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Pedestrian trajectory prediction method based on the social-lstm model for vehicle collision

Transportation Safety and Environment 2024年第3期6卷 140-151页

作者： Yong Han Xujie Lin Di Pan Yanting Li Liang Su Robert Thomson Koji Mizuno School of Mechanical and Automotive Engineering Xiamen University of TechnologyXiamen 361024China Fujian Province Key Laboratory of Advanced Design and Manufacturing of Buses Xiamen 361024China Engineering Research Institute of Xiamen Kinglong United Automobile Industry Co. Ltd.Xiamen 361000China Chalmers University of Technology Gothenburg 41296Sweden Department of Mechanical Systems Engineering Nagoya UniversityNagoya 464-8603Japan

Techniques for predicting the trajectory of vulnerable road users are important to the development of perception systems for autonomous vehicles to avoid *** most effective trajectory prediction methods,such as social-lstm,are often used to predict pedestrian trajectories in normal passage ***,they can produce unsatisfactory prediction results and data redundancy,as well as difficulties in predicting trajectories using pixel-based coordinate systems in collision avoidance *** is also a lack of validations using real vehicle-to-pedestrian *** address these issues,some insightful approaches to improve the trajectory prediction scheme of social-lstm were proposed,such methods included transforming pedestrian trajectory coordinates and converting image coordinates to world *** YOLOv5 detection model was introduced to reduce target loss and improve prediction *** DeepSORT algorithm was employed to reduce the number of target transformations in the tracking *** Perspective Transformation(IPT)and Direct Linear Transformation(DLT)theories were combined to transform the coordinates to world coordinates,identifying the collision location where the accident could *** performance of the proposed method was validated by training tests using MS COCO(Microsoft Common Objects in Context)and ETH/UCY *** results showed that the target detection accuracy was more than 90%and the prediction loss tends to decrease with increasing training steps,with the final loss value less than 1%.The reliability and effectiveness of the improved method were demonstrated by benchmarking system performance to two video recordings of real pedestrian accidents with different lighting conditions.

关键词： vehicle-to-pedestrian collisions pedestrian trajectory prediction YOLOvB DeepSORT social-lstm

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Workload Allocation Based on User Mobility in Mobile Edge Computing

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Journal on Big Data 2020年第3期2卷 105-115页

作者： Tengfei Yang Xiaojun Shi Yangyang Li Binbin Huang Haiyong Xie Yanting Shen National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data(NEL-PSRPC) BeijingChina National Computer Network Emergency Response Technical Team Coordination Center of China BeijingChina Department of Science and Technology China Electronics Technology Group CorporationBeijingChina School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhouChina University of Science and Technology of China HefeiChina

Mobile Edge Computing(MEC)has become the most possible network architecture to realize the vision of interconnection of all *** offloading compute-intensive or latency-sensitive applications to nearby small cell base stations(sBSs),the execution latency and device power consumption can be reduced on resource-constrained mobile ***,computation delay of Mobile Edge Network(MEN)tasks are neglected while the unloading decision-making is studied in *** this paper,we propose a workload allocation scheme which combines the task allocation optimization of mobile edge network with the actual user behavior activities to predict the task allocation of single *** obtain the next possible location through the user's past location information,and receive the next access server according to the grid ***,the next time task sequence is calculated on the base of the historical time task sequence,and the server is chosen to preload the *** the experiments,the results demonstrate a high accuracy of our proposed model.

关键词： Edge computing workload allocation social-lstm context-sensitive user mobility

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