Self-driving cars are a hot topic of debate right now, and for good reason.
They may trigger the biggest cultural shift since the Industrial Revolution.
Everyone seems to be getting on board.
In just a few years, driverless cars will likely dominate the automotive sector.
From whispers about Apple’s self-driving car to real applications by Lyft and Uber, the shift is underway.
We created this post to explain how self-driving cars work and the technology behind them.
There’s much to learn about how driverless cars will reshape the automotive scene.
What is a Self-Driving Car?
A self-driving car, also called an autonomous car, uses sensors, cameras, radar, and AI to navigate without a human driver.
To be fully autonomous, a vehicle must navigate to a location across unmodified roads.
Audi, BMW, Ford, Google, General Motors, Tesla, Volkswagen, and Volvo are among the companies developing and/or testing autonomous vehicles.
Read: Are Self-Driving Cars a Possibility in Africa?
Levels of Autonomy in Self-Driving Cars
Self-driving cars advance current Advanced Driver-Assistance Systems (ADAS) by totally eliminating the need for a driver, while still offering vital safety features like steering assistance, automatic braking, and pre-collision alerts.
In actuality, autonomy has different “levels,” which are summarised as follows:
- Level 0: The automated system cannot steer the car, but it may alert the driver to potential hazards.
- Level 1: Control over the vehicle is shared by the driver and the automated system. The majority of vehicles with ADAS include examples of this.
- 2 Level: Although the automated system is capable of taking full control of the car, the driver must be prepared to take over if it misses a possible hazard.
- Level 3: The passenger can safely divert their attention from driving duties when the automated system takes full control of the car, but they must still be able to act if necessary.
- Level 4: Drivers can let the automated system take over completely while securely diverting all of their attention away from driving-related chores. Currently, only particular “geofenced” locations and other highly regulated contexts can use this functionality.
- 5 Level: In all situations, the vehicle’s ADS serves as a virtual chauffeur and handles all of the driving. The human occupants are only ever supposed to be passengers and never the driver.
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How Self-Driving Cars Work
AI powers systems for self-driving cars.
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Start NowTo create systems that can drive independently, self-driving car developers combine massive volumes of data from image recognition systems with machine learning and neural networks.
The machine learning algorithms are given the patterns that the neural networks find in the data.
The neural network uses images from self-driving car cameras to recognize traffic lights, trees, curbs, pedestrians, and street signs.
Google Waymo Self-Driving Car Project
Google’s Waymo project uses sensors, cameras, and lidar to identify everything around the vehicle and predict their movements.
This takes place in brief intervals of time.
These systems must be mature.
As it accumulates more driving data, the system can use deeper learning algorithms to make more sophisticated driving decisions.
The driver (or passenger) chooses where to go.
The car’s computer calculates a route.
A 360-degree Lidar sensor on the roof scans a 60-meter radius, creating a dynamic 3D representation of the surroundings.
A sensor on the left rear tyre tracks sideways motion to determine the car’s location relative to the 3D map.
The front and rear bumpers’ radar systems measure the distances to obstructions.
The AI software in the automobile connects to all the sensors and gathers data from the internal cameras and Google Street View.
The AI uses deep learning to mimic human perception and decision-making processes and directs driver control systems like steering and braking.
To be aware of things like landmarks, traffic signs, and lights in advance, the car’s software consults Google Maps.
A human can take over control of the vehicle using an override function that is accessible.
Read: 10 Real-Life Applications of Machine Learning
8 Astonishing Technologies That Power Google’s Self-Driving Car
Laser range finder
The revolving roof-mounted Lidar camera, a laser range finder, is the brains of Google’s self-driving car.
This camera creates 3D views of things using an array of 64 laser beams, assisting the vehicle in spotting road hazards.
Based on the time it takes for the laser beams to strike and return to the target, this device determines how far an object is from the moving vehicle.
With an impressive 200m range, these powerful lasers can measure distance and produce images of objects.
Front camera for near vision
A camera installed on the windshield handles the car’s ability to “see” things directly in front of it.
The usual suspects are other drivers and pedestrians.
Additionally, the camera gathers data regarding traffic signals and road signs, which the car’s software can then intelligently analyse.
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Get StartedBumper mounted radar
The car can see the cars in front of it and behind it thanks to four radars mounted on the front and rear bumpers.
Since this technology is the same as the adaptive cruise control systems on which our cars are built, most of us are already familiar with it.
The car’s bumper-mounted radar sensor keeps a “digital eye” on the vehicle in front of it.
The software is set up always to maintain a distance of 2-4 seconds (or even greater) from the automobile in front of it.
Therefore, using this technology, the car will automatically accelerate or decelerate depending on the behaviour of the vehicle or driver in front of it.
This technology is used in Google’s self-driving cars to prevent collisions and keep passengers and other drivers safe.
Aerial that reads precise geolocation
Thanks to GPS satellites, an aerial on the back of the car receives information on its precise location.
Together with the sensors, the car’s GPS inertial navigation system helps it locate itself.
However, due to signal alterations and other atmospheric interferences, GPS estimates may be off by several metres.
The GPS data is compared with sensor map data previously obtained from the same spot to reduce uncertainty.
The internal map of the vehicle is updated as it moves to reflect the most recent positional data provided by the sensors.
Ultrasonic sensors on rear wheels
A rear wheel equipped with an ultrasonic sensor aids in tracking the vehicle’s movements and warns it when obstructions are behind it.
Some of the most cutting-edge automobiles on the market today already use these ultrasonic sensors.
Vehicles with automatic “Reverse Park Assist” technology use such sensors to guide the vehicle into confined reverse parking spaces.
These sensors typically turn on while the vehicle is in reverse gear.
Devices within the car
Altimeters, gyroscopes, and tachymeters located inside the vehicle provide extremely accurate positional information by measuring various characteristics.
This provides the car with the data it needs to run safely.
Synergetic combining of sensors
The car’s CPU or in-built software system compiles and analyses all the data collected by these sensors to produce a safe driving environment.
Programmed to interpret common road
The software has been programmed to interpret typical driver indicators and on-road behaviour correctly.
For instance, if a cyclist makes a manoeuvre-indicating motion, the autonomous automobile recognises it and slows down to provide room for the bike to turn.
Predetermined shape and motion descriptors are encoded into the system to aid the automobile in making wise selections.
For example, if a car detects a two-wheeled item and decides that its speed is 10 mph rather than 50 mph, the automobile will immediately assume that the object is a bicycle rather than a motorcycle and will act accordingly.
The car’s central processing unit will run several of these programmes simultaneously, assisting it in making wise decisions on congested highways.
Read: Artificial Intelligence vs Deep Learning
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Get StartedThe Future of Self-Driving Cars
Though technological advances and adaption have been made toward completely autonomous vehicles, there is still a long way to go before driverless automobiles become the norm.
Expect more autonomous vehicles on public highways in the following ten years.
However, it should be a few decades before we see autonomous vehicles in highly populated regions with irregularly marked roads, frequent roadwork, and considerable pedestrian traffic.
Conclusion
Self-driving cars are rapidly transforming the future of transportation.
While technological advancements have brought us closer to fully autonomous vehicles, widespread adoption still faces significant challenges.
The progress we’ve seen suggests that autonomous vehicles will become more common on our roads within the next decade.
However, achieving full integration will take more time, especially in densely populated areas with complex road systems.
As we move forward, continuous innovation and rigorous testing will ensure that self-driving cars can safely and efficiently navigate our evolving urban landscapes.
The journey is ongoing, and the future of transportation is just beginning.
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