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).
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.
This class will provide the student with the skills, knowledge, and abilities to interpret, analyze and apply HVEDR data in real-world applications. This course has been designed to build on the concepts presented in the SAE course Accessing and Interpreting Heavy Vehicle Event Data Recorders (ID# C1022). Advanced topics will include associating HVEDR data with collision events through timestamps, odometer logs, and data signatures, validating HVEDR speed data using specified vehicle parameters, performing time and distance analyses using HVEDR data, and correlating HVEDR data to physical evidence from the vehicle and roadway.
For automotive engineers involved in crash reconstruction and analysis, a knowledge of basic accident reconstruction principles and techniques is essential, but often insufficient to answer all of the questions posed by design engineers, regulators, and lawyers. This course takes participants beyond the basics of accident reconstruction to physical models and analysis techniques that are unique to the reconstruction of single-vehicle rollover crashes.
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.
Photographs and video recordings of vehicle crashes and accident sites are more prevalent than ever, with dash mounted cameras, surveillance footage, and personal cell phones now ubiquitous. The information contained in these pictures and videos provide critical information to understanding how crashes occurred, and analyze physical evidence. This course teaches the theory and techniques for getting the most out of digital media, including correctly processing raw video and photographs, correcting for lens distortion, and using photogrammetric techniques to convert the information in digital media to usable scaled three-dimensional data.
EDR's were first installed in 1994 and are now installed in 99% of new light vehicles sold in the US. In the US EDR’s are not required, but vehicles with EDR’s made after 9/1/2012 must meet minimum standardized content requirements of 49 CFR, Part 563 including speed, throttle, brake on/off and Delta V. Data must be retrievable with a publicly available tool. Only a few manufacturers install EDR’s worldwide currently, but the EU and China are adopting regulations to require them in the next few years.
Determining occupant kinematics in a vehicle crash is essential when understanding injury mechanisms and assessing restraint performance. Identifying contact marks is key to the process. This study was conducted to assess the ability to photodocument the various fluids on different vehicle interior component types and colors with and without the use of ultraviolet (UV) lights. Biological (blood, saliva, sweat and skin), consumable and chemical fluids were applied to vehicle interior components, such as seatbelt webbing, seat and airbag fabrics, roof liner and leather steering wheel. The samples were photodocumented with natural light and UV light (365 nm) exposure immediately after surface application and again 14 days later. The review of the photos indicated that fabric type and color were important factors. The fluids deposits were better visualized on non-porous than porous materials. For example, blood was better documented on curtain airbags than side or driver airbags.
Data-driven driving safety assessment is crucial in understanding the insights of traffic accidents caused by dangerous driving behaviors. Meanwhile, quantifying driving safety through well-defined metrics in real-world naturalistic driving data is also an important step for operational safety assurance of automated vehicles (AV). However, the lack of comprehensive data and methodologies for fine-grained analysis has hindered progress in this critical area. In response to this challenge, we propose a novel dataset for driving safety metrics analysis specifically tailored to car-following situations. Leveraging state-of-the-art technology, we employ drones to capture high-resolution video data at 12 different traffic scenes in the Phoenix metropolitan area, deploy advanced Artificial Intelligence (AI) algorithms to extract precise vehicle trajectories, and utilize semantic maps to discern leader-follower relations among vehicles.
Typical everyday driving situations involve ranges of accelerations relevant to accident reconstruction. Understanding the motions and forces in everyday maneuvers can help define the accelerations experienced by the vehicle and the occupants. This paper evaluates various everyday driving conditions such as speedbumps, dips, left turns, right turns, moderate braking and acceleration while using a data acquisition system to record the forces experienced by the vehicle.
Commercial combination vehicles, configured as a truck-tractor and semitrailer, are designed to transport a wide variety of heavy loads over great distances. Because of their size, mass and proportions, combination vehicles have less lateral roll stability than other types of vehicles on the road. As a result, it is easier for drivers to maneuver their commercial combination vehicle beyond its rollover threshold, compared to drivers of light vehicles. Few publications in the public domain explore the unique dynamics of combination vehicle rollovers. Even fewer publications correlate available reconstruction methods with available experimental data. To better understand commercial combination vehicle rollovers, IMMI conducted two quarter-turn rollover crash tests involving a remotely controlled truck-tractor and a loaded van semitrailer.
Cellular Vehicle-to-Everything (C-V2X) is seen as an enabler for fully automated driving. It can provide the needed information about traffic situations and road users ahead of time compared to the on-board sensors which are limited to line-of-sight detections. This paper investigated the effectiveness of utilizing the C-V2X technology for a valet parking collision mitigation feature. For this study a LiDAR was mounted at the FEV North America parking lot in a hidden intersection with a C-V2X roadside unit. This unit was used to process the LiDAR point cloud and transmit the information of the detected objects to an onboard C-V2X unit. The received data was provided as input to the path planning and controls algorithms so that the onboard controller can make the right decision while approaching the hidden intersection. FEV’s Smart Vehicle Demonstrator was utilized to test the C-V2X setup and the developed algorithms.
Event date recorders were harvested after frontal and frontal offset crashes of late model IIHS vehicles, and the speed and Delta V in the EDR were compared to reference instrumentation. Most speed data agreed within previously published ranges. Most Delta V data also agreed, but there were some significant outliers beyond the generally accepted accuracy. The outliers were noted to be in cases of significant post crash rotation, and the reference instrumentation was located aft of the EDR. Corrections for sensor location were evaluated. In addition, overhead video analysis was used to calculate a Delta V which compared favorably to the EDR recorded values. (Note an early data set looking only at EDR vs IIHS reference instrumentation and not explaining outliers was presented at WREX, since then the video analysis has been added and we intend to increase the sample size and contact IIHS to gather their input on the instrumentation discrepancies.)
With the rapid development of electric vehicles, lithium-ion batteries(LIBs) with high energy and power density have been widely applied as the power producer of electric vehicles. To meet consumer demand for high power and long driving distance, the energy and power density of LIBs are getting higher and higher. However, LIBs with higher energy are more prone to catastrophic thermal runaway. In recent years, electric vehicle accidents due to thermal runaway(TR) of LIBs have been frequently reported, which make consumers lose confidence in electric vehicles. In order to solve the problem, we must understand the mechanism of LIBs TR, thereby reducing the likelihood of thermal runaway in electric vehicles. However, the inducement mechanism of mechanical abuse-induced LIBs TR is very sophisticated. In this paper, the recent evolution of mechanical abuse-induced(including, crushing, bumping and piercing) battery thermal runaway is emphasized.