Physical validation still consumes a large share of automotive R&D spend, and crash test is among the most repetitive and expensive parts of it. All the while, automotive teams sit on decades of crash data, and most of it is never used twice.
BMW Group proves that product development timelines can be dramatically cut by using engineering data to eliminate repetitive, time-intensive testing. Rather than repeat that cycle, BMW's R&D team built self-learning models from the crash data they already had, accurately predicting tibia index forces across a range of crash types with no physical crash required.
Download this white paper to learn more about how they did it, why crash test is an ideal AI use case, and where else in product development the same approach applies.
