Skip to main content

what is self driving car



A self-driving car (sometimes called an autonomous car or driverless car) is a vehicle that uses a combination of sensors, cameras, radar and artificial intelligence (AI) to travel between destinations without a human operator. To qualify as fully autonomous, a vehicle must be able to navigate without human intervention to a predetermined destination over roads that have not been adapted for its use.

Companies developing and/or testing autonomous cars include Audi, BMW, Ford, Google, General Motors, Tesla, Volkswagen and Volvo. Google's test involved a fleet of self-driving cars -- including Toyota Prii and an Audi TT -- navigating over 140,000 miles of California streets and highways.
AI technologies power self-driving car systems. Developers of self-driving cars use vast amounts of data from image recognition systems, along with machine learning and neural networks, to build systems that can drive autonomously.
The neural networks identify patterns in the data, which is fed to the machine learning algorithms. That data includes images from cameras on self-driving cars from which the neural network learns to identify traffic lights, trees, curbs, pedestrians, street signs and other parts of any given driving environment.
For example, Google's self-driving car project, called Waymo, uses a mix of sensors, Lidar (light detection and ranging -- a technology similar to radar) and cameras and combines all of the data those systems generate to identify everything around the vehicle and predict what those objects might do next. This happens in fractions of a second. Maturity is important for these systems. The more the system drives, the more data it can incorporate into its deep learning algorithms, enabling it to make more nuanced driving choices.

Comments

Popular posts from this blog

ANN in depth

Artificial Neural Network Layers Artificial Neural network is typically organized in layers. Layers are being made up of many interconnected ‘nodes’ which contain an  ‘activation function’.  A neural network may contain the following 3 layers: a. Input layer The purpose of the input layer is to receive as input the values of the explanatory attributes for each observation. Usually, the number of input nodes in an input layer is equal to the number of explanatory variables. ‘input layer’ presents the patterns to the network, which communicates to one or more ‘hidden layers’. The nodes of the input layer are passive, meaning they do not change the data. They receive a single value on their input and duplicate the value to their many outputs. From the input layer, it duplicates each value and sent to all the hidden nodes. b. Hidden layer The  Hidden layers  apply given transformations to the input values inside the network. In this, incoming arcs that go...

5 machine learning applications

Artificial Intelligence (AI) is everywhere. Possibility is that you are using it in one way or the other and you don’t even know about it. One of the popular applications of AI is Machine Learning (ML), in which computers, software, and devices perform via cognition (very similar to human brain). Herein, we share few examples of machine learning that we use everyday and perhaps have no idea that they are driven by ML. 1. Virtual Personal Assistants Siri, Alexa, Google Now are some of the popular examples of virtual personal assistants. As the name suggests, they assist in finding information, when asked over voice. All you need to do is activate them and ask “What is my schedule for today?”, “What are the flights from Germany to London”, or similar questions. For answering, your personal assistant looks out for the information, recalls your related queries, or send a command to other resources (like phone apps) to collect info. You can even instruct assistants for certain tasks l...

What is nlp and computer vision

Some of the most common application areas of AI include natural language processing, speech, and computer vision. Now, let's look at each of these in turn. Humans have the most advanced method of communication which is known as natural language. While humans can use computers to send voice and text messages to each other, computers do not innately know how to process natural language. Natural language processing is a subset of artificial intelligence that enables computers to understand the meaning of human language. Natural language processing uses machine learning and deep learning algorithms to discern a word semantic meaning. It does this by deconstructing sentences grammatically, relationally, and structurally and understanding the context of use. For instance, based on the context of a conversation, NLP can determine if the word "Cloud" is a reference to cloud computing or the mass of condensed water vapor floating in the sky.  NLP systems might also be a...