Physics-informed Machine Learning with Heuristic Feedback Control Layer for Autonomous Vehicle Control

Details

• Creators:
  Li, Xianning ; Wang, Yebin ; Ozbay, Kaan ; Jiang, Zhong-Ping
• Corporate Creators:
  New York University. Tandon School of Engineering
• Corporate Contributors:
  Connected Communities for Smart Mobility Toward Accessible and Resilient Transportation for Equitably Reducing Congestion (C2SMARTER) Tier-1 University Transportation Center (UTC) ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  Autonomous Vehicles Congestion Management Systems Connected Vehicles Machine Learning Trajectory Control Vehicle Mix
• Resource Type:
  Manuscript
• Right Statement:
  Public Domain
• Geographical Coverage:
  United States
• Corporate Publisher:
  Connected Communities for Smart Mobility Toward Accessible and Resilient Transportation for Equitably Reducing Congestion (C2SMARTER) Tier-1 University Transportation Center (UTC)
• Abstract:
  This paper proposes a novel physics-informed machine learning framework for motion planning and control of autonomous vehicles. By integrating longitudinal and lateral control, a nonlinear control problem is formulated using Model Predictive Control (MPC). To address computational challenges, a self-supervised framework, Recurrent Predictive Control (RPC), is introduced, leveraging differentiable neural networks and recurrent neural networks to train a neural network controller. Additionally, a heuristic feedback control layer is designed to reduce steady-state errors in the closed-loop tracking. Through numerical simulations and co-simulations using Simulink and CarSim, five neural network controllers are compared with an MPC controller in a lane-changing scenario. The proposed RPC framework improves computational efficiency by 95% compared to MPC, enhances generalization performance compared to Approximate MPC, and reduces performance loss by 17% compared to Differentiable Predictive Control. The heuristic feedback control layer further reduces steady-state errors and improves convergence speed during training.
• Content Notes:
  Public Domain, except where noted in the document. Li, Xianning, Yebin Wang, Kaan Ozbay, and Zhong-Ping Jiang. Physics-informed Machine Learning with Heuristic Feedback Control Layer for Autonomous Vehicle Control.
• Format:
  PDF
• Funding:
  69A3552348326
• Collection(s):
  University Transportation Centers Autonomous Vehicles
• Main Document Checksum:
  urn:sha-512:eb6beb45e790c2b879d0cb95b8f3ad57ed95f24699ef99a0b7d9cd35dec05327cc15e0d8bf35b4b94abde8bcf1ed177aa34992b0b357acd952e3fa707ccf0cb7
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/86348/dot_86348_DS1.pdf
• File Type:
  [PDF - 9.09 MB ]