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Advanced driver assistance systems improve driving safety and comfort by applying onboard sensors to collect environmental data, analyze environmental data and decision making. Therefore, advanced driver assistance systems have high requirements for distance perception of the environment. Perceptual sensors commonly used in traditional solutions include stereo vision sensors and the Light Detection and Ranging (LiDAR) sensors. This paper proposes a multi-sensing sensor fusion method for disparity estimation, which combines the perceptual data density characteristics of stereo vision sensors and the measurement accuracy characteristics of LiDAR sensors. The method enhances the sensing accuracy by ensuring high-density sense, which is suitable for distance sensing tasks in complex environments. This paper demonstrates with experimental results on real data that our proposed disparity estimation method performs well and is robust in different scenarios.

This paper presents a new system to estimate the head pose of human in interactive indoor environment that has dynamic illumination change and large working space. The main idea of this system is to suggest a new morphological feature for estimating head angle from stereo disparity map. When a disparity map is obtained from stereo camera, the matching confidence value can be derived by measurements of correlation of the stereo images. Applying a threshold to the confidence value, we also obtain the specific morphology of the disparity map. Therefore, we can obtain the morphological shape of disparity map. Through the analysis of this morphological property, the head pose can be estimated. It is simple and fast algorithm in comparison with other algorithm which apply facial template, 2D, 3D models and optical flow method. Our system can automatically segment and estimate head pose in a wide range of head motion without manual initialization like other optical flow system. As the result of experiments, we obtained the reliable head orientation data under the real-time performance.