Vehicle Trajectory Prediction Considering Multi-Feature Independent Encoding

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Vehicle Trajectory Prediction Considering Multi-Feature Independent Encoding 20 Pages Posted: 13 Jun 2022 See all articles by Xin XuXin XuShanghai University of Engineering ScienceXiaolan WangShanghai University of Engineering ScienceYansong WangShanghai University of Engineering ScienceJiaqi CaoShanghai University of Engineering Science Abstract Today, self-driving cars are already on the roads of urban life. However, driving safety remains a huge challenge. Trajectory prediction of traffic targets is one of the important tasks of autonomous driving environment perception system, and its output trajectory can provide necessary information for decision control and path planning. This paper proposes a novel scheme that considers multi-feature independent encoding trajectory prediction (MFIE). MFIE is an independent coded trajectory prediction algorithm that consists of space-time interaction module and trajectory prediction module and considers speed characteristics and road characteristics. In the spatiotemporal interaction module, an undirected and weightless static traffic graph is used to represent the interaction between vehicles, and multiple graph convolution blocks are used to perform data mining on the historical information of target vehicles, capture temporal features, and process spatial interaction features. In the trajectory prediction module, three long short-term memory (LSTM) encoders were used to encode the trajectory feature, motion feature and road constraint feature independently. The three hidden features were splicing into a tensor, and the LSTM decoder was used to predict the future trajectory. On data sets such as Apollo and NGSIM, the method in this paper has better prediction accuracy than traditional model-driven and data-driven methods. It can provide a basis for vehicle path planning on highways and urban roads, and is of great significance to the safety of autonomous driving. Keywords: Self-driving cars, Trajectory prediction, Long short-term memory, Traffic graph, Multi-feature independent encoding Suggested Citation: Suggested Citation Xu, Xin and Wang, Xiaolan and Wang, Yansong and Cao, Jiaqi, Vehicle Trajectory Prediction Considering Multi-Feature Independent Encoding. Available at SSRN: https://ssrn.com/abstract=4135360 Xin Xu Shanghai University of Engineering Science ( email ) shanghai, 201620China Xiaolan Wang (Contact Author) Shanghai University of Engineering Science ( email ) shanghai, 201620China Yansong Wang Shanghai University of Engineering Science ( email ) shanghai, 201620China Jiaqi Cao Shanghai University of Engineering Science ( email ) shanghai, 201620China Download This Paper Open PDF in Browser Do you have a job opening that you would like to promote on SSRN? Place Job Opening Paper statistics Downloads 0 Abstract Views 0 PlumX Metrics Feedback Feedback to SSRN Feedback (required) Email (required) Submit If you need immediate assistance, call 877-SSRNHelp (877 777 6435) in the United States, or +1 212 448 2500 outside of the United States, 8:30AM to 6:00PM U.S. Eastern, Monday - Friday. Submit a Paper Section 508 Text Only Pages SSRN Quick Links SSRN Solutions Research Paper Series Conference Papers Partners in Publishing Jobs & Announcements Newsletter Sign Up SSRN Rankings Top Papers Top Authors Top Organizations About SSRN SSRN Objectives Network Directors Presidential Letter Announcements Contact us FAQs Copyright Terms and Conditions Privacy Policy We use cookies to help provide and enhance our service and tailor content. To learn more, visit Cookie Settings. This page was processed by aws-apollo4 in 0.312 seconds