According to the SAE International ^[According to the SAE International ^1], previously known as the Society of Automotive Engineers, there are six different levels of automation defined and agreed on internationally, from 0 to 5.^], previously known as the Society of Automotive Engineers, there are six different levels of automation defined and agreed on internationally, from 0 to 5

As the levels increase, the extent vehicle's independence to human control and intervention increases. i.e. Level 0 means no automation and level 5 indicates fully autonomous driving.

As the levels increase, the extent vehicle's independence to human control and intervention increases. i.e. Level 0 means no automation and level 5 indicates fully autonomous driving

Level 0:

Level 0 the human driver performs all the driving tasks and the car has no control over its operation

Level 1:

Level 1 the vehicle is equipped with a driver assistant system (DAS) ^[2] that can partially help the driver with speed adjustment, automatic cruise, lane divergence warking, or emergency braking at a basic level.

Level 2:

Level 2 the vehicle is equipped with more advanced features- advanced driver assistance system (ADAS) that can oversee steering and accelerate and braking in some easy conditions and good daylight conditions; however, the human driver is required to continue to pay full attention during the entire journey and perform directly on almost all complex driving tasks.

Level 3:

Level 3 the system is capable of performing all parts of the driving task in some ideal conditions ^[3], but the human driver is required to be able to ready to regain control when requested to do so by the system. The driver should be ready for a safe transition between the autonomous mode and the human mode within a few seconds. So the driver is not allowed to sleep or fully recline the driving seat. 

Level 4:

Level 4 the vehicle’s ADAS is able to independently perform all driving operations in certain conditions. In this level, the human vigilance/attention is not required on the pre-defined conditions.

Level 5:

Level 5 the vehicle system is taking all the advanced sensor technologies and state-of-the-art AI, Computer Vision, and Machine Learning techniques ^[4] and is able to perform all driving tasks in all weather and lighting conditions, in any road types from urban to rural to highways without any requirement of human intervention or monitoring. and no driving assistance is required from the human driver. Using cloud data and internet communication the AV would be able to communicate with other vehicles and infrastructures to get the most updated information about the road situation beforehand (from few seconds to few minutes in advance).^[5]

While SAE levels offer a standardized technical framework to classify autonomous driving capabilities, the societal acceptance of these technologies is equally critical to their real-world deployment. The perception of safety, trust in automation, and user experience design significantly influence how drivers interact with semi-autonomous systems. Misunderstanding the limitations of Levels 2 and 3, for instance, has led to documented cases of over-reliance on automation. Therefore, beyond engineering performance, successful integration of autonomous vehicles requires clear user education, human-machine interface improvements, and regulatory policies that align with cognitive and behavioral realities of drivers and passengers.

Implications of SAE Levels

While the SAE framework provides technical clarity, there is widespread confusion among the public regarding what each level entails. In particular, the boundary between Levels 2 and 3 is a source of misunderstanding, sometimes leading to dangerous over-reliance on the system. Fatal incidents involving Level 2 vehicles (e.g., Tesla) have highlighted the need for stricter regulation and clearer driver education.

Moreover, the transition from Level 3 to Level 4 introduces legal and ethical questions. Who is liable if a crash occurs during autonomous operation? How should vehicles respond in morally complex situations (the “trolley problem”)? Legal systems worldwide are grappling with how to define responsibility and ensure accountability for automated systems.

Public Trust and Human Factors

A crucial yet sometimes overlooked component of automation adoption is public trust. Even if the technology is proven, widespread adoption depends on user confidence in its safety and fairness. Issues such as data privacy, algorithmic bias, and human-machine communication must be addressed. Drivers must clearly understand what the system can and cannot do. Visual and audio alerts, intuitive dashboard designs, and real-time feedback can help bridge this communication gap.

Broader Urban and Policy Context

The deployment of autonomous vehicles does not occur in isolation. Cities must adapt their infrastructure—lane markings, signage, sensor-compatible intersections—to accommodate autonomous navigation. Policymakers must update traffic laws, insurance frameworks, and urban planning strategies. Public transportation systems might integrate Level 4 shuttles or buses, enhancing efficiency but also raising questions about employment and equity.

Conclusion

The SAE levels of automation represent more than a technical categorization—they chart the roadmap for the future of mobility. Understanding each level’s capabilities and limitations is essential for developers, regulators, and users alike. As autonomous vehicles continue to evolve, the intersection of technology, law, ethics, and society will shape how—and how soon—they become a regular feature of everyday transport.

Beyond the formal classification of automation levels, the real-world implications of adopting these technologies demand critical attention. As vehicles shift from assisted to autonomous operation, the automotive industry must not only refine hardware and software but also address complex ethical, legal, and social challenges. For instance, the transition from Level 2 to Level 3 automation presents a blurred boundary in responsibility: while the vehicle can technically operate autonomously in specific conditions, the requirement for immediate human takeover introduces legal ambiguity and potential safety risks.

Moreover, autonomous vehicle deployment raises infrastructural and regulatory questions. Urban environments, particularly in densely populated areas, lack the sensor-rich infrastructure or mapped precision that AVs often require. Policymakers need to consider zoning, liability law reform, and data governance—especially in relation to real-time communication between vehicles and external systems (V2X). The readiness of cities to integrate AVs will depend on public investment, inter-agency coordination, and community participation.

Public perception also plays a pivotal role in adoption. Surveys consistently show that consumer trust in AV technology lags behind the pace of development. Fear of system failure, job loss among professional drivers, and concerns over algorithmic bias all influence how societies accept or resist autonomous mobility. Building trust may require not just robust technical safety but also visible ethical standards and transparent system behavior.

From an environmental perspective, high-level autonomous vehicles offer both opportunities and risks. If integrated into electric vehicle (EV) platforms and shared mobility services, AVs can significantly reduce carbon emissions and urban congestion. However, if deployed as private luxury vehicles, they could exacerbate sprawl and increase total vehicle miles traveled (VMT), negating potential sustainability gains. Therefore, transportation planners must guide AV integration within broader sustainability and modal shift strategies.

Economically, the long-term impacts of widespread AV use are transformative. Industries such as insurance, freight, ride-hailing, and urban logistics may be reshaped by automation. For instance, logistics companies are already piloting autonomous delivery robots and trucks to cut costs and increase efficiency. Yet, this comes with labor displacement and a pressing need for reskilling programs and social safety nets.

Finally, autonomous vehicles cannot be evaluated purely through technical performance. They must be seen as part of a broader mobility ecosystem. Integrating AVs with public transport, walking, and cycling infrastructure will require inclusive, equity-driven urban design. Without intentional planning, AVs may further marginalize those without access to digital tools or who live in underserved areas.

In sum, while SAE levels provide essential terminology and structure, a successful transition to autonomous mobility requires a holistic approach—balancing innovation with public interest, efficiency with ethics, and automation with accountability.

 Cover photo:

 Cover photo:^[6]