Machine Learning-Enabled Optimization of Vehicle Restraint Systems – Demonstration in a Real-World Crash Scenario

Vehicle restraint systems, such as seat belts and airbags, play a crucial role in managing crash energy and protecting occupants during vehicle crashes. Designing an effective restraint system for a diverse population is a complex task. This study demonstrates the practical implementation of state-of-the-art Machine Learning (ML) techniques to optimize vehicle restraint systems and improve occupant safety. An ML-based surrogate model was developed using a small Design of Experiments (DOE) dataset from finite element human body model simulations and was employed to optimize a vehicle restraint system. The performance of the ML-optimized restraint system was compared to the baseline design in a real-world crash scenario. The ML-based optimization showed potential for further enhancement in occupant safety over the baseline design, specifically for small-female occupant. The optimized design reduced the joint injury probability for small female passenger from 0.274 to 0.224 in the US NCAP frontal 56.8 km/h rigid barrier impact condition. In a reconstructed field case, the optimized design showed potential reduction in chest injury risk probability from 49% to 35% for an older female passenger. This study demonstrates the efficacy and generalizability of ML-based optimization in vehicle restraint system design, highlighting its immense potential to enhance occupant safety if there is an adaptive restraint in the vehicle.