Self Driving Cars - how Tesla is using AI to achieve autonomous driving.

The race to perfect autonomous driving has been a marathon, not a sprint. And while many have entered the race, Tesla, under the leadership of Elon Musk, seems to be breaking away from the pack. Here's a closer look at how.

At the beginning of 2023, a video surfaced on YouTube, courtesy of Tesla enthusiast Omar (popularly known as Whole Mars Catalog). The footage showcased a Tesla navigating 31 miles through the dense traffic of Los Angeles, all on its own, using Tesla’s latest Full Self-Driving (FSD) Beta software version 10.69.25.1.

Starting from Omar's garage, the Tesla Model 3 autonomously manoeuvred its way out, seamlessly merged onto the road, and drove to the exact destination set by Omar. The video juxtaposes the real-world view with the Tesla's 360-degree 3D rendered perspective, offering a fascinating insight into the car's perception.

Driving, for most, is an instinctive task. Our brains, almost on autopilot, process a plethora of visual cues, from road signs to the speed of nearby vehicles, and then instruct our limbs to act accordingly. This intricate dance of perception, processing, and action is a testament to the marvel of human cognition.

But replicating this in machines? That's been one of the most formidable engineering challenges of our time. And Tesla's FSD software's prowess in this domain is nothing short of groundbreaking.

While many are aware of autonomous driving, the depth of Tesla's advancements might be surprising to some. Over the years, industry giants like Google and Waymo have leaned on LiDAR (Light Detection and Ranging) sensors, which use light pulses to map surroundings. Think of it as akin to bats using sonar for navigation. However, this approach has its limitations, which might explain why full autonomy remains elusive for many.

Tesla, however, has taken a different route. Drawing inspiration from our evolutionary history, Tesla posits that if humans rely on vision for navigation, so should autonomous systems. After all, our world, especially our roads, is designed for visual interpretation, not for LiDAR or other systems like sonar or radar.

Harnessing the power of cameras and artificial intelligence, Tesla's vision-centric approach captures a 360-degree view of its surroundings. This sophisticated system can distinguish between various vehicles, identify pedestrians, and even discern traffic cones from wheelie bins. More impressively, it can read and interpret traffic signs, from speed limits to roadwork indicators.

But capturing this data is only half the battle. Processing and learning from it is where the magic happens. Just as our eyes are paired with our brain, Tesla pairs its cameras with a cutting-edge neural net computer equipped with the latest FSD software.

Neural net learning mirrors human cognitive development. As we encounter more of the world, we recognise patterns, refining our decisions and reactions over time. This iterative learning process is evident in any skill acquisition, from driving to playing chess.

Tesla's FSD operates similarly. Andrej Karpathy, Tesla's former Senior Director of Artificial Intelligence, highlighted in a 2020 talk that Tesla's vast real-world driving datasets, amounting to billions of kilometres, are instrumental in refining the software. The software, like a human brain, becomes adept at identifying even obscured or vandalised stop signs as it processes more 'edge cases'.

By 2020, Tesla had logged about 5 billion km in Autopilot mode. Fast forward to mid-2022, and that number had skyrocketed to over 50 billion km. With Tesla's fleet expanding rapidly, its dataset and the consequent FSD software enhancements are evolving at an unprecedented rate.

In conclusion, while the journey to perfect autonomous driving continues, Tesla's unique approach, backed by vast datasets and innovative technology, positions it at the forefront of this exciting frontier.

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