Application of Multi-Sensor Fusion in Evaluation of Automotive Passive Safety System
Miao Hua, Yanyu Yang · 2025
The system adopts the EKF algorithm to fuse the measurement data from multiple sensors such as accelerometers, gyroscopes and pressure sensors, and accurately estimates the key parameters in the collision process, including collision velocity, acceleration and force. This paper constructs a high-fidelity simulation platform to simulate various collision scenarios (such as frontal collision, side collision and multi-vehicle rear-end collision) to evaluate the response performance of passive safety systems under different conditions. The simulation results show that compared with the single sensor method, the proposed multi-sensor fusion algorithm reduces the error in key parameter estimation by about 30%. In addition, the data processing delay is reduced from the original 50ms to 30ms, which significantly improves the real-time response capability. The fusion algorithm has excellent stability in complex collision environments, especially when the sensor fails partially, it can still maintain high accuracy. This method cannot only effectively improve the evaluation accuracy, but also meet the requirements of automotive passive safety systems for rapid response and high reliability in emergency situations.