The IT giants – Google and Tesla – have made deep inroads in artificial intelligence. Their efforts have made several futuristic concepts of real-world realities. Among these advancements is the self-driving car.
To classify self-driving cars, the Society of Automotive Engineers has proposed a six-level categorization scheme.
Zeroed Level: Driver Only
In this level, the human operator holds complete control of the car (manual transmission).
First Level: Assisted
Modern-cars are part of this level in which there are features like cruise-control and anti-lock brakes that minimizes the manual efforts.
Second Level: Partial Automation
In this level, the control of the car is relinquished to the system for a limited number of scenarios. However, the driver must keep track of the ride. This type of automation can be used on the highways where the car can self-drive while you can keep an eye on the road.
Third Level: Conditional Automation
This level does not necessitate that the driver must monitor the system 24/7. However, in case of a sudden need, it must immediately transfer the control to a human operator. Therefore, at this level, you don’t have to touch your steering wheel, but you must be close to it. So, you can quickly take control in case of an unexpected situation.
Fourth Level: High Automation
If your car can drive you to the parking lot all by itself, this means that you have reached the fourth level. In this level, it is not necessary to have a human operator to drive for any specific use case.
Fifth Level: Full Automation
This is the final level in which the system must be self-sufficient enough to tackle all types of situations, including emergencies. There is no requirement to have a human operator to control the car at any point, i.e. there is no option for you to take control of the vehicle.
Technology for Self-Driving Cars
Sensors are an essential part of the equation. For example, Google is using LIDAR (Light Detection and Ranging) technology for its self-driving cars. LIDAR is a remote sensing procedure wherein a pulsed laser of light is used to determine distances that are near the car’s surrounding environment.
Additionally, a wide range of software techniques and algorithms are needed for self-driving cars. For example, convolutional neural networks of deep learning are required to detect obstacles, traffic lights, and scenes.
