Real-world car-to-pedestrian-crash data from an urban centre

Traumatic rupture of the aorta (TRA) remains the second most common cause of death associated with motor vehicle crashes, only less prevalent than brain injury. On average, nearly 8000 people die annually in the United States due to blunt injury to the aorta. It is observed that over 80% of occupants who suffer an aortic injury die at the scene due to exsanguination into the chest cavity. In the current study, eight near side lateral impacts, in which TRA occurred, were reconstructed using a combination of real world crash data reported in the Crash Injury Research and Engineering Network (CIREN) database, finite element (FE) models of vehicles, and the Wayne State Human Body Model - II (WSHBM). For the eight CIREN cases reconstructed, the high strain regions in the aorta closely matched with the autopsy data provided. The peak average maximum principal strains in all of the eight CIREN cases were localized in the isthmus region of the aorta, distal to the left subclavian artery, and averaged at 22 ± 6.2% while the average maximum pressure in the aorta was found to be 117 ± 14.7 kPa.

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Safety Assessment and a Parametric Study of Forward Collision-Avoidance Assist Based on Real-World Crash Simulations

Journal of Advanced Transportation ◽

10.1155/2021/4430730 ◽

2021 ◽

Vol 2021 ◽

pp. 1-24

Author(s):

MohammadReza Seyedi ◽

MohammadReza Koloushani ◽

Sungmoon Jung ◽

Arda Vanli

Keyword(s):

Reaction Time ◽

Real World ◽

Injury Risk ◽

Injury Severity ◽

Reconstruction Method ◽

Vehicle Speed ◽

Related Field ◽

Detection Range ◽

Crash Data ◽

Input Parameters

In this study, we selected four real-world rear-end crash scenarios with different crash characteristics. The vehicles involved in those crashes were not equipped with any crash avoidance systems. We then used the accident reconstruction method to build those crash scenarios in PC-Crash software. Then, different FCW/AEB safety algorithms have been defined for a subject vehicle model in each crash scenario and each scenario was simulated for a set of input parameters such as vehicle speed, brake intensity, and driver reaction time. The range and distribution of input parameters were extracted from the related field crash data and available literature. A total number of 16000 simulations have been conducted which produced input-output datasets for further investigations. Finally, the effects of input parameters on simulation outcomes including crash occurrence, AEB activation, injury risk, and vehicle damage have been quantified using the Boruta algorithm. The results indicated that the overall effectiveness of the AEB system was a 57% reduction of rear-end crashes, a 52% reduction of injury severity (striking vehicle’s passengers), and a 47% reduction of damages for striking vehicles. The results also showed that the available AEB algorithms were more effective for the average speed equal to or less than 80 kmph. The speed of the subject vehicle, type of AEB algorithm, sensor detection range, and driver reaction time were the most important parameters on crash outcomes. In addition, the results indicated that the performance of FCW had a direct impact on the effectiveness of the AEB system for the integrated FCW + AEB system.

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