Image understanding based on edge histogram method for rear-end collision avoidance system

To avoid rear-end collision with the preceding vehicles on the road, we need the information of collision potentiality. It is decided primarily by the relative locations and velocities between vehicles and their absolute locations on the road lanes. We classify the recognition objects in the road scene into road lanes and vehicles for the goal of collision avoidance. We propose the consistent road scene recognition method using edge histogram with model based vision. The edge histogram can detect line elements of the objects stably with low calculation cost. If the suitable region of interests for each objects in the model are established and their projected edge histograms are observed in time series order, we can derive each objects from the model. Furthermore, we apply Kalman filter to predict the object locations for time series detection. From the recognition results, we can calculate the collision time. It is one of the measures of collision potentiality