Preceding vehicle and road lanes recognition methods for RCAS using vision system

This paper describes the preceding vehicle and road lanes recognition methods for the rear-end collision avoidance system (RCAS) which we are developing. These methods are using an edge histogram method based on the model based vision concept. The edge histogram method can detect line elements of the objects stably with low calculation cost. When the region of interests for the preceding vehicle and road lanes in the image are established and their projected edge histograms are observed in time series order, we can recognize them. Furthermore, we apply Kalman filter to their motions and predict their locations for time series detection. Using this stable recognition, we derive a collision time to control the on-board brake system. We show the performance of these methods by experimental results.