In autonomous driving, using a variety of sensors to recognize preceding vehicles at middle and long distances is helpful for improving driving performance and developing various functions. However, if only LiDAR or cameras are used in the recognition stage, it is difficult to obtain the necessary data due to the limitations of each sensor. In this paper, we proposed a method of converting the vision-tracked data into bird’s eye-view (BEV) coordinates using an equation that projects LiDAR points onto an image and a method of fusion between LiDAR and vision-tracked data. Thus, the proposed method was effective through the results of detecting the closest in-path vehicle (CIPV) in various situations. In addition, even when experimenting with the EuroNCAP autonomous emergency braking (AEB) test protocol using the result of fusion, AEB performance was improved through improved cognitive performance than when using only LiDAR. In the experimental results, the performance of the proposed method was proven through actual vehicle tests in various scenarios. Consequently, it was convincing that the proposed sensor fusion method significantly improved the adaptive cruise control (ACC) function in autonomous maneuvering. We expect that this improvement in perception performance will contribute to improving the overall stability of ACC.

Among the medical parameters used for infants, the grasping force is particularly important because it indicates their musculoskeletal and neurological development. Although several grasping force measuring devices have been developed for infants, their accuracy and reliability are limited owing to their direction-dependent sensing mechanisms. It is challenging to calculate the direction and area of the ambiguous forces applied by infants, and pediatricians cannot control the grasping method used by them. In this study, a direction-independent grasping force measuring device is proposed that features a high resolution (0.1 kPa), cyclic stability (20 000 cycles), and linear sensitivity (21.73 μV kPa −1 ), and high accuracy and reliability. The grasping forces (average, minimum, and maximum) of the left (normal state) and right (injection needle inserted: uncomfortable state) hands of a 1-day old infant can be successfully analyzed using the proposed device. It can be used to obtain the standard grasping force data of infants, which can contribute toward understanding the correlation between the grasping force and neurological diseases. The proposed device can be used to quantitatively measure the grasping force of not only infants but also the elderly; therefore, additional studies may report that the grasping force can be a discriminable parameter for identifying neurological diseases.

In autonomous driving, using a variety of sensors to recognize preceding vehicles in middle and long distance is helpful for improving driving performance and developing various functions. However, if only LiDAR or camera is used in the recognition stage, it is difficult to obtain necessary data due to the limitations of each sensor. In this paper, we proposed a method of converting the tracking data of vision into bird's eye view (BEV) coordinates using an equation that projects LiDAR points onto an image, and a method of fusion between LiDAR and vision tracked data. Thus, the newly proposed method was effective through the results of detecting closest in-path vehicle (CIPV) in various situations. In addition, even when experimenting with the EuroNCAP autonomous emergency braking (AEB) test protocol using the result of fusion, AEB performance is improved through improved cognitive performance than when using only LiDAR. In experimental results, the performance of the proposed method was proved through actual vehicle tests in various scenarios. Consequently, it is convincing that the newly proposed sensor fusion method significantly improves the ACC function in autonomous maneuvering. We expect that this improvement in perception performance will contribute to improving the overall stability of ACC.