From ‘impossible’ to ‘inevitable’, autonomous driving has taken a huge leap lately. With the boom in artificial intelligence, automotive driving has become a hot-button issue in recent times. This became possible in December 2018, when Waymo, a company that emerged from Google’s project. This project is about self-driven cars. They officially came up with its own self-driven car service. It was after this project that it made it possible for humans to invest in self-driven cars. From the very process of manufacturing cars with AI-driven robots to the making of the very own autonomous vehicle, AI has played a major role in every step.
The launch of automated cars is only the beginning as Waymo plans to expand the availability and capability of its services over time. Significantly smaller start-ups such as May Mobility and Drive.ai are working towards starting a self-driven shuttle service in suburban areas of a city. The concept of auto-driven cars is also under the radar of ride-hailing companies such as Lyft and Uber as it would be a profit-driven idea to dismiss their drivers who are shuttling around their customers. Tech giant companies such as Apple, IBM, and Intel are also looking along the same lines in the automobile field.
By one account, it has been found that driver-less cars would add about $7 trillion to the global economy while simultaneously saving thousands of lives from accidents. This may seem like a boon at one hand but the sad truth is that this might cost the jobs of people working as taxi-drivers, truckers etc. Some may prosper from automated cars but a significant number would be left behind.
The First Self Driven Cars
The first of autonomous cars were seen at an abandoned Air Force base outside Los Angeles. Darpa, which is one of Pentagon Skunkwork’s arm had launched its driver-less cars amidst real teal traffic in a competition for autonomous cars, named ‘Urban Challenge’. In the past years, America’s military industry had been experimenting with autonomous trucks but had always failed to come up with one which could match the realistic measure of speed and driving through hazards. Such failed attempts could not deter the spirit of the innovators. Progress in this field was seen in 2004 Grand Challenge and the fastest of all vehicles there made it past 7 miles of sand of the Mojave desert. This team made a come back in the year 2005 with more advancements to their vehicle which finally proved that autonomous driving was possible in reality.
Later in the year 2009, Google hired some veteran engineers of the DARPA Challenge to build its own autonomous car. This car was built and successfully tested in the busiest streets of the USA including San Francisco’s Lombard Street. Later tech-dad Elon Musk had announced that Tesla would be looking into designing self-driven systems that could help in boosting the services of companies such as Uber and Lyft. In the following years, Nissan, Ford, General Motors, and Tesla started investing heavily in the research and development of autonomous cars.
Basic Idea Behind The Working Of The Self-Driven Cars
One idea which is commonly stated is the introduction of the ‘robo-cars’. A robo-car includes a robot that would be sitting on the driver’s seat and driving the car. Cameras could be used for spotting things on the lane, tracking the highway, and collecting data for the objects that lie on the route. Machine learning is a great technique that can help in recognizing the objects on the path and navigate accordingly.
Lidars are laser-based devices used for getting a 3D map around the car. It fires millions of small lasers to detect objects near the car and aids the car to navigate accordingly. Lidars are considered highly efficient in comparison to 2D cameras when it comes to navigation. The lidars which are available in recent times are extremely expensive and hard to manufacture on a large scale. To mitigate this issue, companies are investing heavily in R&D of cost-effective lidars. Machine Learning is a technique that can be of great help when it comes to learning from the past routes and applying it in the future for better navigation. It is crucial that a special mapping of the entire area of functionality is done before the robo-car takes over the streets. Since the early 1900’s radars are used in vehicles for detecting big nearby metallic objects for safe way-finding. Elon Musk has introduced the idea of an underground tunnel transportation system to mitigate ground-level traffic. The tech-dad has practically founded an underground tunnel in California which is currently navigated by self-driven cars. This tunnel is a miniature sample of the giant multi-level underground transportation system Musk plans to implement.
The Future Of Autonomous Vehicles
When we talk about autonomous vehicles taking over the streets on a mass scale, we need to understand there is a whole system that needs to be put in place. Mass scale usage of autonomous vehicles does not only require technology but a cooperative functioning system to be in place. This system may involve compatible road networks, traffic lights, road signals, new insurance rules, a new set of traffic rules, etc. Chip-making companies such as Intel, Nvidia and Qualcomm are working towards introducing supercomputers for autonomous driving. Companies such as Tesla are working towards building their own chips for the same. Looking at the advancements made in this area till date, one can surely say that the future has a lot of exciting innovations in store for us.
