Object Vehicle Motion Prediction based on Dynamic Occupancy Grid Map Utilizing Cascaded Support Vector Machine
- Resource Type
- Conference
- Authors
- Dae Jung Kim; Seung-Hi Lee; Chung Choo Chung
- Source
- 제어로봇시스템학회 국제학술대회 논문집. 2019-10 2019(10):496-500
- Subject
- Occupancy grid
Object vehicle motion prediction
Support vector machine
Advanced driver assistance system
Adaptive cruise control
- Language
- Korean
- ISSN
- 2005-4750
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.