Tesla has never been afraid to pursue a grand ambition of achieving full self-driving (FSD) capabilities solely with cameras, without relying on costly sensors like LiDAR. However, a recently unveiled patent suggests the extent to which the company is going to stretch the limits of vision-based autonomy. The technology is fully patented and utilizes signed distance fields (SDFs) to generate high-quality 3D maps from standard 2D camera feeds, paving the way for a more affordable and scalable type of autonomous navigation.
This system, in essence, translates the visual world into a high-resolution 3D model that not only captures the geometry of the surrounding world but also records finer details, such as painted curbside markings and symbols on parking lots. By eliminating LiDAR dependency, Tesla is making a bet that will ensure the most efficient way to achieve world-class accuracy.
How Signed Distance Fields Transform Vision
Signed distance fields are mathematical functions that are used in space to determine the proximity of a point to the closest surface. In the method of Tesla, cameras are used to capture 2D images, which are converted into voxelized 3D grids, where SDFs give the depth and structural precision.
This enables the AI system to transcend stereo vision or depth estimation. It is able to deduce in-between geometry at levels of sub-voxel, down to 10 centimeters. With such fine-grained mapping, vehicles can learn not only about obstacles and roads but also other small, contextually important details, like:
- Painted handicap parking signs
- Directional arrows and lane markings
- Curb and driveway boundaries
Creating a 3D voxel model with these details allows Tesla cars to have a more detailed view of the surrounding environment than is available through alternative camera-only systems.
오, 이번주 신규 특허 공보 대박이네요! 🤓🎰
Tesla의 FSD 관련 신규 특허 2건이 공개되었습니다. 👀🔥🔥🔥
FSD v14나 그 이후 모델에 적용되는 기술일 것 같습니다. 🤔 pic.twitter.com/viV9VJlLlX
— SETI Park (@seti_park) September 11, 2025
Reducing Costs by Eliminating LiDAR
Among the most prominent changes that this technology brings is that LiDAR, which is an essential component of most autonomous driving systems, could become obsolete. LiDAR provides a high-quality depth view, but is very expensive (both in terms of money and hardware complexity).
The vision-only approach of Tesla reduces costs radically, enhances scalability, and does not rely on third-party suppliers. The software updates will allow millions of Tesla cars to join the vision-driven robots on wheels since every Tesla vehicle now comes with cameras, and the addition of an SDF-based mapping pipeline implies that even older vehicles can theoretically be upgraded to it, allowing their software to be updated.
This is not the first time this was said by CEO Elon Musk, who long asserted that LiDAR is a crutch and that, to be truly good, one needs to solve the vision-based perception – the same system that humans use.
In addition to enhancing the level of navigation and safety, the patent by Tesla solves one of the most topical issues in urban mobility: parking congestion. It has been found that as much as 30 percent of the traffic in the city is due to motorists circling in search of parking.
The detection at the voxel level can distinguish surfaces that can be occupied by an object or are open spaces, as well as marking whether the particular spots are in use by handicapped or reserved. This degree of specificity changes the way in which cars communicate with cities:
- Self-driving Teslas might also show drivers the direction to legal and free parking areas.
- FSD-equipped cars would be able to park themselves with great precision and save time.
- Smart distribution of traffic across the cities would help reduce congestion.
Practically, Tesla is actually marketing its cars not only as autonomous cars but as proactive contributors to the overall problems of urban infrastructure.
