An Efficientnet Based Method for Autonomous Vehicles Trajectory Prediction

Tang H, Wang Y, Yuan W, Sun Y (2021)


Publication Type: Conference contribution

Publication year: 2021

Publisher: Institute of Electrical and Electronics Engineers Inc.

Pages Range: 18-21

Conference Proceedings Title: 2021 IEEE International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology, CEI 2021

Event location: Fuzhou, CHN

ISBN: 9780738146492

DOI: 10.1109/CEI52496.2021.9574480

Abstract

Autonomous driving refers to the use of computer, network, control, communication and other technologies to achieve real-time control of the vehicle. This technology needs to predict the future trajectory of surrounding moving targets as accurately as possible, and adjust its own driving path as needed. The key to this technology is how to predict the trajectory of the vehicle as accurately as possible based on the data of the vehicle itself, nearby moving objects, and traffic lights. Based on a large amount of processed road data, this paper uses the EfficientNet model to predict the trajectory of autonomous driving. We mainly use the EfficientNet network model to train and test the data set, and use the loss value to adjust the model to improve the accuracy of model prediction. Through experiments, we finally found that the EfficientNet model has better prediction performance than the two models VGG16 and ResNet34.

Involved external institutions

How to cite

APA:

Tang, H., Wang, Y., Yuan, W., & Sun, Y. (2021). An Efficientnet Based Method for Autonomous Vehicles Trajectory Prediction. In 2021 IEEE International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology, CEI 2021 (pp. 18-21). Fuzhou, CHN: Institute of Electrical and Electronics Engineers Inc..

MLA:

Tang, Haiyang, et al. "An Efficientnet Based Method for Autonomous Vehicles Trajectory Prediction." Proceedings of the 2021 IEEE International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology, CEI 2021, Fuzhou, CHN Institute of Electrical and Electronics Engineers Inc., 2021. 18-21.

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