Autonomous vehicles are considered the future of transportation and mobility. Cars that can make full driving decisions, navigate, choose the best path, and safely transport passengers on their own are a major focus in the automotive industry. However, this is a developing field, and there's a lot of research yet to be done.
One of the key elements in making this vision come true is the utilization of deep learning in autonomous vehicles. Deep learning is responsible for a number of functions that self-driving cars need. Below, we’ll explain what deep learning is and how it will be used in driverless cars.
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What is Deep Learning?
Deep learning belongs to the domain of machine learning and artificial intelligence and is one of their subfields. The core of deep learning is the creation of an artificial neural network that works similarly to the human brain - by handling and transmitting information.
Deep learning functions thanks to an enormous amount of data that it’s being fed. The input information is transformed into the desired output information through the process of training that the model uses. For instance, the model receives images, text, videos, sensor readings, and more and extracts the information that is meaningful and requested.
How is Deep Learning Applied in Autonomous Vehicles?
When we look at the application of deep learning in autonomous vehicles, we can see that it plays a crucial role in the tendency toward creating a fully self-driving car.
There are six levels of autonomous driving that have deep learning powers. Level 0 is the one where the driver has full control, and level 5 is the one where the car can drive alone, no matter the circumstances.
Below, we’ll break down exactly what applications of deep learning are present in autonomous vehicles and what they are responsible for.
1. Object Detection
Safe navigation is only possible if there is precise object detection in the picture. With the help of deep learning, vehicles can detect different obstacles, such as pedestrians, traffic signs, other vehicles, traffic lights, and more.
Convolutional neural networks (CNNs) are the algorithms that are responsible for this function. They are trained to perform different visual perception tasks and use the data to identify and classify the objects that are in the way.
2. Path Planning
The images that are received by the deep learning model need to be segmented so that the model understands where they are and what they are supposed to do. Semantic segmentation is used to identify what each pixel of the image stands for and thus create a fully understandable image for the vehicle.
As a result, the autonomous car understands where it is and manages to plan the best path.
3. On the Road Prediction
Recurrent neural networks (RNNs) are responsible for helping the vehicle understand how other objects might move and behave on the road. Pedestrians, cars, and other participants in the traffic are moving according to certain rules. These algorithms help the vehicle understand and predict its behavior and adjust its own accordingly.
4. Vehicle Control
Another segment of deep learning is reinforcement learning (RL). It is used to train and teach the vehicle what optimal driving behavior it should always utilize. The vehicle learns this through a series of test rides in the actual environment and receiving positive or negative grades for its behavior.
5. Road Detection
Finally, using deep learning, the autonomous car is trained to understand the road it’s on- the markings, labels, and boundaries. This is how the car manages to stay in its lane and safely maneuver.
What is the Future of Autonomous Vehicles?
Even though this field is fast-growing, and all eyes are on creating a fully autonomous level 5 vehicle, most experts agree that it won’t happen before 2035.
Still, the autonomous car market is expected to grow to 62 billion dollars by 2026, and we can expect lower-level vehicles to be launched somewhere by that time. According to the McKinsey Center for Future Mobility analysis, this market is expected to generate between $300 billion and $400 billion by 2035.
Final Thoughts
Deep learning is developing, and experts are working hard to find new solutions and breakthroughs to apply in the automotive industry. While we are still in the developing, testing, and redoing stage, we can see that autonomous vehicles are bound to be our reality soon.