Improve the LSTM Trajectory Prediction Accuracy through an Attention Mechanism

doi:10.1109/ITEC53557.2022.9813863

	Improve the LSTM Trajectory Prediction Accuracy through an Attention Mechanism
	Zhang, Tong; Wang, Zhiwen
	2022
会议日期	JUN 15-17, 2022
会议地点	Anaheim, CA
DOI	10.1109/ITEC53557.2022.9813863
页码	190-195
英文摘要	Although the long-short term memory (LSTM) network has been widely adopted to predict the vehicle trajectory, the iterative nature of LSTM introduces the accumulative errors. The accumulative errors result in a gradual decrease in the accuracy of trajectory prediction over time. Therefore, how to reduce the accumulative errors of the LSTM is a very critical issue. To solve this problem, we introduce a two-stage attention mechanism with the LSTM Encoder-Decoder model, which uses the spatial attention mechanism and the output attention mechanism to weight the input hidden layer features and the output prediction of the decoder. In this way, the accuracy of trajectory prediction is improved. The cumulative error of the predicted trajectory is significantly reduced. The proposed method is validated on US-101 and I-80 datasets from NGSIM. The simulation results show that the test dataset's average error at one second and five seconds is reduced from 0.7676 meters and 7.7168 meters to 0.4601 meters and 4.2184 meters, respectively. The average prediction error is reduced by 45.33%.
源文献作者	IEEE,AIAA,IEEE Power Elect Soc,IEEE Ind Applicat Soc,IEEE Power & Energy Soc,IEEE Transportat Electrificat Commun
会议录	2022 IEEE/AIAA TRANSPORTATION ELECTRIFICATION CONFERENCE AND ELECTRIC AIRCRAFT TECHNOLOGIES SYMPOSIUM (ITEC+EATS 2022)
会议录出版者	IEEE
语种	英语
WOS研究方向	Engineering ; Transportation
WOS记录号	WOS:000848063600033
内容类型	会议论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/159871]
专题	电气工程与信息工程学院
作者单位	Lanzhou Univ Technol, Lanzhou, Gansu, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Tong,Wang, Zhiwen. Improve the LSTM Trajectory Prediction Accuracy through an Attention Mechanism[C]. 见:. Anaheim, CA. JUN 15-17, 2022.