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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

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

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

Target maneuver Trajectory prediction based on RBF neural network optimized by hybrid algorithm

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Journal of Systems Engineering and Electronics 2021年第2期32卷 498-516页

作者： XI Zhifei XU An KOU Yingxin LI Zhanwu YANG Aiwu Aeronautics Engineering College Air Force Engineering UniversityXi’an 710038China

Target maneuver Trajectory prediction plays an important role in air combat situation awareness and threat assessment.To solve the problem of low prediction accuracy of the traditional prediction method and model,a target maneuver Trajectory prediction model based on phase space reconstruction-radial basis function(PSR-RBF)neural network is established by combining the characteristics of trajectory with time continuity.In order to further improve the prediction performance of the model,the rival penalized competitive learning(RPCL)algorithm is introduced to determine the structure of RBF,the Levenberg-Marquardt(LM)and the hybrid algorithm of the improved particle swarm optimization(IPSO)algorithm and the k-means are introduced to optimize the parameter of RBF,and a PSR-RBF neural network is constructed.An independent method of 3D coordinates of the target maneuver trajectory is proposed,and the target manuver trajectory sample data is constructed by using the training data selected in the air combat maneuver instrument(ACMI),and the maneuver Trajectory prediction model based on the PSR-RBF neural network is established.In order to verify the precision and real-time performance of the Trajectory prediction model,the simulation experiment of target maneuver trajectory is performed.The results show that the prediction performance of the independent method is better,and the accuracy of the PSR-RBF prediction model proposed is better.The prediction confirms the effectiveness and applicability of the proposed method and model.

关键词： Trajectory prediction k-means improved particle swarm optimization(IPSO) Levenberg-Marquardt(LM) radial basis function(RBF)neural network

Hypersonic Vehicle Trajectory prediction Algorithm Based on Hough Transform

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Chinese Journal of Electronics 2021年第5期30卷 918-930页

作者： LI Fan XIONG Jiajun LAN Xuhui BI Hongkui TAN Xiansi Air Force Early Warning Academy Unit 95980 of the PLA

Trajectory prediction is a prerequisite for missile and high-speed vehicle guidance interception. To address the trajectory problem for the near space hypersonic unpowered gliding vehicle. Firstly, the Trajectory prediction mechanism is analyzed. Based on the existing NSHV Trajectory prediction method, the key techniques of Trajectory prediction are discussed from the perspectives of prediction parameter selection and prediction parameter description. Then, a Trajectory prediction method based on Hough transform is proposed. The core of the method is to select the longitude latitude, and high direction positions as prediction parameters, and use the Hough transform to fit the prior basis functions. In the latitude and longitude direction, the rationality of predicting parameters is discussed from two aspects: position extreme point and lateral maneuverability. In the height direction,for the NSHV oscillation drop characteristic, two Hough transforms are used to separate the periodic term from the linear term. In addition, we give the approximate interval of the parameter values of the method, and design a parameter adaptive scheme. Finally, Trajectory prediction is performed on two different control patterns. The error of the prediction time of 100 s is within 25 km, and the results show the effectiveness of the proposed method.

关键词： Neural nets (theory) Spatial variables control Aerospace control Integral transforms Control system analysis and synthesis methods Near space hypersonic vehicle Trajectory prediction Hough transform

Intent Inference Based Trajectory prediction and Smooth for UAS in Low-Altitude Airspace with Geofence

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Computers, Materials & Continua 2020年第4期63卷 417-444页

作者： Qixi Fu Xiaolong Liang Jiaqiang Zhang Xiangyu Fan National Key Laboratory of Air Traffic Collision Prevention Air Force Engineering UniversityXi’an710051China Shaanxi Province Laboratory of Meta-Synthesis for Electronic and Information System Xi’an710051China

In order to meet the higher accuracy requirement of Trajectory prediction for Unmanned Aircraft System(UAS)in Unmanned Aircraft System Traffic Management(UTM),an Intent Based Trajectory prediction and Smooth Based on Constrained State-dependent-transition Hybrid Estimation(CSDTHE-IBTPS)algorithm is proposed.Firstly,an intent inference method of UAS is constructed based on the information of ADS-B and geofence system.Moreover,a geofence layering algorithm is proposed.Secondly,the Flight Mode Change Points(FMCP)are used to define the relevant mode transition parameters and design the guard conditions,so as to generate the mode transition probability matrix and establish the continuous state-dependent-transition model.After that,the constrained Kalman filter(CKF)is applied to improve State-dependent-transition Hybrid Estimation(SDTHE)algorithm by applying equality constraint to the velocity of UAS in the straight phase and turning phase,respectively,and thus the constrained state-dependent-transition hybrid estimation(CSDTHE)algorithm is constructed.Finally,the results of intent inference and hybrid estimation are used to make Trajectory prediction.Furthermore,each flight segment of trajectory is smoothed respectively by Rauch-Tung-Striebel(RTS)backward smooth method using the proposed CSDTHE-RTS algorithm,so as to obtain more accurate Trajectory prediction results.The simulation shows that the proposed algorithm can reduce the errors of Trajectory prediction and the time delay of intent inference.

关键词： Trajectory prediction unmanned aircraft system geofence intent inference hybrid estimation Rauch-Tung-Striebel(RTS)backward smooth

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

A Recurrent Attention and Interaction Model for Pedestrian Trajectory prediction

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IEEE/CAA Journal of Automatica Sinica 2020年第5期7卷 1361-1370页

作者： Xuesong Li Yating Liu Kunfeng Wang Fei-Yue Wang State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190and also with the University of Chinese Academy of SciencesBeijing 100049China College of Information Science and Technology Beijing University of Chemical TechnologyBeijing 100029China State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China IEEE

The movement of pedestrians involves temporal continuity,spatial interactivity,and random diversity.As a result,pedestrian Trajectory prediction is rather challenging.Most existing Trajectory prediction methods tend to focus on just one aspect of these challenges,ignoring the temporal information of the trajectory and making too many assumptions.In this paper,we propose a recurrent attention and interaction(RAI)model to predict pedestrian trajectories.The RAI model consists of a temporal attention module,spatial pooling module,and randomness modeling module.The temporal attention module is proposed to assign different weights to the input sequence of a target,and reduce the speed deviation of different pedestrians.The spatial pooling module is proposed to model not only the social information of neighbors in historical frames,but also the intention of neighbors in the current time.The randomness modeling module is proposed to model the uncertainty and diversity of trajectories by introducing random noise.We conduct extensive experiments on several public datasets.The results demonstrate that our method outperforms many that are state-ofthe-art.

关键词： Deep learning long short-term memory(LSTM) recurrent attention and interaction(RAI)model Trajectory prediction