feat: add Learning-Based Accel/Brake Map Calibrator #231
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…log (autowarefoundation#228) Signed-off-by: Takayuki Murooka <takayuki5168@gmail.com> Signed-off-by: CristianGariboldi <gariboldicristian@gmail.com>
Signed-off-by: CristianGariboldi <gariboldicristian@gmail.com>
Signed-off-by: CristianGariboldi <gariboldicristian@gmail.com>
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Signed-off-by: CristianGariboldi <gariboldicristian@gmail.com>
Signed-off-by: CristianGariboldi <gariboldicristian@gmail.com>
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@xmfcx Hi, just for your information, I opened the previous pull request in Autoware Tools, as you suggested. |
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Description
This is the PR regarding the learning-based vehicle calibration for longitudinal dynamics already discussed and presented in the discussion thread and in the Planning and Control meeting.
Tests performed
This calibrator has been tested in simulation and real vehicles. We've been using the calibrator for a different range of vehicles in Pix Moving with high accuracy.
Additional materials here:
Pix_Longitudinal_Calibration.pdf
Calibration.pptx
Effects on system behavior
This new calibrator improves the previous one in different aspects:
real data are interpolated with a neural network, which is able to provide a very accurate and smooth map, and can capture the non-linearities of the system;
the whole calibration process is very user-friendly, fast and efficient;
you don't need a huge amount of data for training the neural network, making the data collection process faster;
the learning-based approach can be easily adapted to different kind of scenarios, and not solely on the longitudinal dynamic. In my previous pull request for example, I also included the calibration for steering/parking scenarios, just by adding 1 input to the network;
It can be used with a diverse range of vehicles, with different dynamics, kinematics, engines and transmission systems;
It has been tested in simulation and with real vehicles. We now use it for calibrating our vehicles in Pix Moving.
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