Tesla FSD, from full-stack self-development to the future of intelligent driving-EEWORLD

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As the global automotive industry develops towards electrification, intelligence, and networking, intelligent driving technology has become one of the main research and development directions of various automakers. Among the many explorers of autonomous driving technology, Tesla has taken a leading position in the industry with its FSD (Full Self-Driving) system. Since its release, the FSD system has undergone a transformation from external cooperation to full-stack self-development, and has achieved a full-link closed loop in perception, decision-making, and execution. Tesla has demonstrated its super technical integration capabilities from basic chip design to global data closed loops through its developed hardware, software, and computing architecture. The FSD system is not just an autonomous driving system, it also represents Tesla's technical strength in multiple fields such as artificial intelligence, chip design, and big data processing.

Tesla FSD's closed-loop hardware and software architecture

1.1 Development History of Full-Stack Self-Developed Technology

As early as 2013, Tesla began to explore autonomous driving technology, and in October 2014, it cooperated with Mobileye to release the first generation of autonomous driving hardware HW1.0, which was first installed on the Model S. Mobileye provided Tesla with the EyeQ3 smart driving chip based on visual recognition, which supports basic driving assistance functions. However, with the growing demand for autonomous driving technology, Mobileye's technology could not meet Tesla's long-term planning, so in 2016 Tesla turned to cooperate with NVIDIA and launched the HW2.0 system, equipped with the NVIDIA Drive PX2 platform. This platform uses more advanced computing power and perception technology, which further enhances Tesla's autonomous driving capabilities.

Although cooperation with external partners has helped Tesla quickly enter the field of autonomous driving, the iteration speed and integration of hardware are still limited. Therefore, Tesla released the HW3.0 system in 2019 and officially turned to self-developed hardware. This self-developed platform marks Tesla's comprehensive independent control from chips, algorithms to computing power. On this basis, Tesla launched the first fully self-developed FSD chip, and continuously optimized the functions of autonomous driving through OTA (Over-the-Air) upgrades. In 2023, Tesla released FSD V12, becoming the world's first end-to-end autonomous driving system, realizing a closed-loop architecture from perception to decision-making.

Tesla's Autopilot System Development History

1.2 Highly integrated software and hardware architecture

One of the core advantages of Tesla's FSD system is the high integration of its software and hardware architecture. The FSD system integrates the complete chain from perception to execution, covering all levels of autonomous driving. Its system architecture mainly includes three modules: perception layer, regulation and control layer, and execution layer. Each module has been deeply optimized through self-developed algorithms, chips and data platforms.

Perception layer: Tesla uses the BEV (Bird Eye's View) + Transformer architecture to enhance the vehicle's perception of the surrounding environment. By fusing multi-camera data, the FSD system can convert 2D image information into accurate perception of the 3D environment. At the same time, the Occupancy Network further enhances the ability to recognize the motion state of objects in complex scenes, especially in occluded objects and dynamic scenes.

Regulation and control layer: Tesla introduced a neural network-based planning algorithm and Monte Carlo Tree Search in the regulation and control layer to ensure efficient and safe decision-making. The system can not only quickly evaluate all possible driving trajectories, but also make dynamic adjustments based on the real-time road environment.

Execution layer: The FSD system achieves rapid response in decision-making and execution through a highly optimized hardware platform. Tesla's self-developed FSD chip has excellent computing power and can execute the entire process from perception to control in a very short time. This high degree of integration of software and hardware ensures the efficiency, accuracy and stability of Tesla's autonomous driving system.

Tesla's core innovation in FSD technology

2.1 Optimization and Innovation of Perception Algorithms

Perception is one of the core elements of the autonomous driving system, which directly affects the vehicle's understanding and reaction speed to the external environment. The perception module in the Tesla FSD system adopts the HydraNets architecture, which can integrate multiple visual tasks into the same network, thereby achieving multi-task parallel processing. This multi-tasking capability significantly improves the system's perception efficiency, allowing Tesla to achieve high-precision perception at a relatively low hardware cost.

Tesla's visual perception system algorithm uses the HydraNets architecture

In addition, Tesla has introduced Occupancy Network, an upgraded version of HydraNets. Occupancy Network greatly improves the recognition of long-tail obstacles (such as trailers, rocks, etc.) by converting 2D image data from vehicle cameras into 3D space occupancy information. Occupancy Network can not only accurately perceive static objects, but also capture the differences in the motion state of objects, especially in recognizing complex terrain and dynamic objects.

Occupancy Network accurately captures the movement status of two buses in motion

2.2 Planning and decision-making enhancement at the algorithm level

The autonomous driving system not only requires precise perception capabilities, but also needs to make decisions quickly in complex traffic environments. Tesla's FSD system introduces an interactive search framework at the decision-making level. Through hierarchical task decomposition and Monte Carlo tree search, this framework can evaluate all possible driving trajectories in a very short time and select the optimal driving plan. The incremental planning algorithm used by Tesla gradually adds constraints when evaluating each trajectory, thereby optimizing path selection while ensuring system safety.

Especially in complex urban traffic environments, the FSD system can cope with multiple complex scenarios such as multi-lane switching, pedestrian avoidance, road construction, etc. Thanks to Tesla's continuous optimization of the algorithm, the FSD system not only has excellent performance on highways, but also can achieve efficient and safe autonomous driving on city streets.

2.3 Computing power and data engine of self-developed chips

Another core innovation of Tesla FSD comes from its self-developed FSD chip. Tesla introduced its self-developed chip for the first time in the HW3.0 system, which has a powerful computing power of 144 TOPS (Tera Operations Per Second). With the continuous evolution of the FSD system, Tesla's hardware platform has also been significantly improved. The FSD 2.0 chip in the HW4.0 system has achieved a computing power increase of 720 TOPS.

Comparison of different versions of Tesla's autonomous driving hardware platform

The FSD chip adopts a heterogeneous design, integrating multiple core processing units such as CPU, GPU, and neural network accelerator (NNA). This design not only improves the computing performance of the chip, but also enhances safety through a dual redundant system. When the main system fails, the backup system can take over immediately to ensure the safety redundancy of the autonomous driving function. Through this deep combination of software and hardware, Tesla FSD achieves dual optimization of fast calculation and precise control.

Tesla FSD dual-chip system design

In terms of computing power platform, Tesla has also developed its own Dojo supercomputer system to cope with the needs of massive data processing in the FSD system. Through its efficient distributed computing architecture, the Dojo system can process large-scale neural network training tasks in a short period of time, providing powerful computing power support for the algorithm iteration of the FSD system.

FSD’s data closure and intelligent iteration

3.1 Automated Data Labeling and Simulation Training

Another major technical highlight of Tesla's FSD system is its efficient data closed-loop system. The performance of the autonomous driving system depends largely on massive amounts of training data, and how to efficiently obtain, label, and process this data is a major challenge facing autonomous driving companies. Tesla has successfully achieved data closed-loop optimization through an automatic labeling system and simulation training.

The automatic labeling system can label more than 10,000 driving trips in just 12 hours, which is equivalent to 5 million hours of manual labeling work. Tesla's automatic labeling system combines 4D label generation and simulation technology, which not only improves labeling efficiency, but also can train extreme scenarios in a virtual environment. This data closed-loop system ensures the continuous optimization and iteration of the FSD system.

3.2 Data-driven model training and optimization

Tesla continuously optimizes the performance of the FSD system through an automated data closed-loop system. This system includes the collection, processing, annotation and training of massive amounts of real road data and virtual simulation data, enabling Tesla FSD to quickly adapt to various complex driving environments and improve the robustness and safety of autonomous driving.