[CNMO Technology News]Recently, Tesla Vice President Tao Lin shared Musk’s views on intelligent driving technology on an online social platform. This view focuses on the technical path of combining artificial intelligence, digital neural networks and cameras, and emphasizes its adaptability to road system design logic.
Musk said that smart driving requires learning from human eyes + brain! He believes that the global road is designed for biological vision, and AI neural network + camera is"The choice combination"and also called LiDAR image"Let the eyes emit laser light". This shows that Musk believes that if intelligent driving technology wants to better integrate into the existing transportation ecosystem, it needs to be in line with the underlying architecture of human perception and road interaction.
From a technical perspective, Tesla’s betting on the “AI chip + camera” route is different from the solution that relies on lidar. Lidar uses laser to build a three-dimensional environmental model, which has unique advantages in complex scene perception, but has problems such as high cost and complex data processing.
Tesla intelligent assisted driving display picture
Musk’s solution is to input the image data obtained by the camera, and then directly output vehicle control instructions such as steering, acceleration, braking, etc. through algorithms, making the chip more like a human brain. It does not require high-precision maps and lidars. It can analyze and output control strategies by relying solely on image data input. This solution relies on massive visual data to train neural networks, which is in line with the characteristics of road system “designed for human vision” and can reuse the adaptability of the existing traffic environment to human drivers, lowering the threshold for scene transformation for technology implementation.
However, this solution also faces challenges such as extreme weather visual interference and complex road conditions recognition accuracy. How to balance the robustness of the algorithm and the diversity of scenarios has become a key issue in Tesla’s iteration of intelligent driving technology.
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