any significant secondary impacts. These analyses, including both logistic regression and descriptive statistics, were conducted using the Crash Investigation Sampling System for calendar years 2017 to 2021. In the case of occupant sex, the findings agree with those of many recent investigations that have attempted to quantify the circumstances in which females show elevated rates of injury relative to their male counterparts given the same level bodily insult. This study, like others, provides evidence of certain female-specific injuries.

With the current trend of including the evaluation of the risk of brain injuries in vehicle crashes due to rotational kinematics of the head, two injury criteria have been introduced since 2013 – BrIC and DAMAGE. BrIC was developed by NHTSA in 2013 and was suggested for inclusion in the US NCAP for frontal and side crashes. DAMAGE has been developed by UVa under the sponsorship of JAMA and JARI and has been accepted tentatively by the EuroNCAP. Although BrIC in US crash testing is known and reported, DAMAGE in tests of the US fleet is relatively unknown. The current paper will report on DAMAGE in NCAP-like tests and potential future frontal crash tests involving substantial rotation about the three axes of occupant heads. Distribution of DAMAGE of three-point belted occupants without airbags will also be discussed. Prediction of brain injury risks from the tests have been compared to the risks in the real world.