Vehicle local path planning and time consistency of unmanned driving system based on convolutional neural network

The path planning system is an important part of unmanned vehicles, and the development of path planning technology will surely promote the rapid development of unmanned vehicle technology. In order to prevent the node from continuously monitoring its state, a self-triggering control strategy is proposed. Before the trigger moment, the node does not need to monitor its state. Moreover, considering the unpredictable problem of the node state, a control strategy triggered by the observed event is proposed, that is, only the output state information is used to determine the trigger time. In addition, this paper analyzes and models the two major factors that affect the local planning results, the environment and the vehicle, and uses the path smoothing and optimization method based on B-spline curve and the path optimization method based on the steering controller. Finally, this paper designs experiments to analyze the vehicle local path planning method and time consistency of the unmanned driving system. From the experimental results, it can be seen that the unmanned driving system constructed in this paper has a certain effect.