Lane-Change in Dense Traffic with Model Predictive Control and Neural Networks
<|reference_start|>Lane-Change in Dense Traffic with Model Predictive Control and Neural Networks: This paper presents an online smooth-path lane-change control framework. We focus on dense traffic where inter-vehicle 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 (GAN) 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 in different levels of traffic density and cooperativeness of other drivers. The simulation results support the effectiveness, driving comfort, and safety of the proposed method.<|reference_end|>
@article{bae2024lane-change,
    title={Lane-Change in Dense Traffic with Model Predictive Control and Neural
  Networks},
    author={Sangjae Bae, David Isele, Alireza Nakhaei, Peng Xu, Alexandre Miranda
  Anon, Chiho Choi, Kikuo Fujimura, Scott Moura},
    journal={IEEE Transactions on Control Systems Technology ( Volume: 31,
  Issue: 2, March 2023)},
    year={2024},
    doi={10.1109/TCST.2022.3193923},
    archivePrefix={arXiv},
    eprint={2403.19633},
    primaryClass={eess.SY cs.SY}
}