FSD 12 End to End Neural network

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Wow, it sounds like the Full Self-Driving Beta version 12 by Tesla is making some significant advancements in autonomous driving technology! Transitioning to neural networks for vehicle control and implementing end-to-end neural nets are definitely cutting-edge developments. The improved driving experience and new autonomous actions like pulling over to the side of the road autonomously are exciting features. It's fascinating to see how Tesla is pushing the boundaries of self-driving technology.

FSD 12 refers to the Full Self-Driving Beta version 12 developed by Tesla. It is an update to Tesla's Full Self-Driving (FSD) software, which aims to achieve autonomous driving capabilities in Tesla vehicles.

FSD Beta v12 introduces significant changes to the software by transitioning from hard-coded programming to the use of neural networks for vehicle control, such as steering, acceleration, and braking. This shift allows for more advanced decision-making capabilities and reduces the dependency on traditional programming methods.

The most notable difference in FSD Beta v12 is the introduction of end-to-end neural nets for vehicle control. This means that the vehicles' controls are now handled by neural networks rather than being coded by programmers. It is the first time Tesla has implemented this approach in its software.

Early reports suggest that FSD Beta v12 provides a more intuitive and human-like driving experience. Users have reported smoother driving with zero interventions, takeovers, or awkward moments. However, it is important to note that FSD Beta v12 alone may not achieve Level 4 or Level 3 autonomy, but it represents a significant step in that direction.

FSD Beta v12 introduces new autonomous actions, such as the ability to pull over to the side of the road autonomously at the end of a drive. This functionality was showcased in a live stream by Elon Musk, demonstrating the software's capability to make decisions beyond simply stopping.

The Tesla Full Self-Driving (FSD) computer, also known as Autopilot Hardware 3.0, is a highly effective neural network processing chip designed for autonomous driving. The chip incorporates advanced technology, including quad-core Cortex-A72 clusters, a Mali-G71 MP12 GPU, neural processing units, and various hardware accelerators. These components work together to provide the necessary processing power for autonomous driving at levels 4 and 5.

The use of 14nm process technology from Samsung ensures efficient performance and power consumption. The inclusion of neural processing units operating at 2 GHz further enhances the chip's ability to handle complex neural network computations required for autonomous driving tasks. Additionally, the support for up to 128-bit LPDDR4 memory allows for fast data processing and storage.

Tesla's decision to maintain the cost of the FSD hardware while significantly improving processing power demonstrates their commitment to advancing autonomous driving technology without significantly increasing the financial burden on consumers. The announcement of a future FSD Computer 2, using 10nm technology and promising three times the power of the current version, shows Tesla's dedication to continuous improvement in their neural network processing capabilities.

Both DeepRouteai and Tesla Autopilot have distinct approaches to autonomous driving technology, each with its own strengths and challenges. DeepRouteai has showcased its Driver 2.0 Level 4 autonomous driving solution successfully in Shenzhen, China, highlighting its capabilities in navigating complex traffic environments. This demonstrates DeepRouteai's focus on advanced technology and successful implementation in real-world scenarios.

On the other hand, Tesla Autopilot has faced criticism and setbacks in its development of self-driving technology, raising concerns about safety and reliability. Despite these challenges, Tesla has a large user base and a strong presence in the autonomous driving market.

In China, domestic players such as Ponyai and Baidu are making significant advancements in autonomous driving technology, particularly in operating robotaxis and adhering to Beijing's regulatory guidelines. China's AI-powered auto industry is at the forefront of innovation and implementation, projecting substantial growth in the intelligent vehicles market.

Ultimately, the approach that proves to be better will depend on various factors such as technological advancements, regulatory compliance, safety standards, reliability, and user acceptance. Both DeepRouteai and Tesla Autopilot have their unique strengths and challenges, and it will be crucial for them to adapt to the evolving landscape of autonomous driving technology to stay competitive in the industry.