Accurate on-board occupant injury risk prediction of motor vehicle crashes (MVCs) can decrease fatality rates by providing critical information timely and improving injury severity triage rates.The present implemented prediction algorithms in vehicle safety systems are probabilistic and rely on multi-variate logistic regression of real-world vehicle collision databases.As a result,they do not utilize important vehicle and occupant features and tend to overgeneralize the solution space.This study presents a framework to address these problems with deterministic and computationally efficient lumped parameter model simulations driven by a database of vehicle crash tests.A 648-case mixed database was generated with finite element and multi-body models and validated under the principal directions of impact with experimental results for a single vehicle body type.Using the finite element database,we developed lumped parameter models for four principal modes of impact (i.e.,frontal,rear,near side and far side) with parameters identified via genetic algorithm optimization.To obtain confidence bounds for the injury risk prediction,the parameter uncertainty and model adequacy were evaluated with arbitrary and bootstrapped polynomial chaos expansion.The developed algorithm was able to achieve over triage rates of 17.1％ ± 8.5％,whilst keeping the under triage rates below 8％ on a finite dement-multi body model database of a single vehicle body type.This study demonstrated the feasibility and importance of using low-complexity deterministic models with uncertainty quantification in enhanced occupant injury risk prediction.Further research is required to evaluate the effectiveness of this framework under a wide range of vehicle types.With the flexibility of parameter adjustment and high computational efficiency,the present framework is generic in nature so as to maximize future applicability in improved on-board triage decision making in active safety systems.