Research on occupant injury mitigation strategy for vehicle frontal collision based on integrated active-passive...

Integrated active-passive safety plays an important role in improving vehicle crash safety, especially in hazardous scenarios where collision avoidance may become dynamically infeasible. Conventional active-safety strategies mainly focus on collision avoidance and may not fully account for occupant-injury outcomes once an impact becomes unavoidable. To address this issue, this study proposes a stability-constrained injury-aware Model Predictive Path Integral (SCI-MPPI) framework for integrated active-passive safety decision-making. A driving safety domain is constructed based on nonlinear vehicle dynamics, tire-force saturation, and braking-steering stability boundaries, and is embedded into trajectory planning as a dynamic feasibility constraint. High-fidelity crash simulations and THOR anthropomorphic test device (ATD)-based occupant-injury responses are used offline to train a physically partitioned radial basis function (RBF) surrogate model for rapid online Weighted Injury Criterion (WIC)-based injury prediction in frontal asymmetric collisions. SCI-MPPI then optimizes sampled trajectories while considering road boundaries, obstacle avoidance, vehicle-footprint constraints, stability limits, and injury-related costs. Simulation results show that SCI-MPPI generates collision-free trajectories and smooth dynamic responses in avoidable scenarios. Comparative evaluations against baseline methods indicate that SCI-MPPI shows a favorable balance between nonlinear trajectory-optimization performance and computational efficiency. In unavoidable-collision cases, SCI-MPPI achieves a mean per-case first-impact WIC reduction of 18.56% while keeping injury-oriented maneuvers within the driving safety domain. These results indicate that SCI-MPPI supports the coordination of active obstacle avoidance and first-impact injury mitigation within stability-constrained maneuvering limits, providing guidance for improving collision safety in future intelligent-vehicle systems.