It is well known that laser scanner has better accuracy than stereo vision in detecting the distance and velocity of the obstacles, whereas stereo vision can distinguish the objects better than the laser scanner. These advantages of each sensor can be maximized by sensor fusion approach so that the obstacles in front can be detected accurately. In this paper, high-level sensor fusion for the laser scanner and stereo vision is developed for object matching between the sensors. Time synchronization, object age, and reordering algorithms are designed for robust tracking of the objects. A time-delay update algorithm is also developed to determine the process time delay of the laser scanner. The expanded laser scanner data at every 1 ms is predicted by Kalman filter and is matched with the stereo vision data at every 66 ms. A cost function is formulated to describe the object matching similarity between the sensors, and the best matching candidate is selected for theminimumcost function. The proposedmatching algorithms are verified experimentally in field tests of various maneuvering cases.