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Tesla Smart Summon patent highlights progress in 3D labeling for full self-driving features
A recently published Tesla patent application details the machine learning methods behind Smart Summon, specifically highlighting the progress being made with 3D labeling in training data.
The application, titled “Autonomous and User Controlled Vehicle Summon to a Target,” utilizes machine learning methods explicitly detailed in two other recent Tesla patent publications in its functionality. This series of three inventions altogether describes an automated way of generating training data which is then used by a machine learning model to accomplish an expansive list of self-driving capabilities in Summon.
“Traditionally, much of the effort to curate a training data set is done manually by reviewing potential training data and properly labeling the features associated with the data,” Tesla’s first application in the series states. “The effort required to create a training set with accurate labels can be significant and is often tedious… Therefore, there exists a need to improve the process for generating training data with accurate labeled features.”
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- A method flow chart from Tesla’s autonomous 3D labeling patent. | Image: Tesla/USPTO
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- A method flow chart from Tesla’s Smart Summon patent application. | Image: Tesla/USPTO
The application goes on to describe how labeled training data is made autonomously in their invention using sensors and the collection of what’s called a “time series,” i.e., a series of images captured over a period of time.
“Using data captured by sensors on a vehicle to capture the environment of the vehicle and vehicle operating parameters, a training data set is created,” it explains. “In some embodiments, a three-dimensional representation of a feature, such as a lane line, is created from the group of time series elements that corresponds to the ground truth… As one example, a series of images for a time period, such as 30 seconds, is used to determine the actual path of a vehicle lane line over the time period the vehicle travels…a single image of the group and the actual path taken can be used as training data to predict the path of the vehicle.”
-->Tesla CEO Elon Musk has previously mentioned that better labeling is one of the keys to speeding up the rollout of self-driving functionality and features like Reverse Summon. “We need to finish work on Autopilot core foundation code & 3D labeling, then functionality will happen quickly. Not long now,” Musk wrote on Twitter in March this year. With better labeling (more accurate training data) comes safer and more capable software due to improved predictions from the modeling.
When it comes to Tesla’s Smart Summon, prediction modeling is essential considering there isn’t a driver in the vehicle during its operation. The patent publication covering Summon embodies the first application’s time series functionality and a second application’s implementation of the time series’ training data in its methods, demonstrating one of the numerous potential uses for the machine learning invention. Hints about future developments using Smart Summon are also detailed in the application. Examples include:
- Syncing the Smart Summon with a calendar so the vehicle “automatically navigates to arrive at the location at the ending time, such as the end of a dinner party, a wedding, a restaurant reservation, etc.”
- Implementing a multi-part destination into the Summon instructions such as waypoints at an airport to pick up multiple passengers.
- Monitoring the heartbeat of a Summon user to ensure they are maintaining a connection with the vehicle while operating the feature.
- Customizing the vehicle’s arrival settings such as interior lighting, exterior lighting, hazard lights, welcome music, and climate control preferences.
One of the more unique bits about the Smart Summon patent application is the appearance of Elon Musk as an inventor. While the CEO is known to be intimately involved in nearly all aspects of vehicle design, software features, and business operations, his name is unexpectedly absent from most of the company’s inventions. However, this is apparently on purpose. “I generally try my best not to be on patents,” he revealed on Twitter in reply to a post about the Smart Summon application. Notably, inventorship is a legal definition based on the conception of an invention, i.e., not the person/people who suggested or directed its creation, but the person/people who devised the means to accomplish it.
Prior to the most recent patent publication, Musk contributed inventorship to the door and body styling of the Model X. He also contributed the same to both the design and function of Tesla’s vehicle charge inlets.
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Tesla is improving Giga Berlin’s free “Giga Train” service for employees
With this initiative, Tesla aims to boost the number of Gigafactory Berlin employees commuting by rail while keeping the shuttle free for all riders.
Tesla will expand its factory shuttle service in Germany beginning January 4, adding direct rail trips from Berlin Ostbahnhof to Giga Berlin-Brandenburg in Grünheide.
With this initiative, Tesla aims to boost the number of Gigafactory Berlin employees commuting by rail while keeping the shuttle free for all riders.
New shuttle route
As noted in a report from rbb24, the updated service, which will start January 4, will run between the Berlin Ostbahnhof East Station and the Erkner Station at the Gigafactory Berlin complex. Tesla stated that the timetable mirrors shift changes for the facility’s employees, and similar to before, the service will be completely free. The train will offer six direct trips per day as well.
“The service includes six daily trips, which also cover our shift times. The trains will run between Berlin Ostbahnhof (with a stop at Ostkreuz) and Erkner station to the Gigafactory,” Tesla Germany stated.
Even with construction continuing at Fangschleuse and Köpenick stations, the company said the route has been optimized to maintain a predictable 35-minute travel time. The update follows earlier phases of Tesla’s “Giga Train” program, which initially connected Erkner to the factory grounds before expanding to Berlin-Lichtenberg.
-->Tesla pushes for majority rail commuting
Tesla began production at Grünheide in March 2022, and the factory’s workforce has since grown to around 11,500 employees, with an estimated 60% commuting from Berlin. The facility produces the Model Y, Tesla’s best-selling vehicle, for both Germany and other territories.
The company has repeatedly emphasized its goal of having more than half its staff use public transportation rather than cars, positioning the shuttle as a key part of that initiative. In keeping with the factory’s sustainability focus, Tesla continues to allow even non-employees to ride the shuttle free of charge, making it a broader mobility option for the area.
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Tesla Model 3 and Model Y dominate China’s real-world efficiency tests
The Tesla Model 3 posted 20.8 kWh/100 km while the Model Y followed closely at 21.8 kWh/100 km.
Tesla’s Model 3 and Model Y once again led the field in a new real-world energy-consumption test conducted by China’s Autohome, outperforming numerous rival electric vehicles in controlled conditions.
The results, which placed both Teslas in the top two spots, prompted Xiaomi CEO Lei Jun to acknowledge Tesla’s efficiency advantage while noting that his company’s vehicles will continue refining its own models to close the gap.
Tesla secures top efficiency results
Autohome’s evaluation placed all vehicles under identical conditions, such as a full 375-kg load, cabin temperature fixed at 24°C on automatic climate control, and a steady cruising speed of 120 km/h. In this environment, the Tesla Model 3 posted 20.8 kWh/100 km while the Model Y followed closely at 21.8 kWh/100 km, as noted in a Sina News report.
These figures positioned Tesla’s vehicles firmly at the top of the ranking and highlighted their continued leadership in long-range efficiency. The test also highlighted how drivetrain optimization, software management, and aerodynamic profiles remain key differentiators in high-speed, cold-weather scenarios where many electric cars struggle to maintain low consumption.
Xiaomi’s Lei Jun pledges to continue learning from Tesla
Following the results, Xiaomi CEO Lei Jun noted that the Xiaomi SU7 actually performed well overall but naturally consumed more energy due to its larger C-segment footprint and higher specification. He reiterated that factors such as size and weight contributed to the difference in real-world consumption compared to Tesla. Still, the executive noted that Xiaomi will continue to learn from the veteran EV maker.
“The Xiaomi SU7’s energy consumption performance is also very good; you can take a closer look. The fact that its test results are weaker than Tesla’s is partly due to objective reasons: the Xiaomi SU7 is a C-segment car, larger and with higher specifications, making it heavier and naturally increasing energy consumption. Of course, we will continue to learn from Tesla and further optimize its energy consumption performance!” Lei Jun wrote in a post on Weibo.
-->Lei Jun has repeatedly described Tesla as the global benchmark for EV efficiency, previously stating that Xiaomi may require three to five years to match its leadership. He has also been very supportive of FSD, even testing the system in the United States.
Elon Musk
Elon Musk reveals what will make Optimus’ ridiculous production targets feasible
Musk recent post suggests that Tesla has a plan to attain Optimus’ production goals.
Elon Musk subtly teased Tesla’s strategy to achieve Optimus’ insane production volume targets. The CEO has shared his predictions about Optimus’ volume, and they are so ambitious that one would mistake them for science fiction.
Musk’s recent post on X, however, suggests that Tesla has a plan to attain Optimus’ production goals.
The highest volume product
Elon Musk has been pretty clear about the idea of Optimus being Tesla’s highest-volume product. During the Tesla 2025 Annual Shareholder Meeting, Musk stated that the humanoid robot will see “the fastest production ramp of any product of any large complex manufactured product ever,” starting with a one-million-per-year line at the Fremont Factory.
Following this, Musk stated that Giga Texas will receive a 10 million-per-year unit Optimus line. But even at this level, the Optimus ramp is just beginning, as the production of the humanoid robot will only accelerate from there. At some point, the CEO stated that a Mars location could even have a 100 million-unit-per-year production line, resulting in up to a billion Optimus robots being produced per year.
Self-replication is key
During the weekend, Musk posted a short message that hinted at Tesla’s Optimus strategy. “Optimus will be the Von Neumann probe,” the CEO wrote in his post. This short comment suggests that Tesla will not be relying on traditional production systems to make Optimus. The company probably won’t even hire humans to produce the humanoid robot at one point. Instead, Optimus robots could simply produce other Optimus robots, allowing them to self-replicate.
-->The Von Neumann is a hypothetical self-replicating spacecraft proposed by the mathematician and physicist John von Neumann in the 1940s–1950s. The hypothetical machine in the concept would be able to travel to a new star system or location, land, mine, and extract raw materials from planets, asteroids, and moons as needed, use those materials to manufacture copies of itself, and launch the new copies toward other star systems.
If Optimus could pull off this ambitious target, the humanoid robot would indeed be the highest volume product ever created. It could, as Musk predicted, really change the world.
Tesla is improving Giga Berlin’s free “Giga Train” service for employees
Tesla Model 3 and Model Y dominate China’s real-world efficiency tests
