A lightweight intelligent vehicle system was developed to realize autonomous driving, face recognition, face anti-spoofing, remote control, infrared obstacle avoidance and other functions to improve the security of contactless delivery. In this system, BCM2711 was used as kernel control chip, and it was equipped with deep network learning models such as LaneNet, ResNet and LSTM. It had been proved that this system could realize the above functions and achieve real-time effects, thus gaining great economic value and market space in contactless delivery service.

An improved NS traffic flow model was built in this paper to simulate two safety factors of vehicle-pedestrian avoidance and vehicle-vehicle avoidance under different weather conditions. Then the regulations of changes on lanes and vehicle speed under two-lane conditions were optimized as well as the improved NS model based on cellular automata. Results showed that the improved NS model can predict road conditions effectively, thereby improving the safety of roads.

Summary According to statistics, kidney cancer is one of the most deadly cancer. An early and accurate diagnosis can significantly increase the cure rate. Accurate segmentation of kidney tumors in CT images plays an important role in kidney cancer diagnosis. However, it is a challenging task due to many different aspects, such as low contrast, irregular motion, diverse shapes, and sizes. For solving this issue, we proposed a SE‐R esNeXT U ‐Net (SERU) model in this study, which takes the advantages of SE‐Net, ResNeXT and U‐Net. Besides, we implement our model in a coarse‐to‐fine manner to utilize the information of context and key slices from the left and right kidney. We train and test our method on the KiTS19 Challenge. Experimental results demonstrate that our model can achieve promising results.