Last week, we open sourced an advanced driver assistance system in order to help accelerate the future of self driving cars and provide a platform anyone can build on top of. We released both openpilot, driving agent research software, and NEO, a robotics platform capable of running openpilot, under the MIT license.
openpilot is an open source adaptive cruise control and lane keeping assist system, both safety features available on modern cars. We would like to build the best ones on the market, and help you retrofit them to existing cars.
NEO is an open source robotics research platform. It is centered around an Android phone, similar to Android Based Robots. The modern smartphone is an incredible platform packed with sensors and processing power. NEO also includes a cooling solution and a CAN interface board. CAN is a networking protocol used in cars, trucks, power wheelchairs, golf carts, and many other robotics applications.
With a forthcoming openpilot release, it will become easier for researchers to add support for their own vehicle. On older cars, some actuators may be harder to control than others, but it should be very possible to control the gas electronically to have a gas only adaptive cruise control. It's also possible for researchers to add mechanical actuators for the controls that cannot be electronically actuated.
Have fun, be safe, and let's usher in the future of self driving cars together.
Thursday, October 20, 2016
The comma one will not turn your car into an autonomous vehicle. It is an advanced driver assistance system. To put it in traditional auto manufacturer terms, it is "lane keep assist" and "adaptive cruise control"
Our supported car, the Honda 2016/17 Civic with Sensing already has these features. But as anyone with the car will tell you, they aren't very good. The comma.ai system is just much better. It provides no new functionality, so it should be legal everywhere the Honda systems are; it is an aftermarket upgrade.
With all these Tesla autopilot like systems, it is very important that you pay attention. This system does not remove any of the driver's responsibilities from the task of driving. We provide two safety guarantees:
1. Enforced disengagements. Step on either pedal or press the cancel button to retake full manual control of the car immediately.
2. Actuation limits. While the system is engaged, the actuators are constrained to operate within reasonable limits; the same limits used by the stock system on the Honda.
At comma.ai, we are working as hard as we can to deliver the best possible user experience. Onward to the launch.
Wednesday, September 14, 2016
Monday, July 11, 2016
Machine learning is eating software. Here at comma.ai we want to build the best machine learning. This makes us all work really hard and sometimes need some stress relief. Our art therapist suggested us to try adult coloring books to relax. They worked so well for us that we decided to share the love with the world and built commacoloring, comma.ai adult coloring books .
commacoloring was really well received and made it to the front page of Product Hunt. We got a lot of feedback from our users (we love users!). A feature was requested to automatically color the easy parts of the image, letting the user focus in the details. We used our self-driving car engineering skills to build a self-coloring book.
We call this new feature Suggestions. You can try right now by clicking the "suggest" button!
Note: you can skip that section without affecting your coloring experience, but if you are familiar with deep learning jargon, please read along.
To automate the coloring process we trained a deep neural network for pixel level semantic parsing, i.e a network that will classify (color) each pixel using information of its surroundings. Given the state of the art, we knew the right approach would be a fully convolutional neural network. We started by trying an encoder-decoder like architecture with 4 convolutions down and 4 deconvolutions up , with one output channel per class. This was taking too long to converge though.
We later noticed that  claims that retraining the encoder network is not really necessary. They used a pre-trained VGG for dense classification in low resolution and bilinear interpolation followed by Conditional Random Fields for upscaling the image back to its desired size. Also  stated that the job of the decoder/deconvolution network is to mainly upscale and smooth the segmented output image and it can be a smaller network. Reddit brought our attention to ReSeg  that uses only the convolutional layers of VGG as the encoder.
Our final solution combined ideas from  and  and used fixed VGG convolutional layers as the encoder and trained a simple deconvolutional network as the decoder. Each layer of our decoder used only 16 filters of 5x5 pixels with upscaling stride of 2. We tried faster upscaling with stride 4 but the results didn't look sharp enough.
In one of our experiments we reinitilized the VGG weights to random values and were still able to learn a successful decoder. We called this architecture Extreme Segmentation Network, since it resembles Extreme Learning Machines. Unfortunately, we were aware that the acronym would compete with Echo-State Networks' and we decided to use the original VGG filters in production. Our final network is called Suggestions Network (SugNet). Some results are shown in Figure 1 and 2.
Figure 1. Input image and self colored Suggestions example.
Figure 2. Sample outputs of the segmentation network after 400 training epochs compared to human colored images.
All our method was implemented with Keras using Tensorflow backend. The VGG image preprocessing used Theano backend. At test time, using Tensorflow only the results didn't match and we doubted our engineering skills for a while before remembering that Theano implements correlation instead of convolution. Here is how to convert convolutional wieghts from Theano to Tensorflow. Keras didn't have a proper deconvolution layer, but we started working on a PR for that.
 Vijay Badrinarayanan, Ankur Handa and Roberto Cipolla "SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling". arXiv:1505.07293
 Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, Alan L. Yuille "Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs". arXiv:1412.7062
 Adam Paszke, Abhishek Chaurasia, Sangpil Kim, Eugenio Culurciello "ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation". arXiv:1606.02147.
 Francesco Visin, Marco Ciccone, Adriana Romero, Kyle Kastner, Kyunghyun Cho, Yoshua Bengio, Matteo Matteucci, Aaron Courville "ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation". arXiv:1511.07053.
We hope that Suggestions will inspire you to build even more fun apps with the open source commacoloring product. Let us know about all the amazing things you build with it.