The Vehicle Trajectory Prediction Based on ResNet and EfficientNet Model

TL;DR

This paper introduces a novel vehicle trajectory prediction model combining ResNet and EfficientNet, improving accuracy and resource efficiency through optimized network depth, width, and image resolution.

Contribution

The paper's main innovation is integrating ResNet and EfficientNet to enhance prediction accuracy while reducing computational resource requirements.

Findings

The proposed model achieves lower loss values than ResNet and EfficientNet alone.

Experimental results demonstrate improved prediction accuracy.

The model balances performance and computational efficiency.

Abstract

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.