Tesla’s story in self-driving is a mix of real technical progress, bold marketing, and a moving finish line. On one hand, Full Self-Driving (FSD) has evolved into a system that can handle complex navigation—turns, merges, lane changes, intersections, parking maneuvers—99% of the time with startling competence. On the other hand, Tesla itself is explicit that today’s product is not autonomous: it requires active driver supervision and does not make the car self-driving in the legal or technical sense. (Tesla)
Meanwhile, “Robotaxi” is the bigger promise: cars that don’t just help a driver, but replace the driver—turning vehicles into revenue-generating autonomous fleets. That leap is not merely incremental. It’s a jump across technology, regulation, safety validation, business operations, insurance, and public trust. This article explains what Tesla’s FSD really is today, how it works at a high level, what “Robotaxi” requires that FSD doesn’t yet deliver, and why the next phase will be harder than many people expect.
1) What Tesla FSD is today (and what it is not)
Tesla currently sells Full Self-Driving (Supervised). Tesla describes it as a system that can drive you “almost anywhere” under your supervision, and Tesla emphasizes that enabled features require active driver supervision and “do not make the vehicle autonomous.” (Tesla)
Regulators largely categorize this as SAE Level 2 driver assistance, meaning the system can control steering and speed in certain conditions, but the human driver remains responsible and must continuously supervise. NHTSA’s automation-level descriptions make that distinction clear: Level 2 still expects the driver to monitor the environment and be ready to take over immediately. (NHTSA)
This matters because “self-driving” is not one thing—it’s a ladder:
Level 2 (driver assistance): the human supervises everything.
Level 4 (true robotaxi in a defined area): the system drives itself within an Operational Design Domain (ODD)—for example, specific cities, geofenced neighborhoods, certain weather limits—without expecting a human to watch the road.
Level 5 (anywhere, anytime): full autonomy in all conditions.
Tesla’s consumer FSD today is still, by the company’s own characterization and by regulatory framing, on the Level 2 rung. (NHTSA)
2) How Tesla’s approach differs: “vision-first” and fleet learning
Tesla’s technical strategy has been distinctive: heavy reliance on cameras and neural networks, with a philosophy that the best path to scalable autonomy is to solve driving the way humans do—primarily through vision—then scale via software and data.
Over the last several years, Tesla moved further toward “Tesla Vision.” Tesla has published support material describing the transition away from certain non-vision sensors, including the removal of ultrasonic sensors (USS) from vehicles and the shift to camera-based replacements for some features. (Tesla)
(Separately, multiple automotive outlets documented Tesla’s earlier move toward camera-only for certain models/markets by removing radar, as part of the broader “Tesla Vision” shift.) (The Drive)
The upside of this approach is scalability: millions of cars can collect real-world driving data, and Tesla can iterate quickly via over-the-air updates. The downside is that vision-only autonomy has to be extraordinarily robust in the messy corners of reality: glare, heavy rain, occlusions, odd construction layouts, faded markings, emergency scenes, human gestures, and rare-but-critical edge cases.
3) The modern self-driving stack (in plain English)
Even if Tesla increasingly trains end-to-end networks, it’s still useful to understand the core jobs any automated driving system must do:
Perception: detect lanes, curbs, vehicles, pedestrians, cyclists, signals, signs, and “free space.”
Prediction: estimate what other road users are likely to do next.
Planning: choose a safe, lawful, comfortable path through the scene.
Control: execute steering, braking, and acceleration smoothly.
Driver monitoring / supervision enforcement: ensure the human is attentive (for Level 2 systems).
Tesla’s advantage is iteration speed: it can ship improvements to a large fleet rapidly. For example, Tesla software update notes for late 2025 reference new speed-profile behaviors—the system choosing speed based on driver profile, speed limits, and surrounding traffic. (Notatesla App)
Those details might sound small, but they’re the “last mile” behaviors that determine whether a system feels safe and predictable—or weird and stressful.
4) Why Robotaxi is a different problem than “better FSD”
A Robotaxi service requires far more than “FSD, but improved.”
To operate a true driverless service at scale, Tesla must prove all of the following (not just once, but repeatedly, under scrutiny):
Safety without supervision: no attentive driver in the loop.
A defined ODD (at least at first): where it works reliably (city zones, mapped areas, permitted routes).
Operational support: remote assistance, dispatch, cleaning, maintenance, incident response.
Regulatory approvals: state-by-state (and sometimes city-by-city) frameworks for driverless operation.
Liability and insurance: who is responsible in a crash, and how claims are handled.
Customer experience: pickup precision, rider support, accessibility, and safe failure modes.
That’s why Robotaxi is as much an operations company as a software company.
5) Tesla’s Robotaxi: from vision to visible products
Tesla has repeatedly framed autonomy as central to its future. In October 2024, Reuters reported Tesla unveiled a dedicated robotaxi concept called the Cybercab, described as lacking steering wheel and pedals, with claims around high-volume production starting in 2026 and a sub-$30,000 price point, alongside another concept vehicle (“robovan”). (Reuters)
Whether or not the Cybercab hits those targets, the significance is strategic: Tesla is positioning Robotaxi not as a bolt-on feature for consumer cars, but as a platform and business line.
6) Robotaxi is not just a car—Tesla is building service infrastructure
By late 2025, Tesla had also begun publicly documenting Robotaxi as a service. Tesla hosts Robotaxi support pages that describe how riders “schedule an autonomous ride,” how the Robotaxi app works, and what to do for support during a trip. (Tesla)
Tesla’s “Get Started” guidance states the Robotaxi app is available (at least at that time) for iOS, using Tesla Account credentials. (Tesla)
Tesla also maintains a Robotaxi landing page that prompts users to download the app and get updates about service availability. (Tesla)
This is important: it suggests Tesla is moving from “feature in a car” to “product with riders,” which forces concrete decisions on safety policies, rider support, and service boundaries.
7) The regulatory reality check: investigations and scrutiny
Autonomy doesn’t scale on vibes—it scales on evidence. And Tesla’s self-driving efforts have faced continued safety scrutiny.
NHTSA opened an investigation in 2025 into versions of Tesla’s system labeled “FSD (Supervised)” and “FSD (Beta),” examining the scope and potential safety consequences of maneuvers that may involve traffic safety violations; the document notes Tesla characterizes FSD as an SAE Level 2 partial automation system requiring an attentive driver at all times. (NHTSA)
The broader context is that regulators care not only about what the system can do, but how it behaves in edge cases and whether drivers are miscalibrated into overtrust. Any Robotaxi rollout will likely face even stricter oversight, because the human fallback is removed.
8) Competition: Robotaxi is already real (just not everywhere)
Tesla is entering a market where several players already operate or expand autonomous ride services globally. A Reuters overview in late 2025 described accelerating robotaxi deployments and pilots involving multiple companies and regions—Waymo, Zoox, Baidu, WeRide, and others—highlighting that the field is moving quickly, not waiting for Tesla. (Reuters)
Tesla’s main competitive distinction remains its fleet scale and software distribution model. Its biggest competitive risk is that rivals may achieve robust driverless operation sooner within constrained ODDs (specific cities, carefully validated routes) using heavier sensor stacks and slower—but steadier—expansion.
9) The economics: why Wall Street cares so much
Robotaxi economics are seductive because they change the math of car ownership:
A consumer car sits parked most of the day.
A robotaxi fleet car could operate many hours per day.
Revenue per vehicle per year could rise dramatically.
Per-mile costs could fall with scale, optimization, and high utilization.
That’s why autonomy narratives can dominate Tesla valuation discussions even when the core car business is under pressure. For example, Reuters reporting on Tesla’s broader business environment notes investor focus on future initiatives like robotaxis and self-driving, even amid challenges in deliveries and competition. (Reuters)
But the path from “demo” to “profitable fleet” is long. The cost structure isn’t just compute—it’s also cleaning, maintenance, customer support, insurance, remote assistance, and compliance.
10) What to watch next: the milestones that actually matter
If you want to judge Tesla’s Robotaxi progress like an analyst—not a fan or a critic—watch for a few concrete signals:
ODD clarity: Where exactly can it operate driverlessly, and under what conditions (weather, time of day, road types)?
Safety validation: Transparent evidence that driverless operation is safer than human driving in that ODD, including how disengagements and incidents are handled.
Regulatory approvals at scale: Not just one city, but repeatable approvals across states and metros.
Operational maturity: Fleet uptime, rider support quality, rapid incident response, vehicle cleaning logistics.
Unit economics: Real per-mile cost, utilization rates, and insurance loss ratios.
Robotaxi will not be “won” by the company with the most impressive single drive. It will be won by the company that can repeat safe, reliable, regulator-approved driverless miles millions of times—and do it profitably.
Conclusion: Tesla’s autonomy bet is real—but the hardest part is ahead
Tesla FSD has progressed into a powerful supervised driving system, with rapid iteration and a distinctive vision-first strategy. Tesla also appears to be laying the groundwork for Robotaxi as a service—apps, support flows, and rider documentation—while promoting a future of dedicated autonomous vehicles like the Cybercab. (Tesla)
But the jump from supervised assistance to unsupervised Robotaxi is not a software update—it’s a transformation in responsibility. When the driver is removed, the system becomes the driver, and Tesla becomes (in practice) a transportation operator. That means higher standards, tougher regulation, and a new kind of trust to earn.
If Tesla succeeds, it could reshape transportation economics and turn autonomy into a platform business. If it stumbles, it won’t just be a missed product deadline—it will be a reputational and regulatory setback in one of the highest-stakes engineering problems of our era.
