Estimated injury risk for specific injuries and body regions in frontal motor vehicle crashes

OBJECTIVE

Injury risk curves estimate motor vehicle crash (MVC) occupant injury risk from vehicle, crash, and/or occupant factors. Many vehicles are equipped with event data recorders (EDRs) that collect data including the crash speed and restraint status during a MVC. This study's goal was to use regulation-required data elements for EDRs to compute occupant injury risk for (1) specific injuries and (2) specific body regions in frontal MVCs from weighted NASS-CDS data.

METHODS

Logistic regression analysis of NASS-CDS single-impact frontal MVCs involving front seat occupants with frontal airbag deployment was used to produce 23 risk curves for specific injuries and 17 risk curves for Abbreviated Injury Scale (AIS) 2+ to 5+ body region injuries. Risk curves were produced for the following body regions: head and thorax (AIS 2+, 3+, 4+, 5+), face (AIS 2+), abdomen, spine, upper extremity, and lower extremity (AIS 2+, 3+). Injury risk with 95% confidence intervals was estimated for 15-105 km/h longitudinal delta-Vs and belt status was adjusted for as a covariate.

RESULTS

Overall, belted occupants had lower estimated risks compared to unbelted occupants and the risk of injury increased as longitudinal delta-V increased. Belt status was a significant predictor for 13 specific injuries and all body region injuries with the exception of AIS 2+ and 3+ spine injuries. Specific injuries and body region injuries that occurred more frequently in NASS-CDS also tended to carry higher risks when evaluated at a 56 km/h longitudinal delta-V. In the belted population, injury risks that ranked in the top 33% included 4 upper extremity fractures (ulna, radius, clavicle, carpus/metacarpus), 2 lower extremity fractures (fibula, metatarsal/tarsal), and a knee sprain (2.4-4.6% risk). Unbelted injury risks ranked in the top 33% included 4 lower extremity fractures (femur, fibula, metatarsal/tarsal, patella), 2 head injuries with less than one hour or unspecified prior unconsciousness, and a lung contusion (4.6-9.9% risk). The 6 body region curves with the highest risks were for AIS 2+ lower extremity, upper extremity, thorax, and head injury and AIS 3+ lower extremity and thorax injury (15.9-43.8% risk).

CONCLUSIONS

These injury risk curves can be implemented into advanced automatic crash notification (AACN) algorithms that utilize vehicle EDR measurements to predict occupant injury immediately following a MVC. Through integration with AACN, these injury risk curves can provide emergency medical services (EMS) and other patient care providers with information on suspected occupant injuries to improve injury detection and patient triage.

Initial analysis of archived non-human primate frontal and rear impact data from the biodynamics data resource

OBJECTIVE

The research objective was to conduct an initial analysis of non-human primate (NHP) data from frontal and rear impact events archived in the Biodynamics Data Resource (BDR) records of the Naval Biodynamics Laboratory (NBDL). These rare data, collected between 1973 and 1989, will inform the safety community of upper-end tolerance limits of NHP and may be related to severe crash scenarios.

METHODS

Data from frontal and rear acceleration tests to 93 macaque NHP were examined. Each NHP was fully torso restrained, whereas the head-neck complex was unrestrained. Each NHP underwent between 1 and 21 total runs; 2 total runs was most common-a low-level run and then a high-level run. Following each impact exposure, the NHP was evaluated using a series of medical examinations. Now part of the legacy collection in the BDR, these evaluations were used to assess NHP exposures to be in one of 3 categories: noninjurious, injurious, or fatal. Using reported peak sled acceleration values, data were amenable to survival analysis statistical methodology to derive injury probability curves (IPCs). IPCs were derived for injury and fatality outcomes.

RESULTS

Fatal injuries for both frontal and rear impacts were mostly at the cranio-vertebral junction. In addition to hemorrhage, fatal frontal and rear impact tests both produced predominantly atlanto-occipital dislocations, with and without spinal cord transection. After exclusions, IPCs were derived for frontal and rear impact for both (1) fatal outcome and (2) injurious outcome (any injury including fatal injury). For frontal impact, 53 NHP qualified with 5, 25, and 50% risk for fatality at 89, 105, and 114 peak sled Gs, respectively, and for injurious outcome at 70, 92, and 106 Gs, respectively. For rear impact, 34 NHP qualified with 5, 25, and 50% risk for fatality at 96, 122, 138 peak sled Gs, respectively, and for injurious outcome at 75, 99, and 115 Gs, respectively.

CONCLUSIONS

The majority of injuries were at the cranio-vertebral junction, indicating that the inertial head mass caused a tensile loading mechanism to the cervical spine. These data may be used in conjunction with finite element modeling to estimate risks to the human population. The most direct application in the automotive environment could be to the well-restrained child. The N_ij neck injury criteria, currently based on data from piglet studies, could also benefit because the NHP is a more accurate human surrogate. These types of tests are likely to never be repeated and will form an upper bound of tolerance information valuable to safety system designers.

Prospective assessment of the effectiveness of autonomous emergency braking in car-to-cyclist accidents in France

Objective: This study aimed to assess the effectiveness of autonomous emergency braking (AEB) systems in car-to-cyclist frontal collisions by simulating their effects, in terms of crash avoidance and injury mitigation, on a representative target population of real-world accidents. Identifying effectiveness-critical AEB-cyclist design parameters through a sensitivity analysis was also targeted.Methods: The analysis is based on a representative set of real-world car-to-cyclist frontal collisions gathered from French police reports. AEB-cyclist-relevant accident cases were first selected and used to build injury risk curves for fatal, severe, and slight cyclist injuries. The effect of AEB-cyclist on these cases was then simulated by means of a car kinematic model involving sensor detection strategies and actuator actions. Combining the resulting simulated impact speed distributions with the injury risk curves allowed to assess AEB-cyclist's effectiveness in terms of lives saved and mitigated injuries. Using design of experiments methods, the sensitivity of this effectiveness with regards to AEB-cyclist design parameters could be assessed.Results: Cyclist injury risks curves were built, along with their confidence intervals, for fatal, severe, and slight injuries using a polytomous complementary log-log regression model, with squared impact speed as an independent variable. A sensitivity analysis on an ideal bisensor AEB-cyclist setting highlighted influential design parameters such as maximal braking intensity or crucial decision algorithm parameters such as maximal time and distance to collision thresholds. AEB-cyclist effectiveness was nevertheless shown to range from 35% to 59% in fatalities, 14% to 54% in severe injuries, and 11% to 42% in slight injuries, depending on field of view parameters alone, once reference values of decision algorithm parameters had been set.Conclusions: This study illustrates the potential benefits and limits of AEB-cyclist systems. High-end systems show acceptable effectiveness rates, but road safety performance strongly depends on external factors such as road surface conditions or has to be tuned in order to avoid unnecessary activations and driver discomfort. Limits of the system's everyday use (lack of maintenance, driver reaction time to collision warnings, etc.) were not taken into account, thus resulting in optimistic evaluations of AEB-cyclist effectiveness.

Development of injury risk models to guide CT evaluation in the emergency department after motor vehicle collisions

OBJECTIVE

The clinical evaluation of motor vehicle collision (MVC) victims is challenging and commonly relies on computed tomography (CT) to detect internal injuries. CT scans are financially expensive and each scan exposes the patient to additional ionizing radiation with an associated, albeit low, risk of cancer. Injury risk prediction based on regression modeling has been to be shown to be successful in estimating Injury Severity Scores (ISSs). The objective of this study was to (1) create risk models for internal injuries of occupants involved in MVCs based on CT body regions (head, neck, chest, abdomen/pelvis, cervical spine, thoracic spine, and lumbar spine) and (2) evaluate the performance of these risk prediction models to predict internal injury.

METHODS

All Abbreviated Injury Scale (AIS) 2008 injury codes were classified based on which CT body region would be necessary to scan in order to make the diagnosis. Cases were identified from the NASS-CDS. The NASS-CDS data set was queried for cases of adult occupants who sought medical care and for which key crash characteristics were all present. Forward stepwise logistic regression was performed on data from 2010-2014 to create models predicting risk of internal injury for each CT body region. Injury risk for each region was grouped into 5 levels: very low (<2%), low (2-5%), medium (5-10%), high (10-20%), and very high (20%). The models were then tested using weighted data from 2015 in order to determine whether injury rates fell within the predicted risk level.

RESULTS

The inclusion and exclusion criteria identified 5,477 cases in the NASS-CDS database. Cases from 2010-2014 were used for risk modeling (n = 4,826). Seven internal injury risk models were created based on the CT body regions using data from 2010-2014. These models were tested against data from 2015 (n = 651). In all CT body regions, the majority of occupants fell in the very low or low predicted injury rate groups, except for the head. On average, 57% of patients were classified as very low risk and 15% as low risk for each body region. In most cases the actual rate of injury was within the predicted injury risk range. The 95% confidence interval overlapped with predicting injury risk range in all cases.

CONCLUSION

This study successfully demonstrated the ability for internal injury risk models to accurately identify occupants at low risk for internal injury in individual body regions. This represents a step towards incorporating telemetry data into a clinical tool to guide physicians in the use of CT for the evaluation of MVC victims.

Complex Neck Loading and Injury Tolerance in Lateral Bending With Head Rotation From Human Cadaver Tests

Advancements in automated vehicles may position the occupant in postures different from the current standard posture. It may affect human tolerance responses. The objective of this study was to determine the lateral bending tolerance of the head-cervical spine with initial head rotation posture using loads at the occipital condyles and lower neck and describe injuries. Using a custom loading device, head-cervical spine complexes from human cadavers were prepared with load cells at the ends. Lateral bending loads were applied to prerotated specimens at 1.5 m/s. At the occipital condyles, peak axial and antero-posterior and medial-lateral shear forces were: 316-954 N, 176-254 N, and 327-508 N, and coronal, sagittal, and axial moments were: 27-38 N·m, 21-38 N·m, and 9.7-19.8 N·m, respectively. At the lower neck, peak axial and shear forces were: 677-1004 N, 115-227 N, and 178-350 N, and coronal, sagittal, and axial moments were: 30-39 N·m, 7.6-21.3 N·m, and 5.7-13.4 N·m, respectively. Ipsilateral atlas lateral mass fractures occurred in four out of five specimens with varying joint diastasis and capsular ligament involvements. Acknowledging that the study used a small sample size, initial tolerances at the occipital condyles and lower neck were estimated using survival analysis. Injury patterns with posture variations are discussed.

Lower neck injury criteria for THOR and Hybrid III dummies in rear impact

OBJECTIVE

The objectives of the study are to derive lower-neck-injury probability curves under rear impact loading from matched pair Hybrid III and THOR dummy tests.

METHODS

Twelve whole-body and 15 isolated head-neck rear-impact sled tests were conducted using the 2 dummies. They were positioned on a rigid seat that was attached to an acceleration sled. The dummies were positioned with the head parallel to the ground, torso against the seat back, and legs stretched such that there was no axial rotation. The acceleration pulse matched the previous in-house human cadaver tests. The 6-axis lower-neck load cell was used in both dummies. For the isolated rear-impact tests, the head-neck was excised from the dummies, and the lower-neck load cell was mounted to the top of the sled with the head parallel to the ground and such that the acceleration vector was in the rear impact mode. Lower-neck loads and lower-neck-injury criteria (LN_ij) were obtained using the load cell data and survival analysis was used to develop injury-assessment-risk values and curves for both dummies. The LN_ij criteria were determined for both dummies using the intercept value corresponding to the 90% probability level for the forces and moments.

RESULTS

The lognormal and Weibull distributions were the optimal distributions for the Hybrid III and THOR devices. At the 50% risk level, the mean LN_ij was 1.1 and NCIS was 0.77 for the Hybrid III, and 1.5 and 0.30 for the THOR device. The quality indices were in the fair range and good range for the 2 dummies, respectively, at this risk level.

CONCLUSIONS

The lower neck based LN_ij injury assessment risk curves and reference values, IARCs and IARVs, serve as the first dataset for injury assessments, and the THOR may be a better test device for assessing injures in rear impact environments.