Lane-Change in Dense Traffic With Model Predictive Control and Neural Networks

This article presents an online smooth-path lane-change control framework. We focus on dense traffic where intervehicle space gaps are narrow, and cooperation with surrounding drivers is essential to achieve the lane-change maneuver. We propose a two-stage control framework that harmonizes model pre...

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Bibliographic Details
Published in:IEEE transactions on control systems technology 2023-03, Vol.31 (2), p.646-659
Main Authors: Bae, Sangjae, Isele, David, Nakhaei, Alireza, Xu, Peng, Anon, Alexandre Miranda, Choi, Chiho, Fujimura, Kikuo, Moura, Scott
Format: Article
Language:English
Subjects:
Autonomous driving
Autonomous vehicles
Computational modeling
cooperation-aware driving
dense traffic
Generative adversarial networks
generative adversarial networks (GANs)
Kalman filters
Lane changing
lane-change
Maneuvers
model predictive control (MPC)
Neural networks
Optimization
Planning
Predictive control
recurrent neural networks (RNNs)
Safety
Traffic control
Traffic models
Traffic volume
Trajectory
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Summary:This article presents an online smooth-path lane-change control framework. We focus on dense traffic where intervehicle space gaps are narrow, and cooperation with surrounding drivers is essential to achieve the lane-change maneuver. We propose a two-stage control framework that harmonizes model predictive control (MPC) with generative adversarial networks (GANs) by utilizing driving intentions to generate smooth lane-change maneuvers. To improve performance in practice, the system is augmented with an adaptive safety boundary and a Kalman filter to mitigate sensor noise. Simulation studies are investigated at different levels of traffic density and cooperativeness of other drivers. The simulation results support the effectiveness, driving comfort, and safety of the proposed method.
ISSN:1063-6536
1558-0865
DOI:10.1109/TCST.2022.3193923