Hierarchical object recognition algorithm based on Kalman filter for adaptive cruise control system using scanning laser

Not merely running at a designated constant speed as classical cruise control, the adaptive cruise control (ACC) maintains a safe headway distance when the front is blocked by other vehicles. One of the most essential part of ACC system is the range sensor which can measure the position and speed of all objects in front continuously, ignore all irrelevant objects, distinguish vehicles in different lanes and lock on to the closest vehicle in the same lane. In this paper, the hierarchical object recognition algorithm (HORA) is proposed to process raw scanning laser data and acquire valid distance to target vehicle. HORA contains two principal concepts. First, the concept of life quantifies the reliability of range data to filter off the spurious detection and preserve the missing target position. Second, the concept of conformation checks the mobility of each obstacle and tracks the position shift. To estimate and predict the vehicle position a Kalman filter is used. Repeatedly updated covariance matrices determine the bound of valid data. The algorithm is emulated on computer and tested on-line with our ACC vehicle