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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.  

The calibration of Engine Control Units (ECUs) for road vehicles is challenged by stringent legal and environmental regulations, coupled with short development cycles. The growing number of vehicle variants, although sharing similar engines and control algorithms, requires different calibrations. Additionally, modern engines feature increasingly number of adjustment variables, along with complex parallel and nested conditions within the software, demanding a significant amount of measurement data during development. The current state-of-the-art (White Box) model-based ECU calibration proves effective but involves considerable effort for model construction and validation. This is often hindered by limited function documentation, available measurements, and hardware representation capabilities. This article introduces a model-based calibration approach using Neural Networks (Black Box) for two distinct ECU functional structures with minimal software documentation.

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.

              Recent advances in commercial vehicle equipment have increased the potential for incident-related data to be recorded surrounding a collision event.  

The descent phase of GAGANYAAN (Indian Manned Space Mission) culminates with a crew module impacting at a predetermined site in Indian waters. During water impact, huge amount of loads are experienced by the astronauts. This demands an impact attenuation system which can attenuate the impact loads and reduce the acceleration experienced by astronauts to safe levels. Current state of the art impact attenuation systems use honeycomb core, which is passive, expendable, can only be used once (at touchdown impact) during the entire mission and does not account off-nominal impact loads. Active and reusable attenuation systems for crew module is still an unexplored territory. Three configurations of impact attenuators were selected for this study for the current GAGANYAAN crew module configuration, namely, hydraulic damper, hydro-pneumatic damper and airbag systems.

Heavy Vehicle Event Data Recorders (HVEDRs) have the ability to capture important data surrounding an event such as a crash or near crash. Efforts by many researchers to analyze the capabilities and performance of these complex systems can be problematic, in part, due to the challenges of obtaining a heavy truck, the necessary space to safely test systems, the inherent unpredictability in testing, and the costs associated with this research. In this paper, a method for simulating vehicle speed sensor (VSS) inputs to HVEDRs to trigger events is introduced and validated. Full-scale instrumented testing is conducted to capture raw VSS signals during steady state and braking conditions. The recorded steady state VSS signals are injected into the HVEDR along with synthesized signals to evaluate the response of the HVEDR. Brake testing VSS signals are similarly captured and injected into the HVEDR to trigger an event record.

The Advanced Driver Assistance System (ADAS) is a comprehensive feature set designed to aid a driver in avoiding or reducing the severity of collisions while operating the vehicle within specified conditions. In General Motors (GM) vehicles, the primary controller for the ADAS is the Active Safety Control Module (ASCM). In the 2013 model year, GM introduced an ASCM utilizing the GM internal nomenclature of External Object Calculation Module (EOCM) in some of their vehicles produced for the North American market. Similar to the Sensing and Diagnostic Module (SDM) utilized in the restraints system, the EOCM3 LC contains an Event Data Recorder (EDR) function to capture and record information surrounding certain ADAS or Supplemental Inflatable Restraint (SIR) events. The ASCM EDR contains information from external object sensors, various chassis and powertrain control modules, and internally calculated data.

When investigating traffic accidents, it is important to determine the causes. To do so, it is necessary to reconstruct the accident situation accurately and in detail using objective and diverse information. We propose a method for reconstructing the accident situation (“reconstruction method”) which consists of rebuilding the situation immediately before the collision (“pre-crash situation”) using data collected during that time by an event data recorder (EDR) and a dashboard camera (DBC) onboard one or both of the vehicles involved. First, the vehicle’s traveling trajectory was integrally calculated using the vehicle speed and yaw rate recorded by the EDR, each point along the trajectory being linked to the EDR data.

The on-board emergency call system with accurate occupant injury prediction can help rescuers deliver more targeted traffic accident rescue and save more lives. We use machine learning methods to establish, train, and validate a number of classification models that can predict occupant injuries (by determining whether the MAIS (Maximum Abbreviated Injury Scale) level is greater than 2) based on crash data, and ranked the correlation of some factors affecting vehicle occupant injury levels in accidents. The optimal model was selected by the model prediction accuracy, and the Grid Search method was used to optimize the hyper-parameters for the model.

The handling of flexible components creates a unique problem set for pick and place automation within automotive production processes. Fabrics and woven textiles are examples of flexible components used in car interiors, for air bags, as liners and in carbon-fiber layups. These textiles differ greatly in geometry, featuring complex shapes and internal slits with varying material properties such as drape characteristics, crimp resistance, friction, and fiber weave. Being inherently flexible and deformable makes these materials difficult to handle with traditional rigid grippers. Current solutions employ adhesive, needle-based, and suction strategies, yet these systems prove a higher risk of leaving residue on the material, damaging the weave, or requiring complex assemblies. Pincer-style grippers are suitable for rigid components and offer strong gripping forces, yet inadvertently may damage the fabric, and introduce wrinkles / folded-over edges during the release process.

In this study, an optimized structure for opening the headlining considering the deployment of the face-to-face roof airbag was studied. It was confirmed that the deployment performance differs depending on the skin of the headlining, and a standardized structure with mass production was proposed. Non-woven fabric and Tricot skin, which are economical and high-end specifications, satisfy the performance of PVC fusion application specifications after cutting 80% of the skin. The structure that satisfies the entire body including the knit specifications is a type that separates the roof airbag area piece, the corresponding soft piece is separated, and the deployment performance is satisfied with safety. Therefore, the structure is proposed as a standardized structure. This structure is expected to be applicable to roof DAB (Driver Airbag), PAB (Passenger Airbag), and Sunroof Airbag, which will be necessary technologies to secure indoor space.

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.

Occupant protection in side impacts, in particular for near-side occupants, is a challenge due to the occupant’s close proximity to the impact. Near-side occupants have limited space to ride down the impact. Curtain and side airbags fill the gap between occupant and the side interior. This analysis was conducted to provide insight on the characteristics of side impacts and the relevancy of currently regulated test configurations. For this purpose, 2007-2015 NASS-CDS and 2017-2021 CISS side crash data were analyzed for towed light vehicles. 2008 and newer model year vehicle data was selected to ensure that most vehicles were equipped with side/curtain airbags. The results showed that side impacts accounted for approximately 26.7% of the vehicles involved and 18.9% of the vehicles with at least one seriously injured occupant. Most side impacts involved damage to the front and front-to-center of the vehicle.

There are established federal requirements and industry standards for frontal crash testing of motor vehicles. Consistently applied methods support reliability, repeatability, and comparability of performance metrics between tests and platforms. However, real world collisions are rarely identical to standard test protocols. This study examined the effects of occupant anthropometry and passive restraint deployment timing on occupant kinematics and biomechanical loading in a moderate-severity (approximately 30 kph delta-V) offset frontal crash scenario. An offset, front-to-rear vehicle-to-vehicle crash test was performed, and the dynamics of the vehicle experiencing the frontal collision were replicated in a series of three sled tests. Crash test and sled test vehicle kinematics were comparable. A standard or reduced-weight 50th percentile male Hybrid III ATD (H3-50M) or a standard 5th percentile female Hybrid III ATD (H3-5F) was belted in the driver’s seating position.

Event data recorders (EDRs) were harvested and imaged after Insurance Institute for Highway Safety (IIHS) 56 km/hr frontal and 64.4 km/hr frontal offset crashes of 15 different brands of 2016-2022 vehicles. The speed and delta-V in the EDR were compared to reference instrumentation. Speed data was accurate within the generally accepted range of +/-4%. The 40% overlap tests had generally similar vehicle kinematics, and their delta-Vx data was accurate. However, there was a much greater variance in the small (25%) overlap tests. Some outliers in the small overlap delta-Vx tests required further analysis using overhead video analysis. The video analysis more closely matched the EDR recorded values. These offset tests create significant post-crash rotation, and both EDR and IIHS instrumentation were affected by their location away from the center of gravity. The Y-axis was affected much more than the X-axis.