/Interactive-Motion-Planning

Primary LanguagePythonMIT LicenseMIT

Interactive Motion Planning

Codebase for adaptive interactive mixed-integer model predictive control (aiMPC): an optimal control-based interactive motion planning algorithm for autonomous vehicles.

  • Real-time Software-and-Human-in-the-loop simulation in CARLA.

Test scenario illustration

Mandatory lane change scenario: a stopped truck on the right lane necessitates a lane change for the autonomous vehicle which needs to negotiate with a human-driven vehicle on the left lane. alt text

  • Blue vehicle is the ego vehicle and red vehicle is the human-driven neighboring vehicle (NV).

shil

  • aiMPC estimates NV's cost online and adapts the MPC.

A case when ego merges ahead

highinfluence.mp4

A case when ego merges behind

lowinfluence.mp4

A case where the NV's nature changes

naturechange.mp4
  • αp and αa are the estimated NV cost weights.

Key dependencies:

  • CARLA simulator
  • Gurobi
  • ROS Noetic
  • MATLAB (data analysis)

Architecture

shil-arch-1

  • Cite as
@article{bhattacharyya2024automated,
  title={Automated Lane Change via Adaptive Interactive MPC: Human-in-the-Loop Experiments},
  author={Bhattacharyya, Viranjan and Vahidi, Ardalan},
  journal={IEEE Transactions on Control Systems Technology},
  year={2024},
  publisher={IEEE}
}