Abstract
This paper studies whether low-grade inertial sensors can be adequate source of data for the accident characterization and the estimation of vehicle trajectory near crash. The paper presents outcomes of an experiment carried out in accredited safety performance assessment facility in which full-size passenger car was crashed and the recordings of different types of motion sensors were compared to investigate practical level of accuracy of consumer grade sensors versus reference equipment and cameras. Inertial navigation system was developed by combining motion sensors of different dynamic ranges to acquire and process vehicle crash data. Vehicle position was reconstructed in three-dimensional space using strap- down inertial mechanization. Difference between the com- puted trajectory and the ground-truth position acquired by cameras was on decimeter level within short time window of 750 ms. Experiment findings suggest that inertial sen- sors of this grade, despite significant stochastic variations and imperfections, can be valuable for estimation of veloc- ity vector change, crash severity, direction of impact force, and for estimation of vehicle trajectory in crash proximity.
Highlights
Modern vehicles are equipped with clusters of inertial sensors as parts of safety and stability mechanisms
This paper studies whether low-grade inertial sensors can be adequate source of data for the accident characterization and the estimation of vehicle trajectory near crash
It is valuable to study possibility to incorporate this class of sensors as parts of strap-down inertial navigation system (SDINS, [4]) that would be used for short-term vehicle trajectory estimation in crash
Summary
Modern vehicles are equipped with clusters of inertial sensors as parts of safety and stability mechanisms. SDINS combines accelerometers and rate gyro outputs to provide autonomous and precise navigation of an object in space Such reduced model SDINS could become tool for objective analysis of vehicle motion and elucidation of accident causes. When possible, this approach should be integrated with the Global Navigation Satellite System (GNSS) receiver and proper fusion algorithm to provide absolute positioning [5] and tracking of motion sensor error model during regular driving conditions. A common method for integration of the specific forces and angular rates which provides vehicle’s position in space is “inertial mechanization” It is used for short-term solving of navigation differential equations in frame of interest. Mathematical foundations and SDINS, as implemented, are given
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