Trajectory Prediction with Correction Mechanism for Connected and Autonomous Vehicles

Lv, Pin; Liu, Hongbiao; Xu, Jia; Li, Taoshen

doi:10.3390/electronics11142149

Open AccessArticle

Trajectory Prediction with Correction Mechanism for Connected and Autonomous Vehicles

¹

School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China

²

Guangxi Key Laboratory of Multimedia Communications and Network Technology, Nanning 530004, China

³

China-ASEAN International Join Laboratory of Integrated Transport, Nanning University, Nanning 541699, China

^*

Author to whom correspondence should be addressed.

Electronics 2022, 11(14), 2149; https://doi.org/10.3390/electronics11142149

Submission received: 12 May 2022 / Revised: 3 July 2022 / Accepted: 5 July 2022 / Published: 9 July 2022

(This article belongs to the Special Issue Emerging Communication, Computing and Control Technologies for Internet of Vehicles)

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Abstract

Trajectory prediction of surrounding vehicles is a critical task for connected and autonomous vehicles (CAVs), helping them to realize potential dangers in the traffic environment and make the most appropriate decisions. In a practical traffic environment, vehicles may affect each other, and the trajectories may have multi-modality and uncertainty, which makes accurate trajectory prediction a challenge. In this paper, we propose an interactive network model based on long short-term memory (LSTM) and a convolutional neural network (CNN) with a trajectory correction mechanism, using our newly proposed probability forcing method. The model learns the interactions between vehicles and corrects their trajectories during the prediction process. The output is a multimodal distribution of predicted trajectories. In the experimental evaluation of the US-101 and I-80 Next-Generation Simulation (NGSIM) real highway datasets, our proposed method outperforms other contrast methods.

Keywords: trajectory prediction; connected and autonomous vehicles; interactive network; correction mechanism; probability forcing; multimodal distribution

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MDPI and ACS Style

Lv, P.; Liu, H.; Xu, J.; Li, T. Trajectory Prediction with Correction Mechanism for Connected and Autonomous Vehicles. Electronics 2022, 11, 2149. https://doi.org/10.3390/electronics11142149

AMA Style

Lv P, Liu H, Xu J, Li T. Trajectory Prediction with Correction Mechanism for Connected and Autonomous Vehicles. Electronics. 2022; 11(14):2149. https://doi.org/10.3390/electronics11142149

Chicago/Turabian Style

Lv, Pin, Hongbiao Liu, Jia Xu, and Taoshen Li. 2022. "Trajectory Prediction with Correction Mechanism for Connected and Autonomous Vehicles" Electronics 11, no. 14: 2149. https://doi.org/10.3390/electronics11142149

APA Style

Lv, P., Liu, H., Xu, J., & Li, T. (2022). Trajectory Prediction with Correction Mechanism for Connected and Autonomous Vehicles. Electronics, 11(14), 2149. https://doi.org/10.3390/electronics11142149

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Trajectory Prediction with Correction Mechanism for Connected and Autonomous Vehicles

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