|James Jeon abefa9261a Update readme.md||1 year ago|
|balance_data.py||1 year ago|
|can.sh||1 year ago|
|collect_raw_data.py||1 year ago|
|data_size.txt||1 year ago|
|eval.py||1 year ago|
|find_bad_data.py||1 year ago|
|get_train_data.py||1 year ago|
|get_val_data.py||1 year ago|
|live_eval.py||1 year ago|
|model.py||1 year ago|
|mount.sh||1 year ago|
|params.py||1 year ago|
|readme.md||1 year ago|
|train.py||1 year ago|
|visualize_raw_data.py||1 year ago|
Project was inspired by “Deeptesla” (https://github.com/lexfridman/deeptesla.git)
Use TensorFlow to Inference the steering wheel angle only based on the single front image
I drove my car by myself and at the same time collecting the data. It is possible to connect the keyboard to the Jetson TX2 and run the Python code, but I found that it is more convenient to use the serial port in the GPIO port and connect to my MacBook and run the script.
Network: Modified AlexNet to fit my model
Loss: Mean Squared Error
I resized the frame into size of [66,256,1] which I personally thought is good enough for the CNN.
I could find that the Toyota uses the “ID : 0x025” as the steering wheel angle. I found that they are using the signed data, fixed point decimal. For more detail please check the code.
Steering wheel usually don’t change a lot in the real world driving environment. Therefore, the data was extremely imbalanced. I spend lots of time balancing the data.
CNN requires big amount of memory, if the dataset don’t fit into the memory, then cut it down to smaller batch.
Before I start this project I thought the network will make huge difference in linear regression problem, because it does make huge difference in categorical problem. However, I found that the linear regression problem doesn’t hugely affected by the network
It was challenging to make the data into the dataset and balance the data prevent from biased weight.