A Novel Generation-Adversarial-Network-Based Vehicle Trajectory Prediction Method for Intelligent Vehicular Networks

Zhao, Liang; Liu, Yufei; Al-Dubai, Ahmed Y.; Zomaya, Albert Y.; Min, Geyong; Hawbani, Ammar

Abstract

Prediction of the future location of vehicles and other mobile targets is instrumental in intelligent transportation system applications. In fact, networking schemes and protocols based on machine learning can benefit from the results of such accurate trajectory predictions. This is because routing decisions always need to be made for the future scenario due to the inevitable latency caused by processing and propagation of the routing request and response. Thus, to predict the highprecision trajectory beyond the state-of-the-art, we propose a Generative Adversarial Network-based VEhicle trajEctory Prediction method, GAN-VEEP, for urban roads. The proposed method consists of three components, 1) vehicle coordinate transformation for data set preparation, 2) neural network prediction model trained by GAN, and 3) vehicle turning model to adjust the prediction process. The vehicle coordinate transformation model is introduced to deal with the complex spatial dependence in the urban road topology. Then, the neural network prediction model learns from the behavior of vehicle drivers. Finally, the vehicle turning model can refine the driving path based on the driver’s psychology. Compared with its counterparts, the experimental results show that GAN-VEEP exhibits higher effectiveness in terms of the Average Accuracy, Mean Absolute Error, and Root Mean Squared Error.

Citation

Zhao, L., Liu, Y., Al-Dubai, A. Y., Zomaya, A. Y., Min, G., & Hawbani, A. (2021). A Novel Generation-Adversarial-Network-Based Vehicle Trajectory Prediction Method for Intelligent Vehicular Networks. IEEE Internet of Things Journal, 8(3), 2066-2077. https://doi.org/10.1109/jiot.2020.3021141

• Journal Article Type: Article
• Acceptance Date: Aug 28, 2020
• Online Publication Date: Sep 2, 2020
• Publication Date: Feb 1, 2021
• Deposit Date: Aug 30, 2020
• Publicly Available Date: Sep 2, 2020
• Journal: IEEE Internet of Things Journal
• Electronic ISSN: 2327-4662
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 8
• Issue: 3
• Pages: 2066-2077
• DOI: https://doi.org/10.1109/jiot.2020.3021141
• Keywords: Signal Processing; Computer Networks and Communications; Hardware and Architecture; Information Systems; Computer Science Applications
• Public URL: http://researchrepository.napier.ac.uk/Output/2683697