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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsAutonomous-driving systems are built in different ways, but their software architecture is not the same thing as their automation level. A modular system divides driving into stages such as perception, planning and control; an end-to-end system learns a more direct mapping from sensor inputs to driving actions; and a hybrid combines elements of both. None is established as universally best, and none tells you by itself how much responsibility a driver must take.
First, separate the software design from the driving responsibility
SAE automation levels describe how driving tasks and fallback responsibilities are divided between a human and an automated system. They do not rate the quality of the software or identify its architecture. SAE J3016 defines six levels; SAE International’s taxonomy overview covers the classification.
For U.S. drivers, NHTSA says Levels 0–2 require the driver to remain engaged and monitor the driving environment. At Level 2, a system may assist with both steering and speed, but the human remains responsible for supervision. NHTSA also says Levels 3–5 are not available on vehicles for consumer purchase in the United States. These are statements about U.S. consumer availability, not a claim that no higher-level systems are being tested or operated in any context. See NHTSA’s driver-assistance guidance and its automated-vehicle safety page.
“Self-driving” can give people the wrong impression about whether they need to pay attention. Do not stop monitoring a driver-assistance feature because of its name or because of how its software is engineered.
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What are the main approaches to autonomous-driving software?
Modular pipelines: separate stages that pass information forward
A modular pipeline divides the driving problem into components. A common outline is perception (what is around the vehicle?), prediction (what might other road users do?), planning (what should the vehicle do?), and control (how should it execute that action?). The exact divisions vary by system.
The 2017 CARLA paper illustrates this approach with vision-based perception, a rule-based planner and a maneuver controller. Because stages are distinct, engineers can inspect or revise a component without redesigning the whole pipeline. The trade-off is that a mistaken or incomplete output from one stage can affect later stages. That dependency is a design and testing concern, not proof that modular systems are inherently less safe or less capable.
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End-to-end learning: learn a more direct route from input to action
End-to-end approaches train a system to map sensor input more directly to a driving command or motion plan, rather than relying on a fully hand-specified sequence of interfaces between driving modules. The 2023 survey End-to-end Autonomous Driving: Challenges and Frontiers reviews more than 270 papers and describes the potential to optimize features jointly across tasks such as perception and planning. CARLA’s paper compares end-to-end models trained with imitation learning and reinforcement learning.
Learning a more direct mapping can reduce dependence on manually designed boundaries between components. It also makes it harder to explain why a model produced a particular action, and the survey identifies robustness and causal confusion as challenges. “End-to-end” does not necessarily mean that every implementation is one opaque neural network with no rules, monitoring software or safety controls around it.
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Hybrid systems: combine learned components with explicit structure
Hybrid describes a spectrum, not one standardized architecture. A system might use learned perception or prediction while relying on an explicit planner, constraints, monitors or fallback logic. Teams may combine these elements to draw on machine learning’s ability to interpret complex inputs while retaining components that can be inspected or constrained. The exact balance is system-specific; the available sources do not establish hybrids as a benchmark winner.
How do the approaches compare?
The following are design tendencies, not fixed properties of every implementation. The CARLA paper and 2023 survey compare research approaches, but they do not provide an apples-to-apples independent ranking of architecture families on safety or performance.
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| Question | Modular pipeline | End-to-end learning | Hybrid system |
|---|---|---|---|
| Can engineers trace a decision? | Separate stages make it possible to inspect intermediate outputs and isolate some faults. | Intermediate reasoning may be less directly legible; interpreting model behavior is a recognized challenge. | Some components and constraints may be inspectable, while learned components can remain difficult to explain. |
| How are errors handled? | A stage’s output can propagate into later stages, so interfaces and failure handling need testing. | Jointly learned behavior can avoid some manually specified interfaces, but robustness and causal confusion remain concerns. | Monitoring and constraints can be added around learned components, but the combination itself must be validated. |
| What development work is central? | Teams define and test component boundaries, interfaces and each stage’s behavior. | Training and evaluation depend on data and learning methods; CARLA’s comparison includes imitation and reinforcement learning. | Teams must develop and validate both learned components and the structure that connects or constrains them. |
| Does the architecture settle operating limits or fallback? | No. Those depend on the complete vehicle system and its intended operating domain. | No. A direct learned mapping does not by itself specify when a vehicle may operate or who must respond at a limit. | No. A monitor or fallback can be part of a design, but its presence alone does not establish safe operation. |
There is no established score here: architecture labels do not tell a reader how a particular vehicle performs in every road, weather or traffic condition. SAE level is not a software-quality score either.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Sensor choice cuts across all three approaches
Modular, end-to-end and hybrid describe ways to organize or train software. They are not synonyms for particular sensor packages. A system can combine multiple sensor types and use machine learning within a modular or hybrid design.
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In an October 2021 description of its own system, Waymo said it combines lidar, cameras and radar. The company described lidar as providing depth and 3D shape, cameras as capturing visual features such as traffic-signal color, and radar as useful for measuring motion and in difficult weather. This is Waymo’s account of its system, not an independent comparison of sensor performance across manufacturers.
In the same 2021 account, Waymo reported that its major perception, behavior-prediction and planning software used machine-learning models that benefited from more than 20 million autonomously driven miles. That is a company-reported figure from October 2021, not an independently verified or current comparative performance statistic.
Why safety depends on more than architecture
A driving system must be evaluated as a combination of software, vehicle hardware, operating limits and real-world response processes. That includes how a system handles hazards and unusual scenarios, how it is tested before deployment, how its intended operating domain is defined, and what happens when it reaches a limit. Simulation and closed-course testing can probe scenarios, but field operations and response to problems matter too.
Waymo’s 2020 description of its own safety framework organized its approach into hardware, behavioral and operations layers, including scenario-based simulation, closed-course testing, simulated deployments and fleet-response processes. The company wrote: “There is currently no universally accepted approach for evaluating the safety of autonomous vehicles – despite the efforts of policymakers, researchers and companies building fully autonomous technologies.” That statement is from Waymo’s company-authored 2020 post; it describes the state of evaluation as Waymo characterized it at that time, not a universal standard or an independent assessment of Waymo’s safety.
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