- Offline Table
- Throttle Model
- Brake Model
- Online Model
- Throttle Model
- Brake Model
carCalibration will show you how to build a data-driven longitudinal control system by machine-learning.
- numpy >= 1.15.4
- pandas >= 0.22.0
- scipy >= 1.2.0
- tensorflow >= 1.7.0
- keras >= 2.1.6
- python3
if you wish to use matplotlib or plotly then you should use
- matplotlib >= 2.2.2
- plotly >= 2.5.1
- From Pacmod
You need accel, brake, speed, steer, leftWheelSpeed, rightWheelSpeed
| %time |
accel([0, 1]) |
brake([0, 1]) |
speed[m/s] |
steer[rad] |
leftWheelSpeed[rad/s] |
rightWheelSpeed[rad/s] |
| 0 |
0 |
0.4 |
0 |
0.2 |
0 |
0 |
| ... |
... |
... |
... |
... |
... |
... |
- From Imu
You need x, y, z direction acceleration and pitch angle
| %time |
x[m/s^2] |
y[m/s^2] |
z[m/s^2] |
pitch[rad] |
| 0 |
0 |
0 |
-9.8 |
0 |
| .... |
.... |
.... |
.... |
.... |
- Prepare your csv File
- copy csv file under the data directory
- run the following command
python src/main.py
- Result will output in the 'result/' directory
- Output csv file will be below. You will get two type csv. (Brake and Throttle)
| command(Throttle or Brake) |
speed[m/s] |
accceleration[m/s^2] |
| 0.0 |
0 |
0.0 |
| .... |
.... |
.... |
https://arxiv.org/abs/1808.10134
