Two cameras will help you to build an 3d-vector space to help you software get more precise data.

Think about two eyes vs one eye when you are driving.

I think it's little bit waste time to ML one eye data.

PS: do you have a mail list or google group about research?

I was looking for the model and training pipeline from Herald's talk, are they open sourced, where can I find them? https://www.youtube.com/watch?v=7u9sdenbI5M

i know this is a stupid doubt but i googled this and couldnt find an answer, line 20 in autoencoder.py has from utils import load, save. I am getting an error in this line. I tried conda install utils, pip install utils but none of these seem to work. How can i install this utils package. Thanks

fix "ValueError: Shapes are not compatible when training transition model after autoencoder trained" and make the code compatible with recent versions of Keras.

Hello George,

Appreciate that you can share your code. Just got one suggestion, hope it's useful. I know that Keras is quite good and simple, but tensorflow is slow compared with other tools such as mxnet and caffe, even slower than theano. Recently i'm converting to mxnet and found that it can save GPU memory, keep high speed as well as support distributed computation(which i thought would be a trend in future self-driving car), besides, its net-built method is quite like Keras. I'm rewriting your network with mxnet now and will finish it in a couple of weeks. Will send you the speed and memory comparison results after that.

hi, I've read the train_steer_model.py, but I didn't find the relation between that output shape and steer, could you please explain? As far as I see, it still ouput some kind of image, thanks!

model.add(Lambda(lambda x: x/127.5 - 1., input_shape=(ch, row, col), output_shape=(ch, row, col)))

Hi, I am using your dataset to train my own steering angle prediction model. However, since you have not publicly divided the dataset into training set and testing set, I wonder can you specify the dividing criterion so that people who use your data can compare their model in a unified manner. Thanks. Regards, Cardwing

Hi,

first, thank you so much for your Autoencoding beyond pixels using a learned similarity metric code ! I adapted it to my needs, and I when I run it a get an error because of the lines 221-222:

      with tf.control_dependencies(outputs):
        updates = [tf.assign(p, new_p) for (p, new_p) in D.updates]

I get the error:

    updates = [tf.assign(p, new_p) for (p, new_p) in D.updates]
  File "/local/gensoft2/exe/tensorflow/1.0.0-py2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 516, in __iter__
    raise TypeError("'Tensor' object is not iterable.")
TypeError: 'Tensor' object is not iterable.`

Indeed, D.updates - where D is a keras.models.Model instance - is a list of tensors and not a list of tuples (keras 2.0.3). So my questions are:

• What these lines are supposed to do and how should it be corrected ?
• Is there anything I'm missing ?
• Should it just be replaced by:

    with tf.control_dependencies(outputs):
        updates = [tf.assign(p, new_p) for (p, new_p) in D.updates]

Thank in advance !

INFO:main:server started INFO:dask_generator:Loading 9 hdf5 buckets. x 52722 | t 263583 | f 52722 x 58993 | t 294919 | f 58993 x 19731 | t 98719 | f 19731 x 56166 | t 280785 | f 56166 x 25865 | t 129344 | f 25865 x 85296 | t 426596 | f 85296 x 78463 | t 392182 | f 78463 x 30538 | t 152650 | f 30538 x 51691 | t 258571 | f 51691 training on 437158/459465 examples INFO:dask_generator:camera files 9 843.83 ms X (64, 1, 3, 160, 320) angle (64, 1, 1) speed (64, 1, 1)

???

Hey guys,

I'm playing around with your steering model. Will soon equip my old Miata as data acquisition machine, but will be really grateful if you could share some 20-30K steering angle driving data for testing purposes. (I did collect thousand miles data for chffr :) )

i've compared the sha1 checksum to what's specified at https://archive.org/download/comma-dataset/comma-dataset_files.xml

➜  research git:(master) ✗ shasum -a 1 comma-dataset.zip
ae425f5c125a27f3b1f7ef29aeeda7f000ab4b73  comma-dataset.zip

so i've definitely got the right file..

but still when i try to unzip:

➜  research git:(master) ✗ unzip comma-dataset.zip 
Archive:  comma-dataset.zip
warning [comma-dataset.zip]:  43980607958 extra bytes at beginning or within zipfile
  (attempting to process anyway)
error [comma-dataset.zip]:  start of central directory not found;
  zipfile corrupt.
  (please check that you have transferred or created the zipfile in the
  appropriate BINARY mode and that you have compiled UnZip properly)

can anyone tell me what they used?

Hi dear

After I download the comma-dataset , I can not find keys start with 'app_' in the log directory file which you wrote in your log.md. is there something wrong with the comma-data.zip? because I want to find which keys represent the gyro's x,y,z speed . and which keys represent the Accelerometer‘s x,y,z value in the log's h5 file

it will be very appreciate if anyone can answer

thanks weizhen

Is there a shift in the GPS data? When I locate the coordinates using some map pages, the satellite images do not seem to match the images taken by comma ai team camera. Please advise.

Regards,

Riyadh

I found the dataset contains random images, which look like an interior image with charger and monitors. Is the dataset hacked by someone? It is not clean...looks like a serious issue

Environment: Anaconda + Keras 1.0.6 + Tensorflow 1.0.0 (Unable to find TF0.9.0 )

I ran the codes according to the Drive Simulator tutorial: https://github.com/commaai/research/blob/master/DriveSim.md

Everything is OK before the 5th step "Train transition model".

I ran the server for the transition model first, in window 1, and train the transition model in window 2: ./train_generative_model.py transition --batch 64 --name transition

Window 1 (ran the server code:./server.py --time 60 --batch 64) outputs the above error from time to time. As is shown in the screen shot:

Could anyone help me with this issue? Best Regards! ：）