News
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.”
-
- A method flow chart from Tesla’s autonomous 3D labeling patent. | Image: Tesla/USPTO
-
- 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.
News
Tesla takes a step towards removal of Robotaxi service’s safety drivers
Tesla watchers are speculating that the implementation of in-camera data sharing could be a step towards the removal of the Robotaxi service’s safety drivers.
Tesla appears to be preparing for the eventual removal of its Robotaxi service’s safety drivers.
This was hinted at in a recent de-compile of the Robotaxi App’s version 25.11.5, which was shared on social media platform X.
In-cabin analytics
As per Tesla software tracker @Tesla_App_iOS, the latest update to the Robotaxi app featured several improvements. These include Live Screen Sharing, as well as a feature that would allow Tesla to access video and audio inside the vehicle.
According to the software tracker, a new prompt has been added to the Robotaxi App that requests user consent for enhanced in-cabin data sharing, which comprise Cabin Camera Analytics and Sound Detection Analytics. Once accepted, Tesla would be able to retrieve video and audio data from the Robotaxi’s cabin.
Video and audio sharing
A screenshot posted by the software tracker on X showed that Cabin Camera Analytics is used to improve the intelligence of features like request support. Tesla has not explained exactly how the feature will be implemented, though this might mean that the in-cabin camera may be used to view and analyze the status of passengers when remote agents are contacted.
Sound Detection Analytics is expected to be used to improve the intelligence of features like siren recognition. This suggests that Robotaxis will always be actively listening for emergency vehicle sirens to improve how the system responds to them. Tesla, however, also maintained that data collected by Robotaxis will be anonymous. In-cabin data will not be linked to users unless they are needed for a safety event or a support request.
Tesla watchers are speculating that the implementation of in-camera data sharing could be a step towards the removal of the Robotaxi service’s safety drivers. With Tesla able to access video and audio feeds from Robotaxis, after all, users can get assistance even if they are alone in the driverless vehicle.
Investor's Corner
Mizuho keeps Tesla (TSLA) “Outperform” rating but lowers price target
As per the Mizuho analyst, upcoming changes to EV incentives in the U.S. and China could affect Tesla’s unit growth more than previously expected.
Mizuho analyst Vijay Rakesh lowered Tesla’s (NASDAQ:TSLA) price target to $475 from $485, citing potential 2026 EV subsidy cuts in the U.S. and China that could pressure deliveries. The firm maintained its Outperform rating for the electric vehicle maker, however.
As per the Mizuho analyst, upcoming changes to EV incentives in the U.S. and China could affect Tesla’s unit growth more than previously expected. The U.S. accounted for roughly 37% of Tesla’s third-quarter 2025 sales, while China represented about 34%, making both markets highly sensitive to policy shifts. Potential 50% cuts to Chinese subsidies and reduced U.S. incentives affected the firm’s outlook.
With those pressures factored in, the firm now expects Tesla to deliver 1.75 million vehicles in 2026 and 2 million in 2027, slightly below consensus estimates of 1.82 million and 2.15 million, respectively. The analyst was cautiously optimistic, as near-term pressure from subsidies is there, but the company’s long-term tech roadmap remains very compelling.
Despite the revised target, Mizuho remained optimistic on Tesla’s long-term technology roadmap. The firm highlighted three major growth drivers into 2027: the broader adoption of Full Self-Driving V14, the expansion of Tesla’s Robotaxi service, and the commercialization of Optimus, the company’s humanoid robot.
“We are lowering TSLA Ests/PT to $475 with Potential BEV headwinds in 2026E. We believe into 2026E, US (~37% of TSLA 3Q25 sales) EV subsidy cuts and China (34% of TSLA 3Q25 sales) potential 50% EV subsidy cuts could be a headwind to EV deliveries.
“We are now estimating TSLA deliveries for 2026/27E at 1.75M/2.00M (slightly below cons. 1.82M/2.15M). We see some LT drivers with FSD v14 adoption for autonomous, robotaxi launches, and humanoid robots into 2027 driving strength,” the analyst noted.
News
Tesla’s Elon Musk posts updated Robotaxi fleet ramp for Austin, TX
Musk posted his update on social media platform X.
Elon Musk says Tesla will “roughly double” its supervised Robotaxi fleet in Austin next month as riders report long wait times and limited availability across the pilot program in the Texas city. Musk posted his update on social media platform X.
The move comes as Waymo accelerates its U.S. expansion with its fully driverless freeway service, intensifying competition in autonomous mobility.
Tesla to increase Austin Robotaxi fleet size
Tesla’s Robotaxi service in Austin continues to operate under supervised conditions, requiring a safety monitor in the front seat even as the company seeks regulatory approval to begin testing without human oversight. The current fleet is estimated at about 30 vehicles, StockTwists noted, and Musk’s commitment to doubling that figure follows widespread rider complaints about limited access and “High Service Demand” notifications.
Influencers and early users of the Robotaxi service have observed repeated failures to secure a ride during peak times, highlighting a supply bottleneck in one of Tesla’s most visible autonomy pilots. The expansion aims to provide more consistent availability as the company scales and gathers more real-world driving data, an advantage analysts often cite as a differentiator versus rivals.
Broader rollout plans
Tesla’s Robotaxi service has so far only been rolled out to Austin and the Bay Area, though reports have indicated that the electric vehicle maker is putting in a lot of effort to expand the service to other cities across the United States. Waymo, the Robotaxi service’s biggest competitor, has ramped its service to areas like the San Francisco Bay Area, Los Angeles, and Phoenix.
Analysts continue to highlight Tesla’s long-term autonomy potential due to its global fleet size, vertically integrated design, and immense real-world data. ARK Invest has maintained that Tesla Robotaxis could represent up to 90% of the company’s enterprise value by 2029. BTIG analysts, on the other hand, added that upcoming Full Self-Driving upgrades will enhance reasoning, particularly parking decisions, while Tesla pushes toward expansions in Austin, the Bay Area, and potentially 8 to 10 metro regions by the end of 2025.
Tesla takes a step towards removal of Robotaxi service’s safety drivers
Mizuho keeps Tesla (TSLA) “Outperform” rating but lowers price target
