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trajectory prediction in pipeline form for intercepting hypersonic gliding vehicles based on LSTM

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Chinese Journal of Aeronautics 2023年第5期36卷 421-433页

作者： Lihan SUN Baoqing YANG Jie MA Control and Simulation Center Harbin Institute of TechnologyHarbin 150040China

The interception problem of Hypersonic Gliding Vehicles(HGVs)has been an important aspect of missile defense systems.In order to provide interceptors with accurate information of target trajectory,a model based on an improved Long Short-Time Memory(LSTM)network for trajectory prediction pipeline is proposed for the interception of a skip gliding hypersonic target.Firstly,for trajectory prediction required by intercepting guidance laws,the altitude,velocity and velocity direction of the target are formulated in the form of analytic functions,consisting of linear decay terms and amplitude decay sinusoidal terms.Then,the dynamic characteristics of the model parameters are analyzed,and the target trajectory prediction pipeline is proposed with the prediction error considered.Finally,an improved LSTM network is designed to estimate parameters in a dynamically-updated manner,and estimation results are used for the calculation of the final trajectory prediction pipeline.The proposed prediction algorithm provides information on the velocity vector for midcourse guidance with the effect of prediction errors on interception taken into account.Simulation is conducted and the results show the high accuracy of the algorithm in HGVs’trajectory prediction which is conducive to increasing the interception success rate.

关键词： Hypersonic glide vehicles Long-short time memory Near space Skip glide trajectory prediction

trajectory distributions:A new description of movement for trajectory prediction

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Computational Visual Media 2022年第2期8卷 213-224页

作者： Pei Lv Hui Wei Tianxin Gu Yuzhen Zhang Xiaoheng Jiang Bing Zhou Mingliang Xu School of Information Engineering Zhengzhou UniversityZhengzhou 450001China

trajectory prediction is a fundamental and challenging task for numerous applications,such as autonomous driving and intelligent robots.Current works typically treat pedestrian trajectories as a series of 2D point coordinates.However,in real scenarios,the trajectory often exhibits randomness,and has its own probability distribution.Inspired by this observation and other movement characteristics of pedestrians,we propose a simple and intuitive movement description called a trajectory distribution,which maps the coordinates of the pedestrian trajectory to a 2D Gaussian distribution in space.Based on this novel description,we develop a new trajectory prediction method,which we call the social probability method.The method combines trajectory distributions and powerful convolutional recurrent neural networks.Both the input and output of our method are trajectory distributions,which provide the recurrent neural network with sufficient spatial and random information about moving pedestrians.Furthermore,the social probability method extracts spatio-temporal features directly from the new movement description to generate robust and accurate predictions.Experiments on public benchmark datasets show the effectiveness of the proposed method.

关键词： trajectory prediction convolutional LSTM trajectory distributions social probabihty method

Interaction-Aware Cut-In trajectory prediction and Risk Assessment in Mixed Traffic

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IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1752-1762页

作者： Xianglei Zhu Wen Hu Zejian Deng Jinwei Zhang Fengqing Hu Rui Zhou Keqiu Li Fei-Yue Wang the College of Intelligence and Computing Tianjin UniversityTianjin 300350 the China Automotive Technology and Research Center Co.Ltd. Tianjin 300300China the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body College of Mechanical and Vehicle EngineeringHunan UniversityChangsha 410082China the Department of Mechanical and Mechatronics Engineering University of WaterlooWaterlooON N2L3G1Canada the School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China the Macao University of Science and Technology MacaoChina the Waytous Inc. Qingdao 266000China IEEE the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Accurately predicting the trajectories of surrounding vehicles and assessing the collision risks are essential to avoid side and rear-end collisions caused by cut-in.To improve the safety of autonomous vehicles in the mixed traffic,this study proposes a cut-in prediction and risk assessment method with considering the interactions of multiple traffic participants.The integration of the support vector machine and Gaussian mixture model(SVM-GMM)is developed to simultaneously predict cut-in behavior and trajectory.The dimension of the input features is reduced through Chebyshev fitting to improve the training efficiency as well as the online inference performance.Based on the predicted trajectory of the cut-in vehicle and the responsive actions of the autonomous vehicles,two risk measurements are introduced to formulate the comprehensive interaction risk through the combination of Sigmoid function and Softmax function.Finally,the comparative analysis is performed to validate the proposed method using the naturalistic driving data.The results show that the proposed method can predict the trajectory with higher precision and effectively evaluate the risk level of a cut-in maneuver compared to the methods without considering interaction.

关键词： Cut-in behavior interaction-aware mixed traffic risk assessment trajectory prediction

Lane-changing trajectory prediction based on multi-task learning

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Transportation Safety and Environment 2023年第4期5卷 67-77页

作者： Xianwei Meng Jinjun Tang Fang Yang Zhe Wang Smart Transport Key Laboratory of Hunan Province School of Transport and Transportation EngineeringCentral South UniversityChangsha 410075HunanChina

As a complex driving behaviour,lane-changing(LC)behaviour has a great influence on traffic flow.Improper lane-changing behaviour often leads to traffic accidents.Numerous studies are currently being conducted to predict lane-change trajectories to minimize dangers.However,most of their models focus on how to optimize input variables without considering the interaction between output variables.This study proposes an LC trajectory prediction model based on a multi-task deep learning framework to improve driving safety.Concretely,in this work,the coupling effect of lateral and longitudinal movement is considered in the L.C process.trajectory changes in two directions will be modelled separately,and the information interaction is completed under the multi-task learing framework.In addition,the trajectory fragents are clustered by the driving features,and trajectory type recognition is added to the trajectory prediction framework as an auxiliary task.Finally,the prediction process of lateral and longitudinal trajectory and LC style is completed by long short-term memory(LSTM).The model training and testing are conducted with the data collected by the driving simulator,and the proposed method expresses better performance in LC trjectory prediction compared with several traditional models.The results of this study can enhance the trajectory prediction accuracy of advanced driving assistance systems(ADASs)and reduce the traffic accidents caused by lane changes.

关键词： lane-changing behaviour trajectory prediction long short-term memory(LSTM)network multi-task learning trajectory clustering

trajectory prediction of cyclist based on dynamic Bayesian network and long short-term memory model at unsignalized intersections

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Science China(Information Sciences) 2021年第7期64卷 104-116页

作者： Hongbo GAO Hang SU Yingfeng CAI Renfei WU Zhengyuan HAO Yongneng XU Wei WU Jianqing WANG Zhijun LI Zhen KAN Department of Automation University of Science and Technology of China Institute of Advanced Technology University of Science and Technology of China The Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Automotive Engineering Research Institute Jiangsu University College of Traffic and Transportation Southeast University School of Automation Nanjing University of Science and Technology School of Vehicle and Mobility Tsinghua University

Cyclist trajectory prediction is of great significance for both active collision avoidance and path planning of intelligent vehicles. This paper presents a trajectory prediction method for the motion intention of cyclists in real traffic scenarios. This method is based on dynamic Bayesian network(DBN) and long short-term memory(LSTM). The motion intention of cyclists is hard to predict owing to potential large uncertainties. The DBN is used to infer the distribution of cyclists’ intentions at intersections to improve the prediction time. The LSTM with encoder-decoder is used to predict the cyclists’ trajectories to improve the accuracy of prediction. Therefore, the DBN and LSTM are adopted to guarantee prediction accuracy and improve the prediction time. The experiment results are presented to show the effectiveness of the predict strategies.

关键词： trajectory prediction dynamic Bayesian network (DBN) long short-term memory (LSTM) unsignalized intersections motion intention

trajectory prediction model for crossing-based target selection

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Virtual Reality & Intelligent Hardware 2019年第3期1卷 330-340页

作者： Hao ZHANG Jin HUANG Feng TIAN Guozhong DAI Hongan WANG Beijing Key Lab of Human-Computer Interaction Institute of SoftwareChinese Academy of SciencesBeijing 100190China State Key Lab of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China

Background Crossing-based target selection motion may attain less error rates and higher interactive speed in some cases.Most of the research in target selection fields are focused on the analysis of the interaction results.Additionally,as trajectories play a much more important role in crossing-based target selection compared to the other interactive techniques,an ideal model for trajectories can help computer designers make predictions about interaction results during the process of target selection rather than at the end of the whole process.Methods In this paper,a trajectory prediction model for crossing based target selection tasks is proposed by taking the reference of a dynamic model theory.Results Simulation results demonstrate that our model performed well with regard to the prediction of trajectories,endpoints and hitting time for target-selection motion,and the average error of trajectories,endpoints and hitting time values were found to be 17.28%,2.73mm and 11.50%,respectively.

关键词： Target selection Crossing-based selection trajectory prediction

Motion Planning for Autonomous Driving with Real Traffic Data Validation

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Chinese Journal of Mechanical Engineering 2024年第1期37卷 74-86页

作者： Wenbo Chu Kai Yang Shen Li Xiaolin Tang Western China Science City Innovation Center of Intelligent and Connected Vehicle Chongqing 400044China College of Mechanical and Vehicle Engineering Chongqing UniversityChongqing 400044China School of Civil Engineering Tsinghua UniversityBeijing 100084China

Accurate trajectory prediction of surrounding road users is the fundamental input for motion planning,which enables safe autonomous driving on public roads.In this paper,a safe motion planning approach is proposed based on the deep learning-based trajectory prediction method.To begin with,a trajectory prediction model is established based on the graph neural network(GNN)that is trained utilizing the INTERACTION dataset.Then,the validated trajectory prediction model is used to predict the future trajectories of surrounding road users,including pedestrians and vehicles.In addition,a GNN prediction model-enabled motion planner is developed based on the model predictive control technique.Furthermore,two driving scenarios are extracted from the INTERACTION dataset to validate and evaluate the effectiveness of the proposed motion planning approach,i.e.,merging and roundabout scenarios.The results demonstrate that the proposed method can lower the risk and improve driving safety compared with the baseline method.

关键词： trajectory prediction Graph neural network Motion planning INTERACTION dataset

Ship behavior prediction via trajectory extraction-based clustering for maritime situation awareness

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Journal of Ocean Engineering and Science 2022年第1期7卷 1-13页

作者： Brian Murray LokukalugePrasad Perera UiT The Arctic University of Norway TromsøNorway

This study presents a method in which historical AIS data are used to predict the future trajectory of a se-lected vessel.This is facilitated via a system intelligence-based approach that can be subsequently utilized to provide enhanced situation awareness to navigators and future autonomous ships,aiding proactive col-lision avoidance.By evaluating the historical ship behavior in a given geographical region,the method applies machine learning techniques to extrapolate commonalities in relevant trajectory segments.These commonalities represent historical behavior modes that correspond to the possible future behavior of the selected vessel.Subsequently,the selected vessel is classified to a behavior mode,and a trajectory with respect to this mode is predicted.This is achieved via an initial clustering technique and subsequent tra-jectory extraction.The extracted trajectories are then compressed using the Karhunen-Loéve transform,and clustered using a Gaussian Mixture Model.The approach in this study differs from others in that tra-jectories are not clustered for an entire region,but rather for relevant trajectory segments.As such,the extracted trajectories provide a much better basis for clustering relevant historical ship behavior modes.A selected vessel is then classified to one of these modes using its observed behavior.trajectory predic-tions are facilitated using an enhanced subset of data that likely correspond to the future behavior of the selected vessel.The method yields promising results,with high classification accuracy and low prediction error.However,vessels with abnormal behavior degrade the results in some situations,and have also been discussed in this study.

关键词： Maritime situation awareness Ship navigation trajectory prediction Collision avoidance Machine learning Unsupervised learning AIS

A Spatial-Temporal Attention Model for Human trajectory prediction

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IEEE/CAA Journal of Automatica Sinica 2020年第4期7卷 965-974页

作者： Xiaodong Zhao Yaran Chen Jin Guo Dongbin Zhao the School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083 the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the Key Laboratory of Knowledge Automation for Industrial Processes Ministry of EducationBeijing 100083China

Human trajectory prediction is essential and promising in many related applications. This is challenging due to the uncertainty of human behaviors, which can be influenced not only by himself, but also by the surrounding environment. Recent works based on long-short term memory(LSTM) models have brought tremendous improvements on the task of trajectory prediction. However, most of them focus on the spatial influence of humans but ignore the temporal influence. In this paper, we propose a novel spatial-temporal attention(ST-Attention) model,which studies spatial and temporal affinities jointly. Specifically,we introduce an attention mechanism to extract temporal affinity,learning the importance for historical trajectory information at different time instants. To explore spatial affinity, a deep neural network is employed to measure different importance of the neighbors. Experimental results show that our method achieves competitive performance compared with state-of-the-art methods on publicly available datasets.

关键词： Attention mechanism long-short term memory(LSTM) spatial-temporal model trajectory prediction