A Personalized Deep Learning Approach for Trajectory Prediction of Connected Vehicles

Yang Xing, Chao Huang, Chen Lv, Yahui Liu, Hong Wang, Dongpu Cao

Research output: Journal article publicationConference articleAcademic researchpeer-review

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number2020-01-0759
Number of pages7
JournalSAE Technical Papers
Volume2020-April
Issue numberApril
DOIs
Publication statusPublished - 14 Apr 2020
Externally publishedYes
EventSAE 2020 World Congress Experience, WCX 2020 - Detroit, United States
Duration: 21 Apr 202023 Apr 2020

ASJC Scopus subject areas

  • Automotive Engineering
  • Safety, Risk, Reliability and Quality
  • Pollution
  • Industrial and Manufacturing Engineering

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