This repository contains an implementation of MPC-based racing in a Carla simulation. Features include
- Smooth parameterization of racing tracks
- Model predictive control formulation which maximizes the vehicle's progress along path
- Custom combined PID and pure pursuit racing-tuned controllers for comparison
- Optimization of MPC parameters using genetic algorithm
- Kinematic, dynamic, and blended bicycle models with custom extensions to accurately model the simulation vehicle
- Collect, visualize, and create filtered datasets from vehicle data
- Train and evaluate tire models using pytorch
The simulation is built on the GRAIC platform for the CARLA simulator.
First, follow the instructions to install GRAIC here. I found the following modifications are necessary:
-
Get the carla install by following instructions at carla-simulator/carla#7017 (comment)
-
Update networkx to 2.8:
pip install networkx==2.8 -
Add scenario_runner-0.9.13/srunner/tests/carla_mocks to pythonpath:
export PYTHONPATH=$PYTHONPATH:/opt/scenario_runner-0.9.13/srunner/tests/carla_mocks -
install tkinter:
sudo apt-get install python3-tk
If you did not install CARLA to /opt/carla-simulator, then change the parameters in auxillary/carla.py to match your install location. Then when you run python3 auxillary/carla.py, it should load the CARLA simulator on the Shanghai map.
How to run (without scenarios):
- Start carla with
python3 auxillary/carla.py. - Run the GRAIC wrapper using the script
python3 automatic_control_GRAIC.py.
Note the main branch implements the model predictive controller, while the pp branch implements the pure pursuit controller with the best parameters found via GA.
The control directory holds mpc-related files. the models directory holds files related
to the development of the vehicle models (these are re-implemented in casadi symbolics in
control/mpc.py). The splines directory holds files related to the parameterization of
the centerline and the lane boundaries. The script directory contains testing and visualization
scripts for various parts of the project.