  At present, a major challenge for the application of automatic driving
technology is the accurate prediction of vehicle trajectory. With the vigorous
development of computer technology and the emergence of convolution depth
neural network, the accuracy of prediction results has been improved. But, the
depth, width of the network and image resolution are still important reasons
that restrict the accuracy of the model and the prediction results. The main
innovation of this paper is the combination of RESNET network and efficient net
network, which not only greatly increases the network depth, but also
comprehensively changes the choice of network width and image resolution, so as
to make the model performance better, but also save computing resources as much
as possible. The experimental results also show that our proposed model obtains
the optimal prediction results. Specifically, the loss value of our method is
separately 4 less and 2.1 less than that of resnet and efficientnet method.
