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A Personalized Deep Learning Approach for Trajectory Prediction of Connected Vehicles
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 14, 2020 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Forecasting the motion of the leading vehicle is a critical task for connected autonomous vehicles as it provides an efficient way to model the leading-following vehicle behavior and analyze the interactions. In this study, a personalized time-series modeling approach for leading vehicle trajectory prediction considering different driving styles is proposed. The method enables a precise, personalized trajectory prediction for leading vehicles with limited inter-vehicle communication signals, such as vehicle speed, acceleration, space headway, and time headway of the front vehicles. Based on the learning nature of human beings that a human always tries to solve problems based on grouping and similar experience, three different driving styles are first recognized based on an unsupervised clustering with a Gaussian Mixture Model (GMM). The GMM generates a specific driving style for each vehicle based on the speed, acceleration, jerk, time, and space headway features of the leading vehicle. Then, a personalized joint time-series modeling (JTSM) method based on the Long Short-Term Memory (LSTM) Recurrent Neural Network model (RNN) is proposed to predict the trajectory of the front vehicle. The JTSM contains a joint LSTM layer and different fully-connected regression layers for different driving styles. The proposed method is tested with the Next Generation Simulation (NGSIM) data on US101, and I-80. The JTSM is tested for making predictions one second ahead. Results indicate that the proposed personalized JTSM approach shows a significant advantage over the other algorithms.
CitationXing, Y., Huang, C., Lv, C., Liu, Y. et al., "A Personalized Deep Learning Approach for Trajectory Prediction of Connected Vehicles," SAE Technical Paper 2020-01-0759, 2020, https://doi.org/10.4271/2020-01-0759.
Data Sets - Support Documents
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