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MPC-CBF_for_ADS

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[![DOI](https://zenodo.org/badge/505930246.svg)](https://zenodo.org/badge/latestdoi/505930246)
# MPC-CBF_for_ADS

The source codes for the work ["Robust Safe Control for Automated Driving Systems With Perception Uncertainties"](https://link.springer.com/chapter/10.1007/978-3-031-27540-1_28) are listed here. You can also read more about this project by Yan Feng Yu [here](https://kth.diva-portal.org/smash/record.jsf?dswid=-2929&pid=diva2%3A1704875&c=2&searchType=SIMPLE&language=no&query=feng+yu&af=%5B%22dateIssued%3A2022%22%5D&aq=%5B%5B%5D%5D&aq2=%5B%5B%5D%5D&aqe=%5B%5D&noOfRows=50&sortOrder=author_sort_asc&sortOrder2=title_sort_asc&onlyFullText=false&sf=all).

Simulation results are shown in the folder `sim_output` (at the moment when Kalman filter is placed after the control action).

The scripts: 
* `acc_with_cbf_mpc.py` simulate the case without any perception noise present
* `acc_with_cbf_mpc_noise.py` simulates the case with perception noise present
* `acc_with_cbf_mpc_kf.py` simulates with Kalman filter placed before the control action to handle the perception noise

## Carla

The simulation is performed using [Carla simulator](https://github.com/carla-simulator/carla) version 0.9.12 with Python version 3.6.

## Scenarios

The scenario is based on [Scenario runner](https://github.com/carla-simulator/scenario_runner), an module provided by CARLA.

The autonomous agents are based on KeyingLucyWang's repository [Safe_Reconfiguration_Scenarios](https://github.com/KeyingLucyWang/Safe_Reconfiguration_Scenarios).

The simulation provided here is tested and validated on scenario: `FollowLeadingVehicle_5`.

## Perception noise model

The perception noise model based on the computed distance by a CNN model during salt and pepper noise is stored in `differences.save`.

## Getting started

To run the simulation, begin with launching CARLA (CarlaUE4.sh or CarlaUE4.exe).

Next, open two terminals where you export the following path that suits your own computer (according to [Scenario runner: Getting started](https://carla-scenariorunner.readthedocs.io/en/latest/getting_scenariorunner/)):
```
export CARLA_ROOT=/path/to/your/carla/installation
export SCENARIO_RUNNER_ROOT=/path/to/your/scenario/runner/installation
export PYTHONPATH=$PYTHONPATH:${CARLA_ROOT}/PythonAPI/carla/dist/carla-<VERSION>.egg
export PYTHONPATH=$PYTHONPATH:${CARLA_ROOT}/PythonAPI/carla
```
In my computer where Windows version of CARLA is used, it becomes:
```
set CARLA_ROOT=C:\Users\Admin\Simplepath\IL2232\CARLA_0912\WindowsNoEditor
set SCENARIO_RUNNER_ROOT=C:\Users\Admin\Simplepath\Exjobb\MPC-CBF_for_ADS
set PYTHONPATH=%PYTHONPATH%;%CARLA_ROOT%\PythonAPI\carla\dist\carla-0.9.12-py37-win-amd64.egg
set PYTHONPATH=%PYTHONPATH%;%CARLA_ROOT%\PythonAPI\carla\agents
set PYTHONPATH=%PYTHONPATH%;%CARLA_ROOT%\PythonAPI\carla
set PYTHONPATH=%PYTHONPATH%;%CARLA_ROOT%\PythonAPI
set PYTHONPATH=%PYTHONPATH%;%CARLA_ROOT%\PythonAPI\carla\agents\navigation
set PYTHONPATH=%PYTHONPATH%;%CARLA_ROOT%\PythonAPI\carla\agents\tools
```
In the first terminal, enter the command: `python scenario_runner.py --scenario FollowLeadingVehicle_5 --reloadWorld`.  
In the second terminal, enter the python script name that you want to run i.e.: `python acc_with_cbf_mpc_kf.py`.

You will now see a pygame window with the scenario running. To run it again, simply enter the aforementioned commands again.

The figures in the folder `sim_output` stores the case when N = 6, 8, 12. For respective case, T is preferred to be T = 12, 8, 8.

MPC-CBF_for_ADS

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