The Autopilot and Full Self-Driving (FSD) systems of Tesla are about to be visually enhanced significantly. Based on the new leaks found in the software code, it is expected that soon, the in-car visualization system will be capable of identifying and displaying a far broader scope of vehicles and types of mobility.
Such improvement is a reflection of how Tesla is continually working towards a more intelligent, context-sensitive perception system, one that is reminiscent of the complexity of the real-world driving situations.
Other 3D assets discovered in the code include the models of a wide range of vehicles and travel means, including ambulance, train, fire truck, garbage truck, school bus, European semi-truck, golf cart, scooter, skateboard, stroller, street sweeper, three-wheeler, trailer, tram, and even wheelchair. All these extensions hint at the fact that Tesla is getting ready to make its visualizations more accurate reflections of what the car cameras and neural networks are seeing on the road.
A Step toward a Smarter Perception
Up to this day, the visualization presented by Tesla has mostly represented regular cars, trucks, bicycles, and pedestrians. This simplistic representation did not cover a high number of vehicle and road user types that are already detected internally by the FSD system. The new update seals that divide, and gives the driver and system a more detailed and enhanced representation of the surroundings.
New autopilot viz assets came
Ambulance
Firetruck
Garbage truck
schoolbus pic.twitter.com/JYsK7ZsLwg— green (@greentheonly) October 13, 2025
Identifying an ambulance or fire truck, e.g., is not merely a visual show, but with a real-world implication to safety. Autonomous systems must be able to recognize emergency vehicles and know their road priority. Likewise, the differentiation between a school bus and a street sweeper will assist the FSD software in predicting the distinctive behavioral patterns, e.g., stop-and-go or long-turn.
The fact that smaller or non-traditional types of mobility, such as scooters, skateboards, golf carts, and wheelchairs, are included in Tesla also demonstrates the urbanity of the company and mixed-traffic scenarios. This is exactly the situation in which the diversity and unpredictability of objects are at their most, and any visual and behavioral interpretation is of vital importance to safety.
Beyond Visualization: Its Implications to FSD
Although initially the 3D assets will be introduced into the car’s screen visualization, their presence will suggest that the neural networks that underlie the perception will improve. The Tesla strategy of autonomy is based on a camera-neural network-only version of AI to see the world without lidar or radar.
The extra object types are a good indication that the perception stack at this point is capable of distinguishing the difference between them on a reliable basis. It is not an easy one: to teach AI to differentiate among, say, a stroller and a wheelchair moving along, or a semi-truck and a tram moving in the distance, one will have to train neural networks with large quantities of labeled video data.
An Insight into the Next Level at Tesla.
Visualization will frequently be released ahead of new features at Tesla. Previously, the introduction of new 3D car models or road signs alluded to the release of new features of FSD. The introduction of these various capabilities can be seen as the next generation of FSD, perhaps combined with the new Autopilot 4 hardware and new generation AI driving models.
This feature also coincides with the overall trend of Tesla to pursue realism and simplicity in the driving interface. The company has recently rolled out a new visualization update named Tron: Ares Mode that transforms the on-screen world into a dynamic and stylized environment in 3D. The new assets would harmonize with this aesthetic and serve as a source of visual pleasure as well as practical use.
