The algorithms used in self-driving vehicles belong to the family of what we refer to as ‘machine learning’ algorithms. But that is a broad term. Machine learning can mean anything like forecasting the weather based on historical weather data.
Machine learning can be loosely defined as the ability of a machine (primarily a computer) to accomplish a task effectively by learning through repetitions. This is similar to how humans learn. We act based on some preliminary knowledge, see the results, contemplate on whether those results were what we were looking for, and then reassess our actions accordingly. Over time, we keep getting better at performing our tasks because we are learning from our mistakes.
Similarly, machines can also be trained to perform certain tasks more efficiently by implementing what we call ‘machine learning algorithms. Machine learning algorithms can be broadly divided into three types, namely, supervised learning, unsupervised learning, and reinforcement learning.
Autonomous vehicles runs on different hardware and software which rely on a host of sensors to plot their trajectory and avoid accidents.these are the list briefly below.
GPS
Accurate to within 1.9 meters, it pinpoints the macro location of the car; combined with readings from tachometers, altimeters and gyroscopes it provides more precise positioning.
LIDAR ( Light detection and ranging )
Ranging system comprising 64 lasers emails pulses of light to take in 360 degree, view of surroundings, identifying nearby objects with an accuracy of up to 2 cm.
RADAR
Detects obstructions in car's blind spots; serves as accident prevention system that triggers alerts when they detect something in blinds spot.Can work in all weather but cannot differentiate objects.
STEREO VISION
Two windshield mounted cameras create real-time 3D image of road ahead, looking for potential hazards like pedestrians and animals.
INFRARED CAMERA
Infrared beams emitted from headlamps and picked up by camera extend vision for night driving, The signature of the infrared beam is detected by a camera, which displays an illuminated image in dashboard display.
LANE GUIDANCE
Cameras mounted behind rear-view mirror focus on lane markings, spotting the contrast and distinguish between road surface and boundary lines.
WHEEL ENCODER
Sensors on wheels take measurements of car velocity as it drives and maneuvers through road traffic.
ULTRASONIC SENSORS
Track and measure positions of objects very close to the car like curbs and sidewalks, as well as other cars when parking.
CENTRAL COMPUTER
Analyzes all information from the sensors; processes and translates data on the spot to control and adjust steering accelerating and braking in response to real time driving conditions.
The central computer learns with every decision it makes and gets better and better the more the car is driven. The computer also requires a high-quality and low-latency connection to the Internet, ideally using 5G technology.
However, as far as the technology behind autonomous vehicles is concerned, the self-driving car is most definitely the next step in the evolution of the automobile industry. Experts currently put the estimate at the year 2050 in order to see a massive reduction of up to 90% in road accidents caused as a result of driving.
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