AI and computer learning is quickly gaining use, so what happens when AI becomes commonplace? Before we dive into this it is important to understand what AI can and can't do today and what aspect of it is already common. Computer learning is a subset of AI, but the two are often discussed together or interchanged. Computer learning is a method where a computer is trained on a set of data and then uses that training to learn a task. Facial feature recognition is a common computer learning task where the computer is trained to recognize the various features (eyes, lips, nose and mouth) of anyone's face.
In order to decipher these complex situations, autonomous vehicle developers are turning to artificial neural networks. In place of traditional programming, the network is given a set of inputs and a target output (in this case, the inputs being image data and the output being a particular class of object). The process of training a neural network for semantic segmentation involves feeding it numerous sets of training data with labels to identify key elements, such as cars or pedestrians. Machine learning is already employed for semantic segmentation in driver assistance systems, such as autonomous emergency braking, though.
Let me cut to the chase: below's a video of my fully-autonomous car driving around in a virtual testing environment. To train that software, SDCs must drive for thousands of hours and millions of miles on the road to accumulate enough information to learn how to handle both usual road situations, as well as unusual ones (such as when a woman in an electric wheelchair chases a duck with a broom in the middle of the road). To save on the incredibly expensive training (that requires thousands of hours of safety drivers plus the safety risks of having a training vehicle on public roads), SDC developers turn to virtual environments to train their cars. To train the deep learning algorithm, I'll drive a car with sensors drives around a track in simulator a few times (think: any car racing video game), and record the images that the sensors (in this case, cameras) "see" inside the simulator.
Basically, machine learning uses algorithms that iteratively learn from data, meaning that it enables computers to find hidden insights without being explicitly programmed where to look. For starters, it is applicable to healthcare, as machine learning algorithms can process more information and spot more patterns than humans can, by several orders of magnitude. It should be obvious then that driverless cars will require an immense amount of data gathering and analysis; they will also need to connect to cloud-based traffic and navigation services, and will draw on leading technologies in sensors, displays, on-board and off-board computing, in-vehicle operating systems, wireless and in-vehicle data communication, analytics, speech recognition and content management. The IoT and machine learning look set to fundamentally alter the way our world works – in a manner that is exactly the opposite of a killer robot from the future.
Basically, machine learning uses algorithms that iteratively learn from data, meaning that it enables computers to find hidden insights without being explicitly programmed where to look. However, where data mining extracts information for human comprehension, machine learning uses it to detect patterns in data and to adjust its program actions accordingly. Incredibly, it's a science that is not new; it is one that was, in fact, predicted nearly 70 years ago by Alan Turing, widely considered the father of theoretical computer science and artificial intelligence. For starters, it is applicable to healthcare, as machine learning algorithms can process more information and spot more patterns than humans can, by several orders of magnitude.
The Sensor Acquisition Module, for example, is responsible for communicating and receiving data from sensors, while the Main Module will make predictions and make decisions to send to the engine's control module. This experimental system includes 4 main modules: The Sensor Acquisition Module, the Vision Module, the Occupant-System Communication Module and the Artificial Perception Operation Module. Data from the Sensor Acquisition Module and Vision Module is then transmitted through a communication network to an Artificial Perception Operation Module, which is a powerful computer with an intelligent software capable of predicting the behavior of surrounding objects. Also using object detection based on reflection technique; however, radar sensors use electromagnetic waves to scan objects.
Here started work building HD maps back in 2013, according to Sanjay Sood, the company's VP for highly automated driving. "Starting last year, we're essentially building the road network in order to have this map available for the first fleets of cars that are going to be leveraging this technology that are going to be showing up on the roads around 2020," said Sood. But a more scalable solution involves leveraging the embedded sensors in cars already using HD maps to navigate. "Here's adoption of our deep learning technology for their cloud-to-car mapping system will accelerate automakers' ability to deploy self-driving vehicles."
Meeting these requirements is somewhat problematic through the current centralized, cloud-based model powering IoT systems, but can be made possible through fog computing, a decentralized architectural pattern that brings computing resources and application services closer to the edge, the most logical and efficient spot in the continuum between the data source and the cloud. Fog computing reduces the amount of data that is transferred to the cloud for processing and analysis, while also improving security, a major concern in the IoT industry. IoT nodes are closer to the action, but for the moment, they do not have the computing and storage resources to perform analytics and machine learning tasks. An example is Cisco's recent acquisition of IoT analytics company ParStream and IoT platform provider Jasper, which will enable the network giant to embed better computing capabilities into its networking gear and grab a bigger share of the enterprise IoT market, where fog computing is most crucial.
Over the past few years, machine learning and AI have pushed forward the capacity of computers to recognize images, understand context, and make decisions. A report from IHS Technology expects that the number of AI systems in vehicles will jump from 7 million in 2015 to 122 million by 2025, bringing new opportunities to enhance the capabilities of connected cars as more data becomes available. In addition, AI will push advanced driver assistance systems (ADAS) into the mainstream. For that, they need AI, which is what enables the camera-based machine vision systems, radar-based detection units, driver condition evaluation and sensor fusion engine control units (ECU) that make autonomous vehicles work.
Reed says IOx with FogDirector is a rapidly evolving platform, already capable of doing orchestration of all edge devices and acting as the DevOps layer for edge processing gateways. Most are just setting up their infra at present and working to get data collection flowing alongside complex event processing that can track which data to store, which to act on immediately, and which to discard. Tarik Hammadou, CEO and co-founder at VIMOC Technologies has built both hardware (VIMOC's neuBox that has both sensors and a compute layer included), and a hardware-agnostic software platform that operates at the cloud level where applications can be built and connected via API to sensors and gateways. VIMOC's sensors and platform have been taken up by parking garages to optimize parking spaces and already Hammadou has introduced deep learning algorithms on the gateway to better understand the sensor readings being collected.