The big idea is to make the car learn how to drive instead of programming a separate response for every road situation. Known broadly as AV2.0, the approach uses machine-learning models to connect camera, lidar, radar and other sensor data with an internal representation of the scene, predictions about what may happen next, and the vehicle’s driving actions.
That does not mean simply installing a larger neural network or removing every conventional software module. The more realistic version combines end-to-end learned driving models with simulation, world models, maps or navigation data, classical vehicle control, hard safety limits and fallback systems. The goal is to make the learned part generalize across unfamiliar cities, vehicles, weather and road behavior.
There is important progress. Wayve says one driving model has operated zero-shot in more than 500 cities; Waymo has introduced a driving world model based on Google DeepMind’s Genie 3; Waabi is building a closed-loop generative simulator; and NVIDIA is supplying tools for generating synthetic physical-world data. But these are not proof that a consumer car can drive everywhere without supervision. In the formal SAE sense, Level 5 still means driving on all roads, in all conditions, without a human fallback—and no such consumer vehicle is available in the United States, according to NHTSA.
What does it mean for a self-driving car to go anywhere?
The phrase sounds precise, but it can describe several very different achievements. A system that drives on unfamiliar streets in a known city has demonstrated something valuable. It has not necessarily demonstrated that it can operate in a different country, in heavy snow, on an unmarked rural road or without a safety driver.
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| Meaning of go anywhere | What it tests | Why it is not enough by itself |
|---|---|---|
| Geographic generalization | Driving in a city or country absent from the training data | The test may still use limited speeds, favorable weather or a safety driver |
| Road-type generalization | Handling urban streets, highways, alleys, parking lots and rural roads | A model can be strong in one road class and weak in another |
| Weather generalization | Operating in rain, snow, fog, glare, darkness or flooding | Passing a rain test does not establish all-weather capability |
| Behavioral generalization | Understanding local driving customs, aggressive merges and informal right-of-way negotiation | Traffic behavior is not fully described by written rules |
| Vehicle generalization | Working across different sensor positions, dimensions, steering systems and vehicle dynamics | Some adaptation or calibration may still be required |
| Operational generalization | Operating without a detailed pre-built map or fixed geofence | A map-light system can still have other geographic or weather limits |
| SAE Level 5 | Driving on all roads under all conditions without a human fallback | This is the universal standard implied by true everywhere autonomy, and it has not been achieved |
The technical term for the boundary around a system’s capabilities is its operational design domain, or ODD. An ODD can limit geography, road type, speed, weather, lighting, traffic density, construction conditions, vehicle type, emergency-vehicle interaction and the availability of remote support.
A vehicle can be highly capable inside a carefully selected ODD and still not be a general-purpose self-driving car. That is exactly the distinction between SAE Level 4 and Level 5. SAE J3016 defines the levels, while NHTSA describes Level 4 as automation limited to defined conditions and Level 5 as automation that works everywhere. The headline claim needs those conditions attached before it means anything.
The traditional self-driving stack
Most early autonomous-driving programs were built as a collection of specialized components:
Sensors → perception → localization → prediction → planning → control
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- Localization estimates where the vehicle is, often using GPS, visual features, inertial sensors and a high-definition map.
- Prediction estimates what other road users are likely to do.
- Planning chooses a route and a short-term trajectory.
- Control converts that trajectory into steering, braking and acceleration.
This modular design is not foolish or obsolete. It offers explicit interfaces, familiar debugging tools and the ability to test individual components. Engineers can impose rules and safety constraints at several points in the system. Waymo’s driverless service demonstrates that a carefully bounded, sensor-rich and operationally supported system can work in the real world.
The scaling problem is that every module, interface and exception becomes an engineering project. A team may need to handle a new lane marking, a new traffic-sign convention, a different camera position, an unusual road layout, a temporary construction pattern and a local driving habit separately. The number of edge cases grows faster than a test team can write rules for them.
Rare failures are particularly difficult. The events most likely to expose a weakness—an emergency responder directing traffic, a pedestrian stepping from behind a bus, a flooded lane, a confusing road closure or an aggressive driver forcing a merge—may occur too infrequently to collect in sufficient quantities through ordinary driving. Real-world testing is essential, but waiting for every important event to happen naturally is slow and expensive.
AV2.0: replace some hand-engineering with learned driving
The original AV2.0 argument, described in Autonomy 2.0: Why is self-driving always five years away? and covered by MIT Technology Review in 2022, was that self-driving needed a more data-driven approach.
Instead of explicitly programming every relationship between perception, prediction and action, a learned system could absorb those relationships from large amounts of driving data. A simplified version looks like this:
Sensor observations and context → learned scene and world representation → predicted futures → driving action
An end-to-end model might produce steering, braking and acceleration directly. More commonly, a production system produces intermediate learned outputs such as waypoints, trajectories, occupancy grids, action distributions or latent scene states. A classical controller may then execute the selected trajectory, while independent monitors impose emergency braking, collision-avoidance or safe-stop behavior.
That is why end-to-end should be treated as a spectrum rather than a binary label. It asks how much of the driving problem is trained jointly from data, not whether the vehicle contains no rules, no map, no controller and no safety monitor.
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AV2.0 is sometimes described as a reinforcement-learning revolution, but the modern pipeline uses several related techniques:
- Imitation learning learns from human demonstrations, such as recorded steering, braking and route decisions.
- Reinforcement learning improves a policy through rewards, penalties or other feedback after actions are taken.
- Offline reinforcement learning learns from previously recorded driving data without experimenting with a live car.
- Model-based reinforcement learning uses a learned model of how the environment changes to train or evaluate a policy.
- Self-supervised pretraining extracts useful representations from large quantities of unlabeled video and sensor data.
- Foundation models attempt to learn representations and behaviors broad enough to transfer across locations, vehicles and situations.
These terms are not interchangeable. End-to-end describes an architecture. Reinforcement learning describes one way to optimize behavior. A system may be end-to-end without being trained primarily through reinforcement learning, or it may use reinforcement learning inside a larger modular stack.
Why Wayve became the clearest AV2.0 example
The original 2022 article focused on a group of newer companies—Wayve, Waabi, Ghost and Autobrains—that challenged the heavily engineered Waymo and Cruise-style approach. Their common theme was not identical hardware or one shared algorithm. It was the belief that data-driven learning could reduce the cost of building a new driving system for every city and vehicle.
Wayve’s early demonstrations made the idea tangible. In a 2018 experiment, the company reported teaching a car to follow a lane using a single camera in roughly 15 to 20 minutes. Safety-driver interventions supplied feedback during the process. This was a narrow lane-following task, not general autonomous driving, but it showed the intended learning loop: instead of manually specifying every lane-following response, the policy improved from examples and corrective feedback. Wayve describes the experiment in Learning to drive in a day.
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Wayve then explored what it called Dreaming about Driving. A learned world model generated imagined driving sequences, allowing a policy to practice and improve in virtual experience before being tested on a real vehicle. The progression was significant:
- Collect real driving observations and intervention data.
- Learn a compact representation of the road scene and its dynamics.
- Predict how the scene could evolve after different actions.
- Train or evaluate a driving policy inside those imagined sequences.
- Transfer the resulting policy back to the real vehicle and measure the gap.
This is the core reason simulation and world models matter. A vehicle cannot safely try every dangerous action on public roads. A sufficiently useful learned environment can provide counterfactual experiences: what might have happened if the car had braked earlier, merged later, moved around the obstacle or yielded to the pedestrian?
Generalization is the business case
The commercial promise is not merely smoother driving in one location. It is cheaper and faster deployment.
A heavily mapped system may require detailed road geometry, traffic-light locations, lane topology, speed limits and turn restrictions for each service area. It must also maintain those maps as construction and road conditions change. If a learned driver can reuse its understanding of traffic behavior in a new city, the company may need much less city-specific engineering and data collection.
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- Generalization from London to other UK cities and between a passenger car and a commercial van.
- Adaptation from the UK to the US using 500 hours of additional US-specific data, with the US data covering conventions such as four-way stops, right turns on red, unprotected left turns and freeway merging.
- Adaptation to a different vehicle platform using 100 hours of vehicle-specific data.
- A single driving model operating zero-shot in more than 500 cities across Europe, North America and Japan.
These claims come from Wayve’s own generalization work, multi-country results and Series D announcement. They demonstrate an important direction, but they do not by themselves establish driverless, high-speed, all-weather performance in all those cities.
Zero-shot also needs a definition. It might mean no city-specific fine-tuning, no new labels, no new map, no new route or no new vehicle calibration. Those are different achievements. The reader should ask whether the tests were conducted on public roads, with what speed limits, in what weather, for how many miles, with what intervention rate and with or without a safety driver.
Wayve’s US adaptation numbers illustrate both progress and limitation. The model transferred useful knowledge across countries, but it still needed additional data for local conventions. That is evidence that a broad driving representation can reduce adaptation work—not evidence that geographic generalization is automatic.
The funding story also reveals the scale of the challenge. Wayve announced a $1.2 billion Series D round in February 2026 and said its total new capital had reached $1.5 billion. That is evidence of serious investment in the software-and-data approach, not a safety certification or proof of production autonomy.
Maps are not simply good or bad
High-definition maps give an autonomous vehicle valuable prior information:
- lane topology and road geometry;
- traffic-light and crosswalk locations;
- curbs and drivable boundaries;
- speed limits and turn restrictions;
- known landmarks for localization.
They also create a coverage and maintenance burden. A stale map can be actively misleading when a lane shifts, a road is closed or construction changes the geometry. The more locations a robotaxi service covers, the more data must be collected, verified and updated.
A mapless or map-light system can potentially generalize more easily, but it does not have to be ignorant of geography. It may still use ordinary navigation maps, GPS, visual localization, fleet data, route context or semantic maps generated as it drives. The useful question is not whether a company says maps are present. It is:
Which information must be prepared in advance, and how does the vehicle respond when that prior information disagrees with what its sensors see?
Why world models are the next step
A world model is a learned model that predicts how an environment evolves and how actions affect it. As Google DeepMind explains, such models can simulate aspects of the world so an agent can anticipate future states and the consequences of its actions.
For driving, a world model might predict:
- future camera views;
- lidar or radar observations;
- the positions and velocities of other road users;
- road occupancy and free space;
- the ego vehicle’s future motion;
- weather and lighting changes;
- what happens after a steering, braking or acceleration decision.
This matters because driving is inherently predictive. The vehicle must estimate whether a pedestrian will step into the lane, whether a car behind it will react to braking, whether an oncoming vehicle will appear around an obstruction or whether another driver will accelerate into a gap.
A useful simulator therefore needs to be closed loop. In an open-loop replay, the model simply predicts what happened next in a prerecorded clip. In a closed loop, the autonomous system chooses an action, the virtual world responds, other agents react and the system must continue driving based on the consequences of its own decisions. Closed-loop evaluation is harder, but it is much closer to the actual control problem.
A world model is not automatically a human-like understanding of the world. It may generate a visually convincing future while getting causality, object identity, road geometry or vehicle dynamics wrong. The value of a world model depends on whether its predictions correlate with what happens in the real environment, especially in safety-critical cases.
Waymo’s world model: a new tool, not a Level 5 car
On February 6, 2026, Waymo introduced the Waymo World Model, built on Google DeepMind’s Genie 3 and adapted for autonomous-driving simulation.
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Waymo says the system can generate controllable, multimodal scenes with camera and lidar outputs. It can vary scene layout, driving actions and language prompts, and create rare or difficult situations involving extreme weather, unusual objects and dangerous interactions. That makes it potentially useful for finding scenarios that are too rare, dangerous or expensive to collect in ordinary road testing.
The important qualification is that this announcement concerns a simulation and training technology. It does not mean that the production Waymo vehicle is directly controlled by Genie 3. Nor does it establish that every generated scene is physically accurate or that the model has validated every safety-critical behavior. Waymo itself notes that maintaining quality and consistency becomes harder over longer simulated sequences.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteWaymo separately reported more than 220 million fully autonomous miles through the end of March 2026 and more than 20 billion simulated miles. Its latest safety analysis compared performance in five operating geographies with human-driver benchmarks and reported lower crash rates in the categories it studied. Those are important operational data points, but they are Waymo’s analysis, tied to specific locations, exposure levels, crash definitions and comparison methods. They should not be read as a universal safety result or as evidence of Level 5 capability.
Waabi’s simulation-first approach
Waabi has made the simulator itself central to its autonomy strategy. Its Waabi World system is described as a closed-loop simulator that:
- Builds digital twins from real-world data.
- Simulates the sensors that the autonomous vehicle would use.
- Creates diverse, rare and adversarial situations.
- Lets the autonomy system learn from its mistakes as the simulated world reacts to its actions.
The closed-loop detail matters. This is not merely a collection of prerecorded videos or attractive synthetic images. The policy must make decisions, and those decisions change the next state of the virtual world.
Waabi reports a 99.7% realism score based on paired real-world and simulated trajectories. That figure must be attributed to Waabi and treated as a company-specific metric, not an accepted industry standard. Waabi’s own explanation of simulator realism identifies sources of divergence including the behavior of simulated actors, sensor differences, hardware latency and vehicle-dynamics approximations.
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That is the central test for any simulator: does a policy that behaves correctly in simulation also behave correctly on the road? Small errors can compound in a closed loop. A slightly wrong position estimate can produce a slightly wrong action, which changes the next scene and creates a much larger error several seconds later.
NVIDIA is building the infrastructure layer
NVIDIA’s Cosmos represents another part of the same shift. Rather than presenting one complete consumer driving service, NVIDIA is supplying world-foundation-model tools that can generate or predict physical-world video and synthetic data for robots and autonomous vehicles.
NVIDIA says Cosmos Transfer can use inputs such as segmentation maps, depth maps, lidar scans, poses and trajectories to produce controllable, photorealistic video. Its autonomous-vehicle simulation tools can vary weather, lighting and other conditions for synthetic-data generation.
This points toward a future in which an autonomous-driving system is an ecosystem rather than one monolithic brain:
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- a general foundation model;
- a vehicle-specific driving policy;
- a learned or physics-based simulator;
- synthetic-data generation tools;
- perception and planning models;
- vehicle-control software;
- safety monitors and redundant systems;
- automotive computing hardware.
Cosmos can make data generation more scalable. It cannot, by itself, provide a safety case for a deployed vehicle. A platform capability and an approved, reliable autonomous service are different things.
How the leading approaches differ
| Approach | Main strength | Main limitation or open question |
|---|---|---|
| Waymo-style mapped, sensor-rich Level 4 | Demonstrated driverless service in bounded operating areas, with extensive operational infrastructure | Mapping, sensor, validation and city-launch costs can make expansion complex |
| Wayve-style end-to-end foundation model | Potential to generalize across cities, countries, vehicles and sensor configurations | Public evidence of large-scale unsupervised driverless operation remains limited |
| Tesla’s neural driving system | A broad consumer fleet can collect data and receive frequent software updates | FSD (Supervised) requires an attentive driver and is not autonomous |
| Waabi’s simulation-first system | Closed-loop training and targeted generation of rare or adversarial scenarios | Simulator fidelity, especially physical and behavioral fidelity, must be independently validated |
| NVIDIA Cosmos ecosystem | Tools for scalable synthetic-data and physical-world model development | Infrastructure capability is not the same as a deployed safety case or complete driving product |
Camera-first and sensor-rich systems
Sensor choice creates another trade-off rather than a settled winner.
A camera-first strategy offers lower sensor cost, easier integration into production vehicles and access to huge quantities of existing video data. It may scale across vehicle platforms more easily. Its challenges include dependence on visual conditions, limited direct depth information, uncertainty in poor weather and a heavier burden on learned perception.
A sensor-rich strategy can provide precise geometry and redundancy across cameras, lidar, radar and other sensors. It can be particularly effective inside a defined service area. The costs include hardware, packaging, calibration, sensor cleaning and degradation, compute requirements and vehicle-specific integration.
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Wayve emphasizes cameras and radar in its sensor strategy, while Waymo’s world-model announcement describes generating both camera and lidar outputs. Those choices reflect different engineering trade-offs; neither announcement proves that one sensor philosophy has solved autonomy.
Where Tesla fits—and where it does not
Tesla also uses large-scale neural networks and a camera-centered driving approach, so it belongs in the broader discussion of learned autonomy. But the consumer product’s legal and operational status is clear.
Tesla calls its system Full Self-Driving (Supervised). The company says it can drive on various road types and almost anywhere under active driver supervision, while also stating that the system does not make the vehicle autonomous. The Tesla support page and owner documentation require the driver to remain attentive and ready to take control.
That makes the formal distinction important:
- At Level 2, the system may provide steering and acceleration or braking assistance, but the human remains responsible and must monitor the driving environment.
- At Level 4, the automated system is responsible for the complete driving task within a defined ODD, and the human is not expected to be takeover-ready inside that domain.
- At Level 5, the system is responsible everywhere and under all conditions.
A vehicle that needs a continuously attentive driver is driver assistance, regardless of the product name or how impressive the route looks in a demonstration.
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The original thesis focused on end-to-end learning, reinforcement learning, generalization and simulation. The field’s vocabulary has expanded:
- 2022: AV2.0 challenged hand-coded modular stacks with learned driving policies and data-driven simulation.
- 2023 and 2024: foundation-model representations and multimodal driving models became more prominent.
- 2025 and 2026: world models, neural simulation, synthetic data and embodied-AI infrastructure moved toward the center of development.
- Production direction: the likely systems combine learned components with maps, route planning, vehicle control, safety rules, monitoring and fallback behavior.
The breakthrough, if it arrives, will probably not be one magical neural network replacing every conventional component. It will be a training and deployment pipeline that makes generalization, testing and safety validation more scalable.
What independent research says
Academic results show why the idea is promising but not finished.
The Drive Anywhere research project used multimodal foundation-model representations for open-set, end-to-end driving and reported testing on a real vehicle. It also reported an average inference speed of about three frames per second, compared with roughly 10 to 30 frames per second for comparison policies. That is a useful warning: a model can demonstrate broad visual or behavioral generalization and still require substantial optimization before it can run with production-grade latency, power consumption and redundancy.
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Examples of visually convincing but physically wrong generated scenes include vehicles intersecting, objects changing shape, inconsistent occlusion, unstable road geometry, pedestrians behaving unrealistically and objects disappearing or reappearing incorrectly after leaving the frame. A simulator that looks real for a few seconds is not necessarily a simulator that can support safety-critical training.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.The hard problems that remain
Simulation-to-reality transfer
Synthetic experience is valuable only if it teaches the real vehicle something reliable. The simulator must model perception artifacts, sensor timing, hardware latency, vehicle dynamics, tire behavior, road friction and the responses of other road users well enough that policy improvements transfer to public roads.
Visual realism is not sufficient. The key questions are causal:
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- Does the simulated vehicle make the same decision as the real vehicle?
- Do simulated drivers react plausibly to that decision?
- Does the world preserve object identity and geometry over long horizons?
- Do sensor artifacts and latency produce the same failure modes?
- Does performance in simulation predict performance in the real world?
Long-horizon consistency
A model may generate a plausible five-second clip but drift over a minute. Small errors in object position, road shape or agent intent can compound after the vehicle’s own actions change the scene. This is especially dangerous in closed-loop testing because the system is no longer being evaluated against a fixed recording.
Unusual road users and emergency situations
A general-purpose driver must handle far more than ordinary cars and pedestrians. Meaningful tests include police, fire and ambulance vehicles; tow trucks; road-maintenance vehicles; bicycles carrying large objects; mobility scooters; animals; wrong-way traffic; and pedestrians emerging from behind buses.
It must also understand the actions of emergency responders and workers directing traffic, even when those actions conflict with a traffic signal or a stale map. Waymo’s world-model demonstrations deliberately include rare and unusual objects, but a generated demonstration is not the same as validated performance by a deployed vehicle.
Construction and temporary geometry
Construction is a strong test of whether the system understands the road rather than merely matching a familiar visual pattern or following a map. A serious evaluation should include missing lane markings, cones, temporary lane shifts, contradictory signs, workers gesturing vehicles through, police directing traffic, blocked sidewalks and closures that have not reached the navigation database.
Local driving culture
Traffic laws do not fully describe driving behavior. A vehicle must infer whether another driver is about to cut in, whether a pedestrian’s gesture means proceed, whether a car that stopped unexpectedly is yielding or malfunctioning and how much space local drivers normally leave during a merge.
Wayve’s US adaptation work is relevant because it identified four-way stops, right turns on red, unprotected left turns and short freeway merges as local competencies. Those examples show why country-level transfer requires more than recognizing new signs.
Weather and sensor degradation
Robustness to one rainy drive does not establish capability in heavy snow, freezing rain, fog, glare on wet pavement, smoke, dust, flooding or rapidly changing illumination. Sensors can also be obscured by dirt, ice, salt or water. The vehicle needs to recognize when its perception has degraded, reduce its operating envelope and reach a safe state.
Knowing when the model does not know
A genuinely general driver needs uncertainty management, not just average smoothness. When the road is blocked or the scene is unfamiliar, can the vehicle slow down, stop safely, pull over, request remote assistance or explain what information is missing? Can it distinguish an unusual but safe event from an unknown event that requires caution?
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This is one reason a learned driving policy cannot be the whole product. A deployable system also needs safe stopping, incident response, remote assistance, emergency-vehicle procedures, passenger support, charging and maintenance, localization updates, cybersecurity, insurance and liability processes.
Runtime and vehicle hardware
Foundation models can be computationally expensive. A production vehicle must run them with predictable latency and power consumption on automotive hardware, often with thermal limits and redundant systems. Engineering teams may need model compression, quantization, distillation and failover paths. Offline research results do not automatically meet onboard real-time requirements.
Regulation and operations
Technical transfer does not automatically produce legal or commercial deployment. Each region can require a safety case, testing evidence, operating permissions, local fleet support and procedures for accidents or stranded vehicles. The UK government’s 2026 partnership with Wayve includes work on safety assurance, simulation at scale and integration into production vehicle platforms, illustrating that the deployment problem extends beyond the driving model itself. See the UK government announcement.
What would prove genuine general-purpose autonomy?
The most convincing milestone would not be a viral video, a city-count claim or a visually realistic generated scene. It would be a system that can enter an unfamiliar environment, identify its limits, handle rare events and fail safely without a continuously responsible human.
When evaluating a company’s claim, ask these questions:
- What exactly was unfamiliar? Was it a new city, country, road, vehicle, sensor configuration, weather condition or all of them?
- What does zero-shot mean in this test? Was there no fine-tuning, no new map, no route preparation, no vehicle calibration or no human intervention?
- Was the test open loop or closed loop? Could the system recover from its own mistakes, or was it predicting a recorded human trajectory?
- Was it on public roads? Private tracks cannot reproduce unpredictable human behavior, emergency responses, ambiguous right-of-way or local road maintenance.
- Was a safety driver present? If so, what was the intervention rate and how much responsibility did the driver retain?
- How much exposure was measured? Ask for miles or hours, speeds, weather, road types and the number and severity of interventions.
- Were the scenarios independently reproducible? Company demonstrations are useful, but independent testing and disclosed acceptance criteria are stronger.
- Does the simulator correlate with reality? Look for paired real-versus-simulated behavior, not just image quality or aggregate realism scores.
- Can the system operate in real time? Check inference latency, frame rate, power draw, redundancy and failover.
- What happens when the system is uncertain? Safe stop, remote support and recovery procedures matter as much as successful normal driving.
A useful evidence hierarchy is:
- Independent real-world crash and disengagement data.
- Reproducible closed-loop tests on genuinely unseen scenarios.
- Paired real-and-simulation validation.
- Publicly specified safety cases and acceptance criteria.
- Peer-reviewed results involving a real vehicle.
- Company demonstrations and promotional videos.
The likely future is learned, but not rule-free
The strongest version of AV2.0 is not a choice between artificial intelligence and engineering. It is a different division of labor.
Machine learning may become responsible for the parts of driving that are difficult to enumerate: recognizing unusual situations, predicting human behavior, interpreting informal gestures, choosing a smooth trajectory and transferring knowledge to new environments. Conventional engineering may remain responsible for deterministic control, redundancy, emergency braking, monitoring, safe stopping, cybersecurity and the explicit limits of the ODD.
The winning architecture may also depend on the business model. Wayve’s software-licensing strategy, Waymo’s integrated robotaxi service and Tesla’s consumer-vehicle approach have different costs and incentives. A cheaper camera package does not necessarily mean a cheaper autonomous operation once data collection, compute, validation, remote assistance, insurance, maintenance and regulatory work are included.
The original AV2.0 idea was that autonomous driving had become too dependent on hand-engineered rules and too expensive to expand. That diagnosis remains influential. The newer answer adds foundation models, world models and neural simulation. The central unresolved question is whether those tools can produce not merely broader demonstrations, but a measurable safety improvement under unfamiliar, long-tail conditions.
Frequently Asked Questions
Is Tesla Full Self-Driving actually autonomous?
No. Tesla’s Full Self-Driving (Supervised) system requires an attentive driver who remains responsible for monitoring the vehicle and taking action. Its name does not make it SAE Level 4 or Level 5 autonomy. Tesla states this on its FSD support page.
What is AV2.0 in self-driving cars?
AV2.0 is a broad approach that replaces some hand-engineered autonomy with learned models trained on driving data, simulation and feedback. It usually includes end-to-end learning, generalization across locations and vehicles, and closed-loop simulation or world models. It does not require eliminating maps, vehicle controllers or independent safety systems.
Is a self-driving car that works in 500 cities Level 5?
Not necessarily. A city-count claim may demonstrate geographic generalization while still involving a safety driver, limited speeds, favorable weather, restricted routes or other ODD limits. Level 5 requires driving on all roads and under all conditions without a human fallback.
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No. A world model predicts or generates possible future scenes and the consequences of actions. It can help train and test a driving policy, but it is usually a simulation or modeling component rather than the complete production vehicle system. Its usefulness depends on physical and behavioral accuracy.
The Bottom Line
The big new idea is learned generalization: train a driving model on enough real and simulated experience that it can transfer what it knows to unfamiliar places instead of requiring a separately engineered stack for every city.
That approach is now a serious engineering program, not just a 2022 research slogan. Wayve, Waymo, Waabi and NVIDIA are all developing different pieces of the data, foundation-model and simulation ecosystem. But the gap between can operate in many unfamiliar environments and can drive everywhere without supervision remains enormous.
The real milestone will be independently reproducible, closed-loop safety evidence showing that a system can recognize its limits, handle rare events and fail safely across a broad ODD. Until then, AV2.0 is best understood as a promising route toward more scalable autonomy—not proof that Level 5 has arrived.
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