How Much Does FSD Rely on Map Data? Maybe More Than You Think [Video] (2024)

Tesla’s FSD visualizations are admittedly one of the coolest parts of owning a Tesla. Seeing vehicles all around you on the screen, and watching the car make decisions can be mesmerizing.

However, have you ever wondered exactly how the car generates the visualizations? Is it real-time, or does it rely on map data that is downloaded separately from the vehicle’s firmware?

Map Data

Tesla distributes map data to vehicles worldwide separately from its vehicle firmware versions. This means that the map data updates can have a different tempo than the firmware updates. For instance, some cars on 2024.8.9 (an FSD V11 version from ~3mo ago), as well as cars on 2024.14.7 (an FSD V12 version from just a few days ago) just started receiving the NA-2024.8-14924 map update last week. These updates ensure that vehicles can get the most current information about road layouts, traffic patterns, and other critical driving data.

While FSD can usually work with different map versions, how much does FSD and the in-car visualizations rely on the predefined map data? It obviously uses it for some things such as speed limit, but does it use it more than that? Thanks to user, ArthurFromX on X, we now have a better idea of how much relies on map data.

FSD Visualizations

Tesla’s FSD visualizations rely on a combination of pre-mapped data and real-time information gathered by the cameras. This approach provides both background context (pre-mapped data), as well as real-time context via computer vision.

It all comes together to provide an accurate representation of what the car sees around it, taking into account the width, height, and length of nearby vehicles, curbs, and other objects, such as garbage bins or traffic cones.

A post on X highlighted that FSD visualizations might be more impacted by background data than initially thought. The post mentioned that construction to a local roundabout rendered the visualizations inaccurate, although the car was correctly navigating the lane and making its exit.

This suggests that while real-time data is crucial, the accuracy of pre-mapped data still plays a significant role in the overall effectiveness of FSD visualizations, and perhaps a lesser role in the actual decision-making process of FSD.

FSD Navigation

Of course, these visualizations impact more than just what you see on screen. The mapping data and real-time data provide information on how the vehicle plans its path, and how it makes driving decisions, such as moving into right, or left-turn lanes as required.

Mapping data most likely provides background information, enabling the onboard hardware to process and work on driving the vehicle with an initial idea of where it is and what the road looks like.

In short, map data plays a backseat role to the actual driving of FSD, but does play a bigger role in route decision, as well as providing context and predictions to what the vehicle sees. It seems at the moment that visualizations are based on a combination of map data and camera data the vehicle gathers.

For a complete list of FSD visualizations supported, check out our FSD visualizations page.

Tesla released their updated vehicle safety report for Q1 2024, showing significant improvements in Autopilot safety.

Quarterly Improvements

For Tesla vehicles driving manually and leveraging Tesla’s safety features, Tesla recorded nearly 1 million miles driven before an accident, compared to about 500,000 miles for the industry average in the United States.

On the other hand, for vehicles using Autopilot or FSD, Tesla recorded nearly 7.63 million miles driven before an accident on average, which is a 15x improvement over the U.S. average. This is a considerable improvement over last quarter, where Tesla saw an average of 4.3 million miles before an accident and almost a 50% improvement year over year.

Tesla’s Active Safety Features

Tesla’s Active Safety Features have a big part to play in the massive increase in the number of miles driven to accident ratio, with key features like Lane Departure Avoidance and Automatic Emergency Braking (AEB) all playing a major part in keeping Tesla drivers safe on the roads. Tesla recently updated their vehicles so that Automatic Emergency Braking works in reverse and at speeds over 100 MPH. As of 2023, AEB now also works for vehicles traveling perpendicular to Tesla vehicles.

In addition to Tesla’s active safety features, they also have some of the safest cars in the world due to the way they’re built. Recently, VP Lars Moravy shared a video (below) that explains Tesla’s front giga casting and why it makes the vehicle much safer.

Safety Updates and Commitment

In addition to improving safety features, Tesla also regularly updates their vehicles with new safety features, such as a recent update for Hazard Light frequency. Tesla has committed to developing the safest cars in the world, and Tesla drivers regularly walk away from accidents that would crush other vehicles or injure their drivers.

Tesla’s Model S, X, 3, and Y, have the lowest overall probability of injury for vehicles tested by the US National Highway Traffic Safety Administration (NHTSA), and regularly achieve nearly perfect ratings in crash tests.

Elon Musk, in a recent post on X, confirmed that the upcoming expansion of Tesla’s supercomputers would be at Giga Texas, as part of a “super-dense, water-cooled” cluster. Elon previously mentioned that just this year, Tesla would be spending $10 billion in just AI and compute capabilities alone.

Much of the equipment for the cooling system has already arrived on site and is being placed on the end further away from the new glass front in the northern section, according to long-time Giga Texas drone flier Joe Tegtmeyer. The southern end of the new expansion has been confirmed to be the site of the supercomputer cluster expansion, as part of Tesla’s recent push towards autonomy and the reveal of Robotaxi, which is expected on August 8.

Compute Expansion

The continued expansion of compute has been a critical part of Tesla’s research and development funding this year, as per the 2024 Earnings Call. The in-house computing facilities support not just FSD development, but also the development of Optimus, Tesla’s humanoid robot. With recent moves towards bringing no-nag FSD, as well as the possible unveiling of FSD in China, these compute expansions will be necessary to support further improvements to Tesla’s AI models.