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March 22, 2024

AI in Self-Driving Cars: Functionality and Real-World Examples

Author:




Edwin Lisowski

CSO & Co-Founder


Reading time:




7 minutes


Until recently, self-driving cars were just an ambitious idea, unlikely to happen. Today, it’s our reality. Of course, they are still not a common sight on the roads, but the technology is ready to use. Self-driving cars have become possible thanks to a number of AI-related technologies, primarily deep learning and computer vision. What do you need to know about AI  in self-driving cars?

AI in self-driving cars
First off, let’s talk about the basics. Artificial intelligence has been present in modern cars for some time now. It has all started with smart driver’s assistants that monitor the vehicle’s surroundings and support the driver or alert them in case of an emergency or an accident risk. Such systems allow drivers to benefit from such solutions as:

  • Emergency braking: For example, Volvo’s CWAB system can use full braking power to stop the car and avoid the accident.
  • Night vision: These systems use thermographic cameras to increase the seeing distance in darkness or poor weather conditions. Mercedes offers such a solution.
  • Enhanced communication and alerts: Voice commands, weather alerts, etc.
  • Lane control: The vehicle alerts you when you drive off your lane. BMW offers such assistant.

But that’s just the beginning. We are all used to these intelligent assistants, as they’ve become common, especially in top-of-the-line cars. Let’s go further because today, we have everything we need to put artificial intelligence in self-driving cars, making them 100% autonomous. Your company can benefit from this technology as well!

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AI in self-driving cars – how it’s used

Self-driving cars have become possible primarily thanks to computer vision and deep learning. CV uses high-resolution cameras and lidars that detect what happens in the car’s immediate surroundings. As a result, car systems can react to possible obstacles and avoid accidents. Of course, CV is not enough. You also have to teach car systems how to drive according to traffic rules. And this is where machine learning, backed up by deep learning, steps into the game.

artificial intelligence in self-driving cars
Deep learning is one of the most advanced AI technologies that works similarly to the human brain. Every piece of data (concerning self-driving cars, we talk about data received by the vehicle’s sensors) goes through the multi-layered neural network, enabling analyzing images in a much more comprehensive way. This solution allows carmakers to achieve a much higher level of complexity and accuracy. In effect, self-driving cars are really smart and can operate even in congested cities.

Benefits of self-driving cars with AI

Self-driving cars are a huge milestone not just from the technological standpoint but also from the operational point of view. You see, these vehicles have everything it takes to make our everyday work accelerated and facilitated.
With self-driving cars:

  • Companies running them can save time and money (e.g., drivers can focus on more complicated work) and even operate 24/7, all year round.
  • The number of accidents decreases (AI algorithms are never tired, intoxicated, or sleepy)

Of course, we’re not saying here that autonomous vehicles are already in common use. This is still a project in the making, partly due to legal regulations in many countries, which forbid autonomous vehicles from driving on public roads. However, it’s just a temporary complication. As technology is growing and becoming more prevalent, the law will have to keep up with these changes.

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Artificial intelligence in self-driving cars: examples

To show you that this technology is at hand, we’ve chosen three tremendous examples of autonomous vehicles with AI n action. For the sake of this article, we focused strictly on cars. Therefore, there are no drones and other self-steering vehicles/devices. Here we go:

Waymo

It’s a US-based company that’s working on the world’s first autonomous ride-hailing service and autonomous trucking and local delivery solutions. They want to develop an autonomous driving system that’s capable of replacing the human driver altogether. Such a system could be applied both in passenger cars and trucks. Waymo based their solution on a network of radars, lidars, and cameras. Waymo’s cars have already driven over 20 billion miles both in the real world and in simulations.

Their systems are capable of detecting:

  • Other vehicles
  • Cyclists
  • Pedestrians
  • Other obstacles

Waymo- self-driving car, AI Example
Today, Waymo works with such car makers as Jaguar, Volvo, and Daimler Trucks in order to develop their solutions even further and put them in other vehicles.

BMW

That’s another company working on AI self driving cars. Did you know that it was in 2015 when for the very first time, a BMW car (i3 to be exact) parked itself in a parking garage? Three years later, BMW opened their Autonomous Driving Campus, where they are working on self-driving vehicles. This campus allows BMW to keep all the research and development within one facility, making their work quicker.

If you’d like to see BMW’s autonomous vehicles, take a look at this video:

Aurrigo

In June 2021, official information was released that autonomous buses produced by the Aurrigo company will be operating on the streets of British Cambridge. These vehicles are currently in the final testing phase, and soon users will be able to use public transport, the operation of which will be based on AI and ecology. You can see Aurrigo’s buses in action even today.

Just play this video:

Are you interested in the implementation of Artificial Intelligence in your company? Just give us a call! We are always vitally interested in the cooperation with companies wanting to start a new phase of their development – with machine learning and Artificial Intelligence.

Find out more now: Artificial intelligence consulting.

 

Artificial Intelligence in Self-Driving Cars: FAQ

What are some examples of data sets used in self driving cars?

Here are the types of data used by engineers for self-driving cars:

  • Lidar point clouds: Create 3D maps of the surroundings by measuring laser reflections.
  • RGB camera images: Use vehicle-mounted cameras to take pictures for object detection.
  • Radar data: Detects objects, distance, and speed.
  • GPS and IMU data: GPS for location and IMU for measuring movement and turns.
  • Vehicle control and sensor data: Information from the car’s controls for understanding interaction with the environment.

Can self driving car be hacked?

Yes, they can be hacked because they use complex technology. It is recommended car makers protect against different hacking methods, such as tricking the car’s sensors or attacking the car’s software. Also, the cars need regular software checks to prevent hacking.

What AI algorithms are used in self driving cars?

AI algorithms used in self-driving cars include supervised learning algorithms for tasks like object and lane detection, and unsupervised learning algorithms for tasks such as anomaly detection and clustering. Some popular machine learning algorithms employed in self-driving cars are AdaBoost for data classification, TextonBoost for object recognition, Histogram of Oriented Gradients (HOG) for detecting objects like pedestrians and vehicles, and YOLO (You Only Look Once) for real-time object detection.

Are self driving cars safe?

Self-driving cars have the potential to be safer than human drivers due to their ability to eliminate human error and reduce accidents and fatalities. These vehicles are equipped with sensors, cameras, and advanced driver assistance systems (ADAS) like emergency braking, lane-keeping assistance, and blind-spot warning systems to enhance safety. While self-driving technology is still evolving, efforts are being made to ensure the safety implications of autonomous vehicles are thoroughly considered, tested, and studied to prioritize safety as the number one priority. Despite occasional incidents and challenges faced by self-driving cars, studies show that they have the potential to improve urban mobility, reduce traffic fatalities, and enhance overall road safety.

The article is an updated version of the publication from Jul. 16, 2021.



Category:


Computer Vision

Artificial Intelligence