Data science behind the future beast!

by Sunil Upreti Digital Marketing Executive (SEO)

Data Science behind the future beast!

The discovery of self-driven cars started with US military invention of unmanned trucks. We have all heard about the Million-Dollar Competition, held in 2004, this was an open competition to create a robot that could drive across the Mojave Desert in California. This marked the birth of Self-driven cars. Lately, over the years, Self-Driven vehicles have become an important approach in understanding the technological developments, and have added knowledge to candidates in various Data Science Institute in Delhi, to make them skilled and knowledgeable about the field.

According to sources, “Intel and Strategy Analytics estimates that the global economy will see a $7 trillion boost from this arising industry ($2 trillion for the US alone) and that the technology will save approximately 600,000 lives by 2045. The caveat here is that some 5 million truckers, cabbies, and other drivers will be put out of work.”

About 5 years later, the US Military open Challenge, Google launched its own first, self-driven car mission. This mission was to create a robot that would cross even the trickiest roads in California with AI algorithms. Tesla, Uber and Lyft were some other companies to start using the techniques and make developments in the past innovations with each step. Many startups started blooming under this umbrella making it a futuristic revolution. Features like blind-spot detection, auto braking, and lane departure warnings are all an outcome of this invention. These giant endeavours were not possible for the tools and techniques of Data Science. Data management like any other field played a very important role in this field.

According to Medium, “To simulate the human brain and its cognitive networks, a basic self-driving car must be equipped with:

* Highly detailed maps of street features (lights, signs, curbs, etc.)

* Sensors such as cameras and LIDAR (similar to radar but uses light to create pulses instead of radio waves) short-distance 3D layout of its surroundings in real-time

* Vehicle-to-vehicle cloud communications

* Sensory inputs into the vehicle’s machine learning algorithms, to predict outcomes based on an enormous volume of data, in order to plan and act.”

The top three features are generally in the models available. Sensors help in providing 3D maps, and if powered with Altimeters and Accelerometers the GPS accuracy increases. Data collected through every ride of the car is used to update the knowledge of the vehicle, it is estimated that self-driven cars can collect up to 1 GB data per second.

Data scientists are therefore are pioneers who work really hard to make these vehicles perfect.

“Perception merges several different sensors to know where the road is and what is the state (type, position, speed) of each obstacle. Localisation uses very specific maps and sensors to understand where the car is in its environment at the centimetre level. Prediction allows the car to anticipate the behaviour of objects in its surrounding. Planning uses the knowledge of the car’s position and obstacles to planning routes to a destination. The application of the law is coded here and the algorithms define waypoints. Control is to develop algorithms to follow the waypoints efficiently.”

Using Machine Learning, Deep Learning, and AI, it is an endless process of collecting and interpreting data. Basically, it is, “to take real-life driving experiences, turn them into programmable information, and train our models to continuously and self-sufficiently improve its understanding of the real world in order to make ‘informed’ decisions.”

Here, image classification and image localisation play a very important role. You must know that these techniques are important to understand Data Science, therefore the Data Science Course in Delhi give enough priorities to this field of study. These two programs work with Convolutional Neural Network (CNN). Training the CNN further allows performing convolution operations on images collected helping in classifying and locating them. The only drawback of this system is that CNN can collect only one image at a given moment.

According to Medium, “There is another technique called Kalman filters to find their position with the highest possible accuracy. Using a non-max suppression algorithm to train the CNN. The results are compared from the CNN for each grid to the actual grid. Non-max suppression is a way to make sure that the algorithm detects each object only once. What non-max suppression does is to clean up multiple detections of one object. A cost function as the area of intersection/area of the union of two grids. The closer the function is to 1, the better our prediction. The grid with the highest IoU (most confident detection within this grid).