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Recent field data have shown that the occupant protection in vehicle rear seats failed to keep pace with advances in the front seats likely due to the lack of advanced safety technologies. The objective of this study was to optimize advanced restraint systems for protecting rear seat occupants with a range of body sizes under different frontal crash pulses. Three series of sled tests (baseline tests, advanced restraint trial tests, and final tests), MADYMO model validations against a subset of the sled tests, and design optimizations using the validated models were conducted to investigate rear seat occupant protection with 4 Anthropomorphic Test Devices (ATDs) and 2 crash pulses.

Define recommendations for color coding of child restraint labels, specifically focused on the information contained therein and whether it provides information for installation in a forward facing, rear facing, or booster mode.

1. SCOPE These guidelines should be considered: When implementing the LATCH system in vehicle seating positions that will be designated by the vehicle owner’s manual and in the information included in the owners manual. When implementing the LATCH system in child restraint designs that include the LATCH system and in the information included in the instruction manual

Accident data show that the head and the chest are the most frequently injured body regions in side impact fatal accidents. Curtain side air bag (CSA) and thorax side air bag (SAB) have been installed by manufacturers for the protection devices for these injuries. In this research, first we studied the recent side impact accident data in Japan and verified that the head and chest continued to be the most frequently injured body regions in fatal accidents. Second, we studied the occupant seating postures in vehicles on the roads, and found from the vehicle's side view that the head location of 56% of the drivers was in line or overlapped with the vehicle's B-pillar. This observation suggests that in side collisions head injuries may occur frequently due to contacts with the B-pillar. Third, we conducted a side impact test series for struck vehicles with and without CSA and SAB.

Accident statistics shows pedestrian accident fatalities as one of the important concerns globally. In view of this, new test protocols for pedestrian safety have been drafted in regulation as well as in consumer group. Also as per new ENCAP requirements, pedestrian safety assessment is used as one of the four assessment criteria's (Adult protection, child safety, pedestrian safety, safety assist) in deciding the overall vehicle safety. Hence today importance of pedestrian safety is perceived as never before in vehicle development program. Basically pedestrian safety evaluation involves subsystem level (head form, upper leg form and lower leg form) impact tests representing human body parts, at specific region on test vehicle with injury limits to decide the severity of impact. In general these injuries are governed by vehicle styling, vehicle stiffness, hard points clearances from vehicle exterior like bonnet, bumper etc.

Prediction of drowsiness based on an objective measure is demanded in machine and vehicle operations, in which human error may cause fatal accidents. Recently, we focused on the pupil which is controlled by the autonomic nervous system, easily and non-invasively observable from the outside of the body. Prior to the large low frequency pupil-diameter fluctuation, which is known to associate with drowsiness, a Gradual Miosis was observed in most subjects. During this miosis period, the subjects were not yet aware of their drowsiness. We have developed a software system which automatically detects the Gradual Miosis in real time.

Approximately 600,000 fatalities occur each year as a result of pedestrians being impacted by motor vehicles (World Bank, 2008). Previous studies (Heller et al., 2009) have utilized databases such as the National Inpatient Sample (NIS) to gain a more thorough understanding of the common injury patterns that occur in real-world traffic collisions involving pedestrians in the United States. The NIS contains records on five to eight million hospital stays annually and provides a wealth of information regarding injuries to hospitalized pedestrian casualties in the U.S. Because of the large number of applicable records in the NIS and the randomized sampling procedure, the data can be used to complete analyses that are not possible with smaller databases such as the Pedestrian Crash Data Study (PCDS), which is not intended to be statistically representative of pedestrian crashes in general.

The statistical analysis of vehicle crash accident data is generally problematic. Data from commonly used sources is almost never without error and complete. Consequently, many analyses are contaminated with modeling and system identification errors. In some cases the effect of influential factors such as crash severity (the most significant component being speed) driver behavior prior to the crash, etc. on vehicle and occupant outcome is not adequately addressed. The speed that the vehicle is traveling at the initiation of a crash is a significant contributor to occupant risk. Not incorporating it may make an accident analysis irrelevant; however, despite its importance this information is not included in many of the commonly used crash data bases, such as the Fatality Analysis Reporting System (FARS). Missing speed information can result in potential errors propagating throughout the analysis, unless a method is developed to account for the missing information.

Pre-collision systems (PCS) use forward-looking sensors to detect the location and motion of vehicles ahead and provide a sequence of actions to help the driver either avoid striking the rear-end of another vehicle or mitigate the severity of the crash. The actions include driver alerts, amplification of driver braking as distance decreases (dynamic brake support, DBS), and automatic braking if the driver has not acted or has not acted sufficiently (crash imminent braking, CIB). Recent efforts by various organizations have sought to define PCS objective test procedures and test equipment in support of consumer information programs and potential certification. This paper presents results and insights from conducting DBS and CIB tests on two production vehicles sold in the US. Eleven scenarios are used to assess the systems' performance. The two systems' performance shows that commercial systems can be quite different.

As software (SW) becomes more and more an important aspect of embedded system development, project schedules are requiring the earlier development of software simultaneously with hardware (HW). In addition, verification has increasingly challenged the design of complex mixed-signal SoC products. This is exacerbated for automotive safety critical SoC products with a high number of analogue interfaces (sensors and actuators) to the physical components such as an airbag SoC chipset. Generally, it is widely accepted that verification accounts for around 70% of the total SoC development. Since integration of HW and SW is the most crucial step in embedded system development, the sooner it is done, the sooner verification can begin. As such, any approaches which could allow verification and integration of HW/SW to be deployed earlier in the development process and help to decrease verification effort, (e.g.: accelerate verification runs) are of extreme interest.

As per the 2018 MoRTH accident report, there were 467,044 accidents, out of which 137,726 were fatal which resulted in 151,417 fatalities. In order to get an idea of the reasons for injuries and estimate the benefits of any intervention, a mathematical model should go a long way. This study is aimed at the development of such a model to predict the injuries sustained by the occupants of an M1 vehicle. We used a detailed accident database of 'Road Accident Sampling System India' (RASSI). RASSI, since 2011, has been collecting traffic accident data scientific across various locations in India. In the data, the occupant injuries are classified as No injury, Minor, Serious and Fatal We used the data of about 4700+ M1 occupants for the study & used almost 40 input parameters to determine the outcome. Based on the data, an algorithm was developed with an overall accuracy of about 67%. The parameters represented human, infrastructure, and environment.

The experimental campaign discussed in publication 2019-01-0635 was extended to emulate more vehicle parameters and also to increase severity leading to deployment event. The engine speed (RPM) and Accelerator Pedal Position (APP) were emulated using LabVIEW and added to the previously reported emulated parameters of wheel speeds and brake status. Overlapping non-deployment events were generated and the EDR data is presented enriched with additional (faster) CAN bus data sniffed from the vehicle harness. While the non-deployment events were still generated using the rubber mallet in pendulum configuration as in 2019-01-0635, a series of tests were performed using an Izod pendulum to incrementally increase event severity until deployment event was generated. The Izod pendulum was instrumented with a rotational potentiometer to measure its instantaneous angle while laboratory accelerometers were used to separately measure acceleration.

Transfer or response equations are important as they provide relationships between the responses of different surrogates under matched, or nearly identical loading conditions. In the present study, transfer equations for different body regions were developed via mathematical modeling. Specifically, validated finite element models of the age-dependent Ford human body models (FHBM) and the mid-sized male Hybrid III (HIII50) were used to generate a set of matched cases (i.e., 192 frontal sled impact cases involving different restraints, impact speeds, severities, and FHBM age). For each impact, two restraint systems were evaluated: a standard three-point belt with and without a single-stage inflator airbag. Regression analyses were subsequently performed on the resulting FHBM- and HIII50-based responses. This approach was used to develop transfer equations for seven body regions: the head, neck, chest, pelvis, femur, tibia, and foot.

Fatal crashes involving automated driving systems, has been raising the concern of minimum standard requirement for safety, reliability and performance required for Autonomous Driving System (ADS)/Advanced Driver Assistance System (ADAS) before this cutting-edge technology takes on public roads. Hence, in order to ensure necessary safety requirements of ADS/ADAS systems we propose a runtime active safety assurance module known as SConSert. SConSert performs dynamic risk assessment of “Sensing, Planning and Action module of ADS/ADAS”; to provide minimal risk maneuver in any given driving scenario. The dynamic risk assessment of ADS/ADAS system is based on the operational design domain (ODD) knowledge of the driving scenario plus the sensor capability, ADS/ADAS algorithm requirement and capability, and finally smooth and collision free maneuver requirement.

This paper is based on, and in continuation of the work previously published in ASEE NCS Conference held in Grand Rapids, MI [1]. Automotive seating rail structures are one of the key components in the automotive industry because they carry the entire weight of passenger and they hold the structure for seating foams and other assembled key components such as side airbag and seatbelt systems. The entire seating is supported firmly and attached to the bottom bodywork of the vehicle through the linkage assembly called the seat rails. Seat rails are adjustable in their longitudinal motion which plays an important role in giving the passengers enough leg room to make them feel comfortable. Therefore, seat rails under the various operating conditions, should be able to withstand the weight of the passenger along with the other assembled parts as mentioned above. Also, functional requirements such as crash safety is very important to avoid or to minimize injuries to the occupants.

In highly automated vehicles (HAVs), new seat configurations may be desirable to allow occupants to perform new activities. One of the current HAV concepts is the swiveled seat layout, which might facilitate communication between occupants. The main objective of this study was to investigate the effects of seat swiveling angles on occupant kinematics and injury risk predicted by a Human Body Model (HBM) during a frontal impact. A detailed 50th percentile male HBM (GHBMC M50-O) was subjected to two frontal crash pulses in a sled setup. The model was positioned on a semi-rigid seat and restrained using a pre-inflated airbag and a three-point seatbelt. Simulations included four seat swiveling angles (0, -10, -20, and -30 degrees), three occupant positions (Sedan driver, large VAN driver or Laptop user), two airbag initial locations (nominal or matching the head Y location), and the inclusion of lateral supports on the seat pan.

This SAE Standard describes the interface between an on-vehicle Mayday detection, reporting system and the off-vehicle response center that will manage the response to the vehicle's call for assistance. The automatic detection and reporting by either the intelligent vehicle itself or by the intelligent roadway of a vehicle that is disabled or involved in an accident, [referred to as Automatic Mayday], is one of the key services identified by the ITS America program plan road map. This effort has been identified, as STD SAE J2313 in recent DOT-FHWA efforts to advance needed National standards. This document primarily addresses the responsibilities of a vehicle in detecting and reporting such an event. Equipment suites on each vehicle will vary widely, as will the communications channel employed to report the incident.

The objective of this document is to enhance the test procedure that is used for ejection mitigation testing per the NHTSA guidelines as mentioned in the FMVSS226 Final Rule document (NHTSA Docket No. NHTSA-2011-0004). The countermeasure for occupant ejection testing is to be tested with an 18kg mass on a guided linear impactor using the featureless headform specifically designed for ejection mitigation testing. SAE does not endorse any particular countermeasure for ejection mitigation testing. However, the document reflects guidelines that should be followed to maintain consistency in the test results. Examples of currently used countermeasures include the Inflatable Curtain airbags and Laminated Glass.

The objective of this document is to enhance the test procedure that is used for ejection mitigation testing per the NHTSA guidelines as mentioned in the FMVSS226 Final Rule document (NHTSA Docket No. NHTSA-2011-0004). The countermeasure for occupant ejection testing is to be tested with an 18kg mass on a guided linear impactor using the featureless headform specifically designed for ejection mitigation testing. SAE does not endorse any particular countermeasure for ejection mitigation testing. However, the document reflects guidelines that should be followed to maintain consistency in the test results. Examples of currently used countermeasures include the Inflatable Curtain airbags and Laminated Glass.