What will future travel and transportation look like based on AI? Such as Cybercab, Robovan and FSD

Musk’s blueprint for autonomous transportation, Cybercab, is a vehicle that completely eliminates the steering wheel and pedals and relies entirely on Tesla’s FSD (Full Self-Driving, Full Self-Driving Capability) system for operation. The Cybercab is expected to begin production in 2026 and will cost about 20 cents per mile to operate, well below the $1 per mile cost of public transportation in the United States. Musk predicts that Cybercab will be priced below $30,000 (approximately RMB 212,000) and will introduce inductive charging technology without the need for a traditional charging port. In addition, Cybercab’s autonomous driving capabilities will improve driving safety to more than 10 times that of human levels, and its appearance adopts a futuristic metallic color and butterfly wing style. Car door design. The Robovan driverless utility vehicle is inspired by the Art Deco trains of the 1930s, with a streamlined and futuristic shape. This car does not have a windshield and cockpit in the traditional sense. Instead, it has a large passenger space that can carry 20 people at a time. It is suitable for commercial or private travel, and can also be used as a school bus or cargo transport. The expected transportation cost of Robovan is only 5 to 10 cents per mile (approximately RMB 0.35 to 0.71 yuan), which will greatly reduce transportation costs. Robovan will also use Tesla's FSD system and be equipped with an automatic load level adjustment suspension system to improve passenger riding experience. Travel and transportation patterns in Musk’s eyes First of all, the efficient sharing economic model will develop like never before. The Robotaxi service Tesla plans to launch will allow car owners to add their own vehicles to a shared fleet. This Airbnb-like model will significantly increase vehicle usage and create additional income for car owners. Imagine that your car no longer just sits in the garage, but makes money for you 24 hours a day. This will completely change people's perception of vehicle ownership. Second, low-cost transportation solutions will become a reality. Self-driving vehicles such as Cybercab and Robovan will revolutionize the logistics and passenger transportation industries with operating costs far lower than traditional transportation. As costs come down, we're likely to see more accessible and ubiquitous public transportation systems. What’s even more exciting is that urban space will be used more optimally. As self-driving vehicles become more common, large parking lots will become redundant. These spaces can be transformed into public facilities such as parks and squares, making our cities more livable. Tesla also has an ambitious plan to turn its global fleet into distributed computing resources. Each Tesla vehicle will become a computing node and together form a huge computing network. This can not only be used for the operation of the vehicle itself, but also provide powerful computing support for AI reasoning, intelligent applications and other fields. Musk’s vision is to bring about all-round social change through technological innovation. From reducing travel costs to improving safety to freeing up time in the car, Tesla is gradually building an intelligent, low-cost, and efficient future travel network. This transportation revolution is not only about travel, but will also profoundly affect our lifestyle, urban planning, and even the entire social structure. The technical core of Tesla’s self-driving taxi Cybercab Tesla has built a full-stack self-research system from the bottom chip to the upper-layer application, including the FSD computing chip and board domain controller on the car side, the computing chip D1 chip and supercomputer Dojo required for training in the cloud, and the full-stack autonomous driving algorithm (perception algorithm, control algorithm and various neural networks). This full-stack self-research system enables Tesla to achieve efficient joint optimization and can be applied to cars and robots to achieve large-scale reduction of Book. The Dojo supercomputer is an important part of Tesla's autopilot system. It is based on the D1 Dojo chip and is a system-on-wafer (System on Wafer) system-level solution. It is used for unsupervised learning of massive video data and accelerates the iteration of Tesla's Autopilot and Fully Self-Driving (FSD) systems. The Dojo supercomputer has extremely high computing power. A single ExaPOD has more than 10^60,000 training nodes, and the BF16/CFP8 computing power is as high as 1.1 EFLOPs, which can provide higher performance and lower energy consumption at the same cost. Tesla adopts an end-to-end smart driving solution, namely BEV+Transformer+Occupancy Network. The core of this solution is to directly send the information collected by the vehicle into a unified deep learning network for processing and output driving commands. This design simplifies hardware costs and improves generalization, but it is highly dependent on large computing power chip hardware and sufficient source data to ensure continuous training and rapid iteration of the algorithm. Tesla’s purely visual perception framework includes the HydraNet multi-head network overlaying data calibration, spatial understanding, and spatiotemporal sequence feature space. This framework can extract spatiotemporal sequence features from video data and achieve deep object recognition and short-term memory through an end-to-end training model. In addition, Tesla has also introduced BEV (Bird’s Eye View) technology to enhance the driver’s perception of the surrounding environment. Tesla uses a multi-task network to improve the efficiency of the autonomous driving perception model and perform post-fusion in the BEV space to save computing resources. The Tesla Autopilot engine contains 48 neural networks that learn specifically for specific tasks to reduce false alarms. Tesla has established a complex automatic marking network to collect different data for accurate 3D reconstruction and use it to train the neural network. This data closed-loop system ensures continuous optimization and iteration of the algorithm. The technical core of Tesla Cybercab lies in its full-stack self-developed autonomous driving system, powerful Dojo supercomputer, end-to-end smart driving solution, pure visual perception framework, multi-task network and neural network models, and data closed-loop system. Together, these technologies form Cybercab’s advanced AI system, ensuring that it is ten times safer than human-driven vehicles and significantly improves vehicle utilization. Specific application cases of Dojo Specific applications The Dojo supercomputer is Tesla's core equipment for cloud training AI models. Its core is the neural network training chip D1 independently designed and manufactured by Tesla. Dojo accelerates the iteration of Tesla's Autopilot and Fully Self-Driving (FSD) systems by processing massive amounts of video data for unsupervised learning. The Dojo supercomputer adopts a unique architecture to optimize single-target visual recognition and processing, greatly improving the processing capabilities of visual data. For example, Dojo is able to train the autonomous driving system through 3D images and timestamps (4D Autopilot system), which enables the system to more accurately predict and mark the three-dimensional motion trajectories of road objects, thereby enhancing the reliability of the autonomous driving function. Dojo is a distributed computing architecture connected through a network structure, with large computing plane, extremely high bandwidth, low latency and extremely scalable. This architecture enables Dojo to efficiently process driving data from more than 1 million Tesla vehicles around the world, helping the Tesla FSD autonomous driving system to continuously optimize. The Dojo supercomputer not only improved the performance of Tesla's Autopilot system, it also helped Tesla save up to $6.5 billion in costs and added $500 billion to the company's market value. The use of Dojo gives Tesla an asymmetric advantage in the potentially $10 trillion autonomous driving market. Effect evaluation: Compared with traditional GPU, Dojo's performance is improved by 4 times, performance/watt is increased by 1.3 times, and space area is reduced by 5 times. These improvements significantly improve Dojo's efficiency and energy efficiency when processing complex visual data. The launch of the Dojo supercomputer marks a major breakthrough for Tesla in autonomous driving technology, especially in AI chips and supercomputing platforms. Dojo not only supports Tesla’s autonomous driving system, but also provides computing power for its humanoid robot Optimus. The application of Dojo supercomputer not only improves the performance of Tesla's autonomous driving system, but also opens up incremental space for the traditional automobile industry. Tesla could add $500 billion to its market capitalization through faster adoption of robotaxis and web services. What are the specific implementation methods and advantages of Tesla’s BEV+Transformer+Occupancy Network? In the past two years, we have frequently heard about end-to-end intelligence, and Tesla is by far the most successful practitioner of end-to-end intelligence. In Tesla's end-to-end smart driving solution, the combination of BEV (Bird's Eye View), Transformer and Occupancy Network has achieved significant technological breakthroughs and advantages. The specific implementation of BEV+Transformer: Tesla first converts the 2D image data around the vehicle into a bird's-eye view through the BEV perspective, which can effectively avoid scale and occlusion problems in the image perspective. Then, the Transformer model is used to reconstruct the multi-view image information into vector space. The Transformer model is based on the attention mechanism of the human brain and can flexibly and efficiently process and fuse image features from different cameras, thereby improving the accuracy of environmental perception. Tesla has implemented this approach in its FSD Beta software visual perception module and demonstrated related technical concepts from BEVFormer. The specific implementation method of Occupancy Network: The core idea of ​​Occupancy Network is to divide the three-dimensional space into countless tiny cubes. When facing obstacles, the specific shape of the object is no longer considered, but it is judged whether the tiny cubes in the corresponding area are occupied. This method effectively solves the problem of identifying special-shaped obstacles. Without using radar, Occupancy Network uses the BEV coordinate system and Transformer neural network to internalize changes in geometric shapes such as ground slope and curvature into the training parameters of the neural network to achieve accurate perception and prediction of object depth information. Specific advantages: Improved information processing efficiency: The BEV+Transformer combination improves information processing efficiency. The introduction of the spatiotemporal sequence feature layer gives autonomous driving short-term memory capabilities to deduce the most likely results in the current scenario. Reduce dependence on high-precision maps: The BEV perspective solves the problem of strong dependence on high-precision maps during the implementation of urban NOA functions. Through vector space reconstruction supported by large models, it has higher accuracy of environmental perception. Unified input and fusion of multi-sensor signals: The end-to-end smart driving solution realizes unified input and fusion of multi-sensor signals, shares image features, and improves algorithm development efficiency. Eliminate occlusion and overlap: The BEV perspective enables local end-to-end optimization. Perception and prediction are performed in the same space and parallel results are output, thereby improving detection accuracy. How do the HydraNet multi-head network and BEV technology work, and their specific contribution to autonomous driving performance? Tesla’s purely visual perception framework significantly improves autonomous driving performance through the HydraNet multi-head network and BEV technology. HydraNet is a multi-task neural network architecture consisting of a backbone network, a neck and multiple branch heads, capable of supporting over 1000 different tasks. Its core feature is that multiple subtask branches share the same feature space, thereby reducing repeated convolution calculations and improving efficiency. HydraNet completes end-to-end training through residual neural network and multi-layer feature fusion structure, extracts multi-scale visual feature space, and then completes sub-network training and outputs perception results according to different task types. BEV (Bird's Eye View) technology is a method used by Tesla to describe the perceived perspective or coordinates of the real world. Tesla launched the BEV+Transformer large model algorithm based on the Transformer architecture in 2021 to build a bird's-eye view panorama and solve the problem of 2D-3D space conversion and perception performance. BEV technology achieves high-precision space conversion by converting 2D data collected by cameras into 3D vector space, thereby improving the perception capabilities of the autonomous driving system. The combination of HydraNet and BEV technology provides advanced support for Tesla’s pure visual perception solution. HydraNet improves the efficiency and flexibility of perception systems through feature sharing and task decoupling. BEV technology solves the problem of 2D-3D space conversion and improves perception accuracy by constructing a bird's-eye view. Tesla data automatic marking network and 3D accurate reconstruction technology? Tesla's data closed-loop system plays a crucial role in autonomous driving technology, of which automatic marking network and 3D accurate reconstruction technology are its core components. The following is a detailed introduction to these two technologies and their role in the optimization of autonomous driving algorithms. Automatic marking network The automatic marking network is a key link in Tesla's data closed-loop system. It is mainly responsible for labeling the collected raw data so that it can be used for neural network training. Tesla collects different types of driving data by building a complex automatic marking network and uses this data to perform accurate 3D reconstruction. This process not only improves the efficiency of data annotation, but also greatly improves the quality of annotated data. 📷 Tesla’s automatic labeling system uses advanced algorithms, such as automatic labeling algorithms based on the Transformer mechanism (such as SAM). These algorithms can quickly process large amounts of data and generate high-quality labeling results. For example, Tesla has implemented an automatic labeling process with minimum manpower requirements. Complex intersection labeling that originally took two weeks now only takes 5 minutes, increasing efficiency by 1,000 times. This efficient automatic annotation process not only reduces labor costs, but also speeds up model training. In addition, Tesla has also efficiently collected a large amount of real and effective driving data through shadow mode, forming a complete data closed loop. After automatic labeling, these data are used for neural network training to continuously optimize the autonomous driving algorithm. 📷 3D accurate reconstruction technology 3D accurate reconstruction technology is an important part of Tesla’s automatic marking network. It generates high-quality three-dimensional scene models by reconstructing the three-dimensional space of the collected data. This technology allows Tesla to conduct various model tests based on reconstruction, including testing of extreme situations. 📷 The application of 3D reconstruction technology has greatly improved the robustness and adaptability of autonomous driving systems. By accurately reconstructing the three-dimensional scene, Tesla can more accurately identify and predict various obstacles and dynamic changes on the road, thereby improving the safety and reliability of the autonomous driving system. In addition, 3D reconstruction technology also provides high-quality data support for simulation, allowing the simulation platform to generate unlimited simulated road scenes to further optimize the training effect of the neural network. The role of automatic marking network and 3D accurate reconstruction technology in autonomous driving The automatic marking network and 3D accurate reconstruction technology play a crucial role in the optimization of Tesla’s autonomous driving algorithm. First, the automatic labeling network provides sufficient training data for the neural network through efficient and high-quality data labeling, thereby continuously optimizing algorithm performance. Secondly, 3D accurate reconstruction technology improves the autonomous driving system's ability to identify and predict complex road conditions by generating high-quality three-dimensional scene models, further improving the robustness and safety of the algorithm. In addition, Tesla has achieved continuous training in extreme simulation scenarios by combining automatic marking and 3D reconstruction technology, which not only reduces dependence on real data, but also accelerates the iteration of algorithm capabilities. This efficient data closed-loop system allows Tesla to complete the annotation and processing of large amounts of data in a short time, thereby quickly iterating and optimizing the autonomous driving algorithm. Another thing is that Tesla was the first car company to refer to the transformer architecture quite early, as early as 2019, 2 years after "attention is all you need" was published. What else? By the way, Musk had not left OpenAI the previous year.