A deep neural network inverse solution to recover pre-crash impact data of car collisions*

In this work, we have successfully developed a data-driven artificial intelligence (AI) inverse problem solution for traffic collision reconstruction. In specific, we have developed and implemented a machine learning computational algorithm and built a deep neural network to determine and identify the initial impact conditions of car crash based on its final material damage state and permanently deformed structure configuration (wreckage). In this work, we have demonstrated that the developed machine learning algorithm as an inverse problem solver can accurately identify initial collision conditions in an inverse manner, which are practically unique if we use permanent plastic deformation as the forensic data signatures. In other words, we think that the massive plastic energy dissipation process and the related big data will make final structure damage state insensitive to the initial car collision conditions. Thus, it provides an inverse solution for car crash forensic analysis by reconstructing the initial failure load parameters and conditions based on the permanent plastic deformation distribution of cars. This approach has general significance in solving the inverse problem for engineering failure analysis and vehicle crashworthiness analysis, which provides a key contribution for the unmanned autonomous vehicle and the related technology.

Automated summary

The study successfully developed a data-driven AI solution for traffic collision reconstruction using a machine learning computational algorithm and deep neural network. By using permanent plastic deformation as forensic data signatures, the machine learning algorithm accurately identified initial collision conditions.