As technologies for injury prevention and crash avoidance both contribute to injury reduction in car crashes, tools predicting the combined effect of all safety features are needed. This study aims at establishing a computer simulation methodology including two important elements for assessing this combined effect. The first element describes the states of the involved vehicles or objects at crash initiation regarding positions, orientations and velocities as parameters used for crash evaluation. The second element focuses on the car occupant, enabling computationally efficient prediction of occupant position transfer during pre-crash maneuvers. An extended aim is to demonstrate how data flows between these elements in an example case study.
Real-world data from the Volvo Cars traffic accident database (VCTAD) was used as the basis for pre-crash simulations involving two cars, with and without a conceptual autonomous emergency braking (AEB) function. For cases in which the crash was not avoided by the AEB function, the crash configuration was identified. A simplified occupant kinematics model (SOCKIMO) was developed and applied to these remaining crashes, supporting the selection of crash situations to be analyzed in detail. The SAFER human body model (HBM) was used for simulation of the occupant response, providing information on pre-crash kinematics as well as the occupant crash response.
As a result, a novel crash configuration definition for estimating the consequences of car crashes based on preceding events was established. The Volvo parametric crash configuration (VPARCC) definition can be used as a link between pre-crash and crash simulation tools as well as for illustrating sets of real-world accident data and how these change based on maneuvers preceding a crash. SOCKIMO results demonstrated occupant kinematics similar to those of volunteers, and the subsequent simulations using the SAFER HBM showed considerable changes in occupant crash response based on pre-crash vehicle kinematics.
The VPARCC definition can also be applied to collision objects such as trucks or vulnerable road users. The developed SOCKIMO can be used to filter out cases from large crash data sets to be further analyzed with detailed models such as finite element active HBMs. By applying the more detailed HBM, the effects of avoidance maneuvers on occupant kinematics relevant for injury prediction can be evaluated. This approach would not be possible using simplified occupant models only (due to the lack of details) or by using detailed models only (due to the large simulation effort).
The presented methodology for estimating combined safety performance can be used for transferring output from pre-crash simulations to input for crash simulations. The feasibility of combining the individual elements of this methodology was demonstrated in an example case where autonomous emergency braking led to a large change in the crash configuration and was predicted to introduce substantial occupant pre-crash excursion. In this example case, it was shown that the present A-HBM tool is able to cover the complete sequence from pre-crash maneuvers to crash in one single simulation.