Object Vehicle Motion Prediction based on Dynamic Occupancy Grid Map Utilizing Cascaded Support Vector Machine

This paper presents a motion prediction scheme of object vehicles based on the dynamic occupancy grid map considering movement of the vehicles by applying a temporal flow and a cascaded algorithm for support vector machine (SVM). We divided occupancy grid map into two types of upper-level and lower level. The upper-level occupancy grid is used to predict motion that the object vehicle can move into the ego vehicle and the lower-level one is needed for decision using the SVM for sensor resolution. The presented algorithm was validated with a experimental data set and the overall accuracy of classification was obtained 90.42% from a confusion matrix.