Human-Centered Trajectory Tracking Control for Autonomous Vehicles With Driver Cut-In Behavior Prediction
Trajectory tracking control in the cut-in scenarios is challenging, since the autonomous vehicles have to follow the reference trajectory and cooperate with the cut-in vehicles. This paper proposes a human-centered trajectory tracking control strategy integrating driver behavior prediction for the cut-in scenarios and their transient processes. A recurrent neural network (RNN) with long short-term memory (LSTM) cells is used to predict the driver behaviors of the cut-in vehicle. Then, a model predictive control (MPC) approach considering the driver behaviors of the cut-in vehicle is designed to track the reference trajectory. The transient processes of the cut-in scenarios are considered for different cut-in behaviors. Moreover, the moving horizon estimator (MHE) is used to estimate the vehicle lateral velocity that is used in the controller. Human driver tests on a driving simulator show that the drivers’ intention of the cut-in vehicle can be predicted by the RNN with LSTM cells. CarSim® simulation studies show the human-centered trajectory tracking controller can track the reference trajectory using the estimated vehicle lateral velocity. The autonomous vehicle can cooperate with the cut-in vehicle in different driving situations and obtain smooth transient processes of the cut-in scenarios.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/00189545
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Supplemental Notes:
- Copyright © 2019, IEEE.
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Authors:
- Chen, Yimin
- Hu, Chuan
- Wang, Junmin
- Publication Date: 2019-9
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 8461-8471
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Serial:
- IEEE Transactions on Vehicular Technology
- Volume: 68
- Issue Number: 9
- Publisher: Institute of Electrical and Electronics Engineers (IEEE)
- ISSN: 0018-9545
- Serial URL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=25
Subject/Index Terms
- TRT Terms: Autonomous vehicles; Behavior; Control systems; Drivers; Highway safety; Markov processes; Mathematical prediction; Mobile robots; Trajectory control
- Identifier Terms: Carsim (Computer program)
- Subject Areas: Highways; Safety and Human Factors; Vehicles and Equipment;
Filing Info
- Accession Number: 01722563
- Record Type: Publication
- Files: TRIS
- Created Date: Nov 15 2019 1:45PM