Carputer is a 1/10th scale self driving car. It drives based on video camera and speedometer inputs. It uses a neural network to drive. The camera image and speedometer get sent to the neural network and the network outputs steering and throttle motor controls for the car.
Since the car uses a neural network for driving, it has to be trained. The training process is basically driving it around a track about 20 times or so using the car's radio control. During that driving, we record all of the radio control inputs along with the video and speedometer data. Once we have that, the neural network can learn how to mimic our driving style by outputting steering and throttle based on the video and speedometer.
The process is to record, train, and then run autonomously, as seen in the steps below.
This is an experimental setup, so it's not super-clean code or hardware.
- Turn on ESC, RC controller. Plug in battery, USB. Start switch to off.
- Run InsomniaX and disable lid sleep and idle sleep.
- activate the virtualenv:
source /path/to/venv/bin/activate - run a script to drive and record training data:
python main_car.py record-- this will let you have manual control over the car and save out recordings when you flip the switch
- Turn on ESC, RC controller. Plug in battery, USB. Start switch to off.
- Run InsomniaX and disable lid sleep and idle sleep.
- activate the virtualenv:
source /path/to/venv/bin/activate - run a script to let tensorflow drive:
python main_car.py tf-- when you flip the switch, you will lose manual control and the car will attempt to drive on its own - for autonomous kill switch: pull throttle and turn the steering wheel
- to revive autonomous mode, hit the channel 3 button (near the trigger)
- convert TRAINING images to np arrays:
python NeuralNet/filemash.py /path/to/data(Can be multiple paths) - convert TEST images to np arrays:
python NeuralNet/filemash.py /path/to/data --gen_test(Can be multiple paths) - train a model:
python NeuralNet/convnet02.py. Train for minimum 1500 iterations, ideally around 5000 iterations. - use this model to drive the car (see above)
- for training info, see
debug.html-- reminder: < 7 is right, > 7 is left - run
analysis/make_video.pyfor debug videos - use
analysis/plot_vs_time.pyto view telemetry data
- Fix the radio control dropped signal error
- Get the TX1 working
- Get the IMU recording data
- Fix keepalive on Arduino
- Look into remote SSH type software so we don't have to keep popping open the car.
Updates - We no longer use the IMUs and we're no longer trying to run the NVidia TX1 computer. Macbook works better.
Work in progress, of course. The simulator runs in Unity. I didn't check in the lighting files because they are big, but if you build lighting, it should look like this...
In the "newer" version of the car we replaced the MacBook Pro with an Nvidia TX2. Here is the current way to get the car up and running (with all the weird quirks)
The TX2 has a few helper aliases on the vehicle. They are located in the ~/.bashrc and are able to be called anywhere. Here they are:
alias record="nohup python2 /home/nvidia/workspace/carputer/main_car.py record & tail -f nohup.out"
alias tf="nohup python2 /home/nvidia/workspace/carputer/main_car.py tf & tail -f nohup.out"
alias kill-car="pkill python"
The vehicle will spin up an access point with SSID carputer-ap on boot. It is unprotected.
The TX2 will take the IP address of 10.42.0.1. Hostname is nvidia and the password is nvidia. Note to drivers at the races: Please don't hack our car.
The training pipeline is relativly the same as described in the README, with a few quality of life improvements.
To start gathering training data, run the following command
record
That will start the python main_car.py script with a few extra commands to make sure it keeps running even if we lose connection with the vehicle.
You can do that. It is pretty slow, but not too bad. The commands are exactly the same as described in the README. However, one note (and this applies to gathering training data too) is that currently the TX2 has very little hard disk memory (less than 20GB nominally).
The requires the user to make sure that there is enough space to check if there is enough disk space to write the training images, training npy files, and the models.
I like to use df -h or ncdu to see where the most disk space is and rm accordingly.
Again, the pipeline is pretty much the same with a few minor changes.
Unless you understand how the dyn_config.json works, your best bet to load your most recently trained model is to edit the config.py file to the model you want to load.
To run, simply type tf. That will start up the script to run the car in autonomous mode.
If at any point you want to stop gathering training data or autonmous data, please run the command kill-car. It really just pkills python, but it is nice to just remember that you want to kill the car.