In a move described as the "moon landing" of the automotive world, Tesla has officially launched its fleet of "Robotaxi" vehicles in several US cities, operating without safety supervisors behind the wheel. This event represents the culmination of a decade of research in artificial intelligence and deep neural networks. It's no longer about assisting the driver, but rather about a fully integrated system capable of perceiving its surroundings and reacting to unexpected traffic situations with an accuracy that often surpasses human capabilities.
The technology behind the revolution: Tesla's latest Full Self-Driving (FSD) system relies on advanced "Dojo" processors that process massive amounts of data from millions of cars on the road. The article explains how Tesla has completely abandoned radar and LiDAR in favor of a system that relies solely on cameras and computer vision, mimicking how humans see the road, but with a 360-degree field of view and the ability to see in harsh weather conditions thanks to advanced image processing technologies.
The economic and social implications: The launch of "Robotaxi" will reshape urban economies. The cost of a trip in a self-driving car is expected to be significantly lower than owning a private car or using traditional transportation services. This shift presents major challenges for the insurance sector, car rental companies, and even urban planning. If cars are constantly moving to transport passengers, will we still need vast parking lots in city centers? The article also discusses the fate of millions of drivers working in the transportation and ride-hailing sectors, and proposed solutions for their retraining in the face of technological dominance.
Safety and Regulation: Despite the technological success, legal questions remain: Who is responsible in the event of an accident? And how will regulatory bodies deal with algorithms that make life-or-death decisions in fractions of a second? Tesla claims its data shows its cars are 10 times safer than the average human driver, but convincing the public and policymakers requires complete transparency and more real-world testing in diverse and complex environments.
