{"title":"Driving Behavior Prediction Based on Combined Neural Network Model","authors":"Runmei Li;Xiaoting Shu;Chen Li","doi":"10.1109/TCSS.2024.3350199","DOIUrl":null,"url":null,"abstract":"Accurate behavior prediction of surrounding vehicles can greatly improve the operating safety of autonomous vehicles. However, in real traffic scence, the complexity and uncertainties of traffic flow bring great challenges to driving behavior prediction. This article proposes a driving behavior prediction model using a wide-deep framework that combines gradient boosting decision tree (GBDT), convolutional neural network (CNN), and long short-term memory network (LSTM) algorithm to fully mine driving behavior characteristics while improve interpretability of the CNN-LSTM model. The GBDT algorithm can quantitatively describe the interaction between the autonomous vehicle and its surrounding vehicles during the driving process, obtaining a series of driving behavior rules, and integrating the driving behavior rule features into the CNN-LSTM neural network. The CNN-LSTM neural network model is constructed to find the spatial features in driving trajectory by CNNs and the temporal features by LSTM networks. The accuracy of the driving behavior prediction model is further improved. Simulation experiments show the rationality and validity of the model and algorithm.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":4.5000,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Computational Social Systems","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10419029/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, CYBERNETICS","Score":null,"Total":0}
引用次数: 0
Abstract
Accurate behavior prediction of surrounding vehicles can greatly improve the operating safety of autonomous vehicles. However, in real traffic scence, the complexity and uncertainties of traffic flow bring great challenges to driving behavior prediction. This article proposes a driving behavior prediction model using a wide-deep framework that combines gradient boosting decision tree (GBDT), convolutional neural network (CNN), and long short-term memory network (LSTM) algorithm to fully mine driving behavior characteristics while improve interpretability of the CNN-LSTM model. The GBDT algorithm can quantitatively describe the interaction between the autonomous vehicle and its surrounding vehicles during the driving process, obtaining a series of driving behavior rules, and integrating the driving behavior rule features into the CNN-LSTM neural network. The CNN-LSTM neural network model is constructed to find the spatial features in driving trajectory by CNNs and the temporal features by LSTM networks. The accuracy of the driving behavior prediction model is further improved. Simulation experiments show the rationality and validity of the model and algorithm.
期刊介绍:
IEEE Transactions on Computational Social Systems focuses on such topics as modeling, simulation, analysis and understanding of social systems from the quantitative and/or computational perspective. "Systems" include man-man, man-machine and machine-machine organizations and adversarial situations as well as social media structures and their dynamics. More specifically, the proposed transactions publishes articles on modeling the dynamics of social systems, methodologies for incorporating and representing socio-cultural and behavioral aspects in computational modeling, analysis of social system behavior and structure, and paradigms for social systems modeling and simulation. The journal also features articles on social network dynamics, social intelligence and cognition, social systems design and architectures, socio-cultural modeling and representation, and computational behavior modeling, and their applications.