Run the following command:
pip install numpy pandas matplotlib scikit-learn casadi
python plot.py
Files:
lane_change.py
anddouble_lane_change.py
are the main files performing lane change and double lane change scenario in a dummy python environmentplot.py
contains the plotting code and integrating the above two files. There are different arguments like lane change, double lane change that can be set. For more information, see theargparse
parameters.
source your carla simulator using:
bash ./CarlaUE4.sh
open another terminal:
cd carla_simulator
python blend_control.py
Files:
autonomous_control.py
contains the source code for controlling the carla vehicle using MPC controllermpc.py
contains the MPC controller codemanual_steer.py
contains the source code for controlling the carla vechile manuallywheel_config.ini
contains the logitech steering wheel mapping informationblend_control.py
contains the main code that's required for collecting and storing the data along with the visualizations. There are different arguments with proper description added in the file. For more information, see theargparse
parameters.
TO DOs:
- in
blend_control.py
ensure that the reference path is getting displayed in the carla simulator properly - record the data using CARLA simulations and store into the desired format. desired recorded behaviour is in recordings folder. have to stabilize the human control a bit more.
- take data of 15-20 licensed drivers for stronger impact:
- 3 runs for simulator familarity using
manual_control.py
- 3 runs for desired double lane change behaviour in
blend_control.py
- 5 runs for actual data recording step using
blend_control.py
- compare different transition metrics - check
plot.py
to see how to compare different transitions and improveanalyze.py
- start writing paper with the results