Month: March 2018

How to ask for a lift to self-driving car????

How to ask for a lift to self-driving car????

I just want to clarify one thing before deep dive inside this question, I am not an expert I am a learner.

This is the question which comes to my mind when I am thinking the self-driving car in a hometown environment. This is just a visionary thought. Implementing self-driving car in India and implementing the same thing in other countries has there own challenges.

I always asked for a lift on road in my college days. I am traveling daily 25KM for college from my hometown.

this is the software engineer who will ask for lift !!hahha

Why this question?

I am exploring little about the self-driving car. Generally, in computer science, we try to mimic human intelligence. In a self-driving car we can say we are cloning drivers behavior, learn some more common patterns and many more things. For this post, I am interested in drivers behavioral cloning, computer vision and machine to machine communication or connected cars.

How car(machine) will understand someone really needs a lift and the person who is asking for lift is a genuine one. For this question, there might be multiple answers. In some cases, some incident or road accident happen How self-driving car will approach to help them? as human do.

So I have two questions

  1. Human asks for a lift.
  2. Car asks for help.(Car to Car help)

Human ask for the lift :-

You are traveling from your workplace to your home when suddenly the car stalls and stops.

You recognize it as an engine problem, but there is no garage for miles ahead. You need to ask for a lift to a self-driving car.

Every human being has a unique approach. Some people are biased toward girls. Isn’t it? What about machines?

Solution:-

With the help Computer vision and NLP, we can achieve a simple solution to detect pedestrian lift posture.

  1. Proper posture.
  2. Give Proper direction which way.
  3. NLP(Natural Language Processing) to find & set the destination path.

There is much more technology required like sensors fusion, google map, GPS and many more.

Car ask for help:-

A self-driving car is one good option to minimize the road accident, but in some cases, some exceptional incident happen or road accident happen. In that case How car will help another car, the way human has an approach to handle the situation and help other peoples if some car accident happen. Or else we can take above example where a car is suddenly stalls and stops and there is no garage.

Solution:-

Car to car communication will be the best one. Car-to-car or vehicle-to-vehicle communication, it lets cars broadcast their position, speed, steering-wheel position, brake status, and other data to other vehicles within a few hundred meters. The other cars can use such information to build a detailed picture of what’s unfolding around them, revealing trouble that even the most careful and alert driver, or the best sensor system, would miss or fail to anticipate.

Due to some problem car stalls or stop, so that car will communicate to another car for towing service or another kind of help. The solution is in a more general way like the car will connect to local hospitals or local police station or generate some alert sound. The car will be enough intelligent to

A problem statement is one but solution may be different based on context, terrain, and ecosystem.

To implement this solution automotive manufacturers need to agree upon communication standards and data privacy.

Let me know your solutions on this or let me help out to understand a problem in more depth.

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Dynamic Computation Graphs(DCG) with Tensorflow Fold!!

Dynamic Computation Graphs(DCG) with Tensorflow Fold!!

Google has introduced a new tool under TensorFlow umbrella i.e TensorFlow Fold.

If you are familiar with the deep learning libraries such as TensorFlow, chainer, theano, caffee and many more. Everyone has a unique approach to building the graph-based computation. But some How almost all machine learning/deep learning frameworks operate on static computation graphs and can’t handle dynamic computation graphs. (PyTorch, Dynet, and Chainer are exceptions).

Tensorflow fold is based on deep Learning with Dynamic Computation Graphs. What an idea!!!

Ref from research.googleblog.com

Why tensorflow fold?

As we already have one beautiful tool suite case tensorflow which is addressing some cool problem. But it has some limitation in terms of dynamic graph computation. Tensorflow uses static graph computation. Batch processing of dynamic graphs is a very common technique for a variety of applications, such as computer vision and natural language processing. However, due to the varieties of type and shapes between distinct data, batch processing with a static graph over such data set is almost impossible with a current tensorflow framework.

Tensorflow fold is not another deep-learning framework. This is the extension to tensorflow that provides a tensorFlow implementation of the dynamic batching algorithm. Dynamic batching is an execution strategy for dynamic computation graphs.

Computations over data-flow graphs is a popular trend for deep learning with neural networks, especially in the field of cheminformatics and understanding natural language. In most frameworks, such as TensorFlow, the graphs are static, which means the batch processing is only available for a set of data with the same type and shape. However, in most original data sets, each data has its own type or shape, which leads to a problem because the neural networks cannot batch these data with a static graph.

To overcome the above problem tensorflow fold has introduced.

Getting started!!!

Fold runs under linux; Python 2.7 and Python3.3 are recommended. Install either using virtualenv or pip.

Please note that Fold requires TensorFlow 1.0; it is not compatible with earlier versions due to breaking API changes.

First install Python, pip, and Virtualenv:

sudo apt-get install python-pip python-dev python-virtualenv
#create virtualenv
virtualenv foo             # for Python 2.7
virtualenv -p python3 foo  # for Python 3.3+
#Activate environment
source ./foo/bin/activate      # if using bash
source ./foo/bin/activate.csh  # if using csh
#  Install the pip package for TensorFlow. For Python 2.7 CPU-only, this will be:
pip install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.0.0rc0-cp27-none-linux_x86_64.whl
#For Python 3.3+ and/or GPU, see here for the full list of available TF binaries.
#Check that TensorFlow can load:
python -c 'import tensorflow'
#  Now install tensoflow fold
#Install the pip package for Fold. For Python 2.7, this will be:
pip install https://storage.googleapis.com/tensorflow_fold/tensorflow_fold-0.0.1-cp27-none-linux_x86_64.whl
#for python 3.3
pip install https://storage.googleapis.com/tensorflow_fold/tensorflow_fold-0.0.1-py3-none-linux_x86_64.whl
#Test is installed successfully or not
python -c 'import tensorflow_fold'

If everything goes well. then test below example.

Next one

  1. Quickstart notebook
  2. Tensorflow fold Documentation
  3. TensorFlow: Concepts, Tools, and Techniques

There are other libraries and framework which are also supporting dynamic graph computation. Tensorflow fold is tensorflow based and it has its own approach to tackle this problem.

In this paper, Google introduced a new algorithm called ‘Dynamic Batching’, and developed a Tensorflow-based library called ‘TensorFlow Fold’, which solved the DCGs problem in both theoretical and empirical fields.
This is the experimental implementations, they proved that their method is effective and more efficient and concise than previous works.

Paper is here for more details.

Moral of the story is tensorflow is not only supporting tensors any more!!!!

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