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Uncertainty-Aware Decision-Making and Planning for Autonomous Forced Merging

This is the Python code for the article which is published in Proceedings of 63rd IEEE Conference on Decision and Control, 2024.

@inproceedings{zhou2024uncertianty,
  title={Uncertainty-Aware Decision-Making and Planning for Autonomous Forced Merging},
  author={Zhou, Jian and Gao, Yulong and Olofsson, Bj\"orn and Frisk, Erik},
  booktitle={2024 63nd IEEE Conference on Decision and Control (CDC)},
  pages={ },
  year={2024},
  organization={IEEE}
}

Jian Zhou and Erik Frisk are with the Department of Electrical Engineering, Linköping University, Sweden. Yulong Gao is with the Department of Electrical and Electronic Engineering, Imperial College London, UK. Björn Olofsson is with both the Department of Automatic Control, Lund University, Sweden, and the Department of Electrical Engineering, Linköping University, Sweden.

For any questions, feel free to contact me as: jian.zhou@liu.se or zjzb1212@qq.com

Packages for running the code

To run the code you need to install the following key packages:

Pytope: https://pypi.org/project/pytope/

CasADi: https://web.casadi.org/

HSL Solver: https://licences.stfc.ac.uk/product/coin-hsl

Note: Installing the HSL package can be a bit comprehensive, but the solvers just speed up the solutions. You can comment out the places where the HSL solver is used (i.e., comment out the command "ipopt.linear_solver": "ma57"), and just use the default linear solver of ipopt in CasADi.

Introduction to the files

In the folder Case_1, you will find the implementation for comparing the proposed method, the DMPC, and RMPC, in a single scenario. The file main.ipynb defines the main file for running the code; Planner_P is the method for the proposed approach, Planner_D is the method for the DMPC, and Planner_R is the method for the RMPC. The two mat files xi_HD.mat and cdf_HD.mat are the distribution data, which is identified offline using the highD dataset, to simulate the control input of SVs. The folders SVModelingHighDDataDistribution contains the method for modeling the SV0 and SV1 that are driven by the highD data.

After running the file main.ipynb, you should be able to generate the simulation animations. The analysis of the results will be straightforward using the output of each method.