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A traffic accident prediction method using a priori knowledge based on accident data is proposed for safe driving support. Implementation is achieved by an algorithm using particle filtering and fuzzy inference to estimate accident risk factors. With this method, the distance between the host vehicle and a vehicle ahead and their relative velocity and relative acceleration are obtained from the results of particle filtering of driving data and are used as attributes to build the relative driving state space. The attributes are evaluated as likelihoods and then consolidated as a risk level using fuzzy inference. Experimental validation was done using videos of general driving situations obtained with an on-vehicle CCD camera and one simulated accident situation created based on the video data. The results show that high risk levels were calculated with the proposed method in the early stages of the accident situations.

Proposed Method for Estimating Traffic Accident Risk Factors Based on Object Tracking and Behavior Prediction Using Particle Filtering