To cope with the challenges of autonomous driving in complex road environments, the need for collaborative multi-tasking has been proposed. This research direction explores new solutions at the application level and has become a hot topic of great interest. In the field of natural language processing and recommendation algorithms, the use of multi-task learning networks has been proven to reduce time, computing power, and storage usage in various task coupling cases. Due to the characteristics of the multi-task learning network, it has also been applied to visual road feature extraction in recent years. This article proposes a multi-task road feature extraction network that combines group convolution with transformer and squeeze excitation attention mechanisms. The network can simultaneously perform drivable area segmentation, lane line segmentation, and traffic object detection tasks. The experimental results of the BDD-100K dataset show that the proposed method performs well for different tasks and has a higher accuracy than similar algorithms. The proposed method provides new ideas and methods for the autonomous road perception of vehicles and the generation of highly accurate maps in visual-based autonomous driving processes.

The security challenges faced by smart cities are attracting more attention from more people. Criminal activities and disasters can have a significant impact on the stability of a city, resulting in a loss of safety and property for its residents. Therefore, predicting the occurrence of urban events in advance is of utmost importance. However, current methods fail to consider the impact of road information on the distribution of cases and the fusion of information at different scales. In order to solve the above problems, an urban spatiotemporal event prediction method based on a convolutional neural network (CNN) and road feature fusion network (FFN) named CNN-rFFN is proposed in this paper. The method is divided into two stages: The first stage constructs feature map and structure of CNN then selects the optimal feature map and number of CNN layers. The second stage extracts urban road network information using multiscale convolution and incorporates the extracted road network feature information into the CNN. Some comparison experiments are conducted on the 2018–2019 urban patrol events dataset in Zhengzhou City, China. The CNN-rFFN method has an R² value of 0.9430, which is higher than the CNN, CNN-LSTM, Dilated-CNN, ResNet, and ST-ResNet algorithms. The experimental results demonstrate that the CNN-rFFN method has better performance than other methods.