- June 2, 2020
- Posted by:
- Category: Artificial Intelligence
When we talk about Artificial Intelligence, many concepts like voice assistants, computational photography, self-driving cars, drones, autonomous bot targets, chatbots quickly come to mind.
Machine intelligence and human intelligence are going to put many disruptive examples of their experiments in the near future which will leave us jaw-dropping and heads turning. Modern cars of today are much advanced as ever thought and have been equipped with driving assistance systems that enhance safety and are a step towards autonomous driving.
The automobile industry has already seen transitions, where we aren’t just talking about driving aids. Artificial Intelligence is integrated throughout the process of creating vehicles in the use of recognition of drivers, their habits, and other related services.
Covered under the acronym ADAS (Advanced Driver Assistance Systems), these systems range from autonomous emergency braking with pedestrian detection, blind-spot detection or fatigue detection system, object recognition, speed detection, to change alert involuntary and lane, active lane-keeping, lane line detection, rear cross-traffic alert or traffic signal recognition.
Today, Humans have reached that center of the intelligence which couldn’t have been even imagined in the past decades. Thanks to human brains for empowering machines to overperform their creators away.
Overcoming traditional ways, the robust technology implemented probabilistic data-driven techniques that used automated feature extraction based on the pivot, i.e Data.
Keeping Data as the base, let’s see how Artificial Intelligence plays its role in Autonomous Driving.
# Lane Detection
Lane detection is one of the elementary concepts in autonomous driving which centers towards and provides lateral bounds on the movement of vehicles that run on roads with the vision of not humans but machines.
Lane detection gives an idea to vehicles about its curvature and an estimate of its deviation from the center of the lane. The phenomenon completely relies on cameras and their extent of vision.
Color thresholding is the next thing that plays a significant role in lane detection and has an impact on the smooth driving of self-driving cars, buses, or ships. A threshold is applied in overvalues of color channels which helps the vehicles to detect road markings, irrespective of their color.
[How Image Detection is taken care of?
Computer vision technology is used to process the image upfront which outputs lane markings to enhance autonomous driving. It helps to automate visual understanding from a sequence of images, PDFs, videos, etc. leveraging the power of Artificial Intelligence and Machine Learning algorithms.]
Lane maintenance assistants act virtually and are programmed to work differently as per their manufacturing, sharing the common power of
artificial intelligence technology, machine learning, deep learning, etc.
One category of systems can make use of two line-tracking sensors, one on each side of the car, in the front bumper, very close to the ground, that recognize and “follow” the white lines, continuous or discontinuous, of the road.
Another category of systems may opt for a camera placed at the top of the windshield (inside, more or less in front of the rearview mirror) and recognize the lines that mark the lane in which we circulate.
[Nota Bene (NB):
Both in one and the other, a microprocessor is constantly watching that the trajectory of the car is maintained between the two lines (left-right).]
If at any time the driver steps on or exceeds any of the lines, and the indicator is not activated (direction change indicator light) on the corresponding side, the system interprets the change as involuntary (due to an oversight) and acts.
Some systems only warn the driver, usually with a vibration in the seat or on the steering wheel, but a small audible alarm may also sound, as well as a message on the instrument panel digital display.
Other systems are a little more advanced, act on the direction, and rotate the steering wheel slightly in the opposite direction, to correct the drift in the path that causes us to step on the line in question.
[Prefer Reading: “AI Robots and their impact on Human Life.”]
# Object Recognition
This system aims especially to avoid being run over by pedestrians or cyclists. It is similar to the night pedestrian recognition system that I told you about in the previous entry on lighting.
Systems integrated with high-resolution cameras provide a three dimensional (3D) view to the smart machines’ microprocessor to ensure vehicle safety. This stereographic representation distinguishes pedestrians, cars, cyclists, and other opaque objects measure the distance to them, and even predicts their trajectories, thus verifying the intersection of the cars and determining the risk of an accident.
The duty doesn’t end here as the intelligent system further takes preventive measures conducive to avoiding possibility or reducing the consequences. It warns the driver, and may even automatically apply the vehicle’s brakes.
Driver assistance is rendered in many ways and we are now familiar with the different concepts applied to practice in autonomous driving. We have understood that the car sees everything that happens around, but what happens to us, nobody keeps a check on the driver and its actions.
Well, Artificial Intelligence comes at play and pays close heed to what happens inside the vehicle in three distinct scenarios:
- User recognition and identification. The software is able to know which driver is in command and take it into account, adapting the conditions to your preferences. It can also become a vehicle lock mode, allowing driving only to recognized persons.
- Monitor while driving. By observing the driver’s eyes, and the movement of his head, the system is able to know if he is falling asleep or pending other things than the road.
- Control of the entertainment system. Whether with voice recognition or gestures, artificial intelligence mediates the control of things like changing music, maps, or notifications that come to your phone.
For a better and clear understanding, take a look at the visual which explains how AI-powered systems hit the pivot, encouraging driver monitoring and identification to work better using special cameras and infrared sensors for the lack of light
# Speed Detection & Speed Control
A speed control system is becoming more and more common, it helps us to drive more comfortably, and not have to be so worried about the speedometer, so as not to exceed the maximum speed (and avoid a fine ).
It is quite simple if the accelerator is electronic, a small processor is in charge of managing it to circulate constantly at the speed that we program.
Nor should we be scared, if we have to brake (for example unexpectedly, to avoid a range), we step on the brake, the system deactivates and the car brakes, and in the same way, if we have to accelerate, we step on the accelerator and three-fourths of the same.
An evolution of this system has been called adaptive speed control. What it does is continuously maintain the optimal safety distance with the vehicle that precedes us, without having to worry (and thus avoiding the problems that some people have in calculating the necessary distance).
A radar placed on the front bumper or grille measures the distance between our car and the vehicle in front, and according to the speed we carry, the microprocessor calculates what the safety distance should be between the two cars and compares it with the one we have at that time.
If there is not enough distance, then the AI-enabled system acts on the accelerator and decelerates to increase the distance to the necessary. If necessary, you can also act on the brake to further reduce speed and achieve the required safety distance faster.
If the safety distance increases, and it is possible, the car will accelerate again to recover the programmed speed. The most advanced systems also take advantage of the ABS sensors and the stability control, to know the state of adherence of the pavement (and to know for example that it is wet) and thus know that the safety distance must be even greater, and adapt automatically thereto.