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To tackle traffic congestion and improve fuel efficiency, we propose a Cooperative Adaptive Cruise Control (CACC) system using Model Predictive Control (MPC). This system maintains optimal vehicle spacing in a string of vehicles, reducing stop-and-go traffic and fuel consumption. Unlike human drivers, the CACC system can significantly decrease inter-vehicle distances, enhancing traffic flow and reducing aerodynamic drag. Our approach integrates disturbance estimation and intent sharing via Lagrangian interpolation, resulting in improved state tracking and reduced computational demands.

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Model Predictive Control of CACC with Disturbance Estimation

  • 会议论文

  • 发表日期：

    2026年

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  • Anas Abulehia,
  • Reza Dariani,
  • Julian Schindler

摘要

To tackle traffic congestion and improve fuel efficiency, we propose a Cooperative Adaptive Cruise Control (CACC) system using Model Predictive Control (MPC). This system maintains optimal vehicle spacing in a string of vehicles, reducing stop-and-go traffic and fuel consumption. Unlike human drivers, the CACC system can significantly decrease inter-vehicle distances, enhancing traffic flow and reducing aerodynamic drag. Our approach integrates disturbance estimation and intent sharing via Lagrangian interpolation, resulting in improved state tracking and reduced computational demands.