TR2025-104

Safe Interactive Motion Planning by Differentiable Optimal Control and Online Preference Learning

    •  Chavez Armijos, A., Berntorp, K., Di Cairano, S., "Safe Interactive Motion Planning by Differentiable Optimal Control and Online Preference Learning", American Control Conference (ACC), July 2025.
      BibTeX TR2025-104 PDF
      • @inproceedings{ChavezArmijos2025jul,
      • author = {Chavez Armijos, Andres and Berntorp, Karl and {Di Cairano}, Stefano},
      • title = {{Safe Interactive Motion Planning by Differentiable Optimal Control and Online Preference Learning}},
      • booktitle = {American Control Conference (ACC)},
      • year = 2025,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2025-104}
      • }
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  • Research Areas:

    Control, Dynamical Systems, Machine Learning, Optimization, Robotics

Abstract:

We present an interactive motion planner that integrates online learning of human driver preferences with parametric control barrier functions. Using stochastic models with Gaussian disturbances to capture human-driven vehicle behavior uncertainty, we update parameters in real-time parameter by Kalman filtering while ensuring safety by control barrier functions. A case study on highway lane-changing tasks demonstrates improved traffic flow, reduced disruptions, and lighter actuation effort compared to non-adaptive algorithms.