HOW TESLA MADE SELF-DRIVING CARS? | Explained in Details
In this article we will share with you how Tesla, the world leader in electronic vehicles, is using artificial intelligence. As a company, Tesla is much more of a technology company than a traditional car manufacturer.
And this is why they’re doing so well. They’re leveraging things like artificial intelligence and everything they’re doing. And one of the key goals for Tesla is to make their cars autonomous. And obviously we use artificial intelligence to make this happen.
How Artificial Intelligence work
So the car will interpret images from the machine vision cameras and the sensors around them to be able to drive by itself. In order to do this, we need to train those algorithms. And we need to collect the right data to help train these algorithms and these AIs.
So what Tesla is doing very well is it’s basically crowdsourcing all of this data from the one million plus vehicles that are now on the roads or the Tesla cars. And what it does is it will monitor using lots of sensors inside and externally to the car to monitor what the car is doing, but also what the driver is doing.
What Tesla Do ?
Even where we’re touching the steering wheel, what we’re touching inside the car. To collect all of this data and to then learn from it. And their approach is basically what they term imitation learning. Where we have these algorithms that basically learn from what collectively all these millions of drivers around the world are doing and how they’re reacting to things. And there are other car companies that use synthetic data, for example, or they use video game data from things like Grand Theft Auto.
So they want to be able to recognize all of these things. objects. They’re training their algorithms to make better predictions, so really understand and anticipate next movements of pedestrians and cyclists and what other cars might be doing. And then it wants to use this to plan their own planning and their own route to decide what should we do, should we stay in line in the lane, should we overtake cars, should we stop now and so on. And this is why Tesla has such a competitive advantage when it comes to self -driving cars at the moment, because it has this data and has the ability to use this data very effectively to improve the potential of self -driving.
But it is also going far beyond this. Tesla is a company that now tries to use artificial intelligence in every part of their organization, so they’re using this now in their charging processes, they’re using this in their solar energy distribution and so on. So a great case study of a company that has really had a vision that actually data and AI will be the key competitive forces for any company in the future.
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FAQ
Tesla collects and analyzes data from sensors and cameras on their vehicles to improve performance and safety. Their AI systems identify and predict potential hazards on the road, such as other vehicles, pedestrians, and obstacles.
Tesla’s Autopilot system uses AI for features like adaptive cruise control, lane-keeping, and automatic lane changes. The AI algorithms process real-time data to assist drivers and enhance safety.
Tesla’s approach involves imitation learning. Their algorithms learn from the decisions and movements of millions of actual drivers worldwide. This data translates into smart autonomous cars with sophisticated tracking systems.
Tesla initially collaborated with NVIDIA to optimize AI-integrated chips. Later, they developed their own chips for AI inference and training, focusing on performance, efficiency, and redundancy.
Tesla employs AI, big data, and machine learning to design and optimize electric propulsion systems. They collect, store, analyze, and visualize data to enhance vehicle performance.
Tesla aims to create a general-purpose, bipedal, autonomous humanoid robot capable of performing unsafe, repetitive, or boring tasks. This ambitious goal involves developing software stacks for balance, navigation, perception, and interaction with the physical world.
The FSD (Full Self-Driving) Chip runs Tesla’s self-driving software, while the Dojo Chip powers their AI training system. Both chips focus on performance, efficiency, and scalability.
Tesla applies cutting-edge research to train deep neural networks for tasks like perception (semantic segmentation, object detection) and control. These networks analyze raw images and output critical information for autonomous driving.
Tesla is designing and building the next-generation machine learning compute system called Dojo. It will handle massive training workloads using their extensive datasets and aims to make AI accessible to the masses.
Tesla’s fleet learning approach leverages AI to deploy training workloads across their vast vehicle fleet. This continuous learning process improves their autonomous driving capabilities over time.
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