Prediction of lane change trajectory of autonomous vehicles based on vehicle-environment data fusion
Article No.: 22, Pages 1 - 4
Abstract
Abstract. Vehicle lane changing is a current research hotspot in the field of autonomous driving. However, the current research does not consider the environmental data of a large number of vehicles as well as the lane change decision risk and lane change trajectory prediction as a system, and thus lacks the consideration of safety and trajectory accuracy in the lane change process. Based on this, a fusion model is established by two algorithms in this paper. First, the vehicle lane-changing behavior is analyzed, and the NGSIM trajectory dataset is used as the study data, and the data is processed using the SEMA method. Then, the XGBoost algorithm is used to build a vehicle lane change risk detection model. Finally, the LSTM algorithm is used to predict the lane change trajectories of vehicles. After experimental simulation, the designed model effectively improves the safety and accuracy of self-driving vehicles in the process of lane changing.
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- Prediction of lane change trajectory of autonomous vehicles based on vehicle-environment data fusion
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Published In
December 2022
199 pages
ISBN:9781450399548
DOI:10.1145/3588340
Copyright © 2022 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 03 November 2023
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ICBICC 2022
ICBICC 2022: 2022 International Conference on Big data, IoT, and Cloud Computing
December 2 - 4, 2022
Chengdu, China
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