An Optimization and Cluster based Approach to Lookup Tables in Design of Adaptive Restraint Systems

Abstract

The timely deployment of vehicle restraint systems is crucial in mitigating the impact of collisions and protecting occupants in the affected vehicles. The level of protection can be further enhanced with the use of adaptive restraint systems, by adjusting the force and timing of seatbelts and airbags based on factors such as vehicle speed, occupant size, and seating position. Virtual testing methods can help identify areas of improvement of adaptive systems by evaluating its performance across a range of crash scenarios. While finite element simulation models and data-based surrogate models have been used in literature for restraint system development, data structures such as lookup tables containing restraint settings offer potential to accelerate the design and deployment of adaptive restraint systems. To implement this, firstly, metamodels using Gaussian process regression were developed to predict specific occupant kinematics and injury risks in frontal collisions between vehicles with varying crash configurations. Furthermore, the optimal restraint settings for each crash configuration was identified using a genetic algorithm, taking into account the injury risk predictions from the metamodels. The frontal collisions were categorized based on crash pulse intensities and then represented in a static lookup table for quick retrieval of restraint settings based on a crash configuration. The restraint settings obtained from the optimization were validated with real-world equivalent settings, exhibiting lower injury risks in low and medium-speed crashes for the concerned vehicle model. Overall, the research presented demonstrates the application of lookup tables as a tool for development and operation of adaptive restraint systems. Furthermore, the combination of look-up tables with machine learning techniques could be scaled to suit the complexity of the engineering problem