Using Hidden Markov Models in Vehicular Crash Detection

This paper presents a system for automotive crash detection based on hidden Markov models (HMMs). The crash pulse library used for training comprises a number of head-on and oblique angular crash events involving rigid and offset deformable barriers. Stochastic distribution characteristics of crash signals are validated to ensure conformity with the modeling assumptions. This step is achieved by analyzing the quantile–quantile (Q–Q) plot of actual pulses against the assumed bivariate Gaussian distribution. HMM parameters are next induced by utilizing the expectation–maximization (EM) procedure. The search for an optimal crash pulse model proceeds using the “leave-one-out” technique with the exploration encompassing both fully connected and left–right HMM topologies. The optimal crash pulse architecture is identified as a seven-state left–right HMM with its parameters computed using real and computer-aided engineering (CAE)-generated data. The system described in the paper has the following advantages. First, it is fast and can accurately detect crashes within 6 ms. Second, its implementation is simple and uses only two sensors, which makes it less vulnerable to failures, considering the overall simplicity of interconnects. Finally, it represents a general and modularized algorithm that can be adapted to any vehicle line and readily extended to use additional sensors.