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Faced with intricate traffic conditions, the single sensor has been unable to meet the safety requirements of Advanced Driver Assistance Systems (ADAS) and autonomous driving. In the field of multi-target tracking, the number of targets detected by vision sensor is sometimes less than the current tracks while the number of targets detected by millimeter wave radar is more than the current tracks. Hence, a multi-sensor information fusion algorithm is presented by utilizing advantage of both vision sensor and millimeter wave radar. The multi-sensor fusion algorithm is based on centralized fusion strategy that the fusion center takes a unified track management. At First, vision sensor and radar are used to detect the target and to measure the range and the azimuth angle of the target. Then, the detections data from vision sensor and radar is transferred to fusion center to join the multi-target tracking with the prediction of current tracks.

The conventional crash box with thin-walled column conceals some limitations on pedestrian protection and lightweight. The metallic NPR metamaterials designed in this study are based on re-entrant lattice structures. Re-entrant structures are known to be one main class of axenic structures that display negative Poisson’s ratio (NPR), which can be manufactured by 3D printing technology. This kind of metamaterial has good designability and can be used as the filling structure of the crash box to improve the crashworthiness of the car. This paper starts from the relations between geometric parameters of the metamaterial. Considering the deformation characteristics of the crash box, the structure were designed into some gradient types. The mechanical properties of different gradient structures under the same impact conditions were compared to find the proper gradient structures. Based on the studies, the gradient lattice structure is applied to the automobile crash box.