Autopilot Without Illusions: Why Full Autonomy Isn’t the Norm Yet and What It Takes to Break Through
The scene from Total Recall where the protagonist steps into a driverless taxi looked like pure science fiction in 1990. Today, autonomous vehicles no longer feel like a distant dream. Yet fully autonomous transport has still not become a mass reality. The reason is not a single technological gap. Algorithm development, digital infrastructure, legal regulation, production economics, and the human factor are advancing at different speeds. It is precisely this lack of synchronization that slows the breakthrough.
To understand where we stand, it is necessary to refer to the SAE classification, which defines six levels of autonomy from 0 to 5. Level 0 means full human control; Level 5 means a completely autonomous vehicle without a steering wheel or pedals. As of today, the mass market operates at Level 2. These systems can steer and control speed simultaneously, but the driver is required to continuously monitor the road. Responsibility remains with the human. Level 3 has already been partially implemented. For example, Mercedes-Benz’s DRIVE PILOT system has been officially certified as Level 3 in certain jurisdictions. Under specific conditions typically in traffic jams on highways and at limited speeds the vehicle can take over driving. The driver is allowed to look away from the road, but the system functions only where legislation permits it and where traffic conditions match predefined parameters. If conditions change, the vehicle requests that control be resumed.
Tesla follows a different approach with its Full Self-Driving (FSD) system. Despite the name, it is legally still classified as Level 2. The vehicle can perform complex maneuvers in urban environments, but the driver must keep hands on the wheel and maintain supervision. The company relies on large-scale data collection and continuous software updates, yet responsibility remains with the human.
Waymo represents a Level 4 example autonomous driving without a driver within clearly defined zones. Robotaxi services operate in several U.S. cities, but only in carefully mapped areas with detailed digital infrastructure and constant support systems. Outside these zones, the system does not activate. This means that full autonomy is currently geographically limited and dependent on controlled environments. Level 5 universal autonomy without operational restrictions has not yet been achieved by any manufacturer. Digital infrastructure plays a significant role in the development of autonomous transport. Vehicles rely on high-definition maps, data processing centers, artificial intelligence algorithms, and communication between vehicles. Connectivity is essential for scaling systems, deploying software updates, managing fleets, and coordinating traffic.
However, an autonomous vehicle cannot depend on network connectivity for core safety decisions. Level 3 and Level 4 systems are designed so that, in the event of signal loss, the vehicle can safely continue driving or come to a controlled stop. LiDAR, radar, cameras, and onboard computing systems operate locally. Cloud synchronization enhances performance but does not replace local autonomy. Satellite-based solutions such as Direct-to-Cell and transport-adapted versions of Starlink extend coverage beyond urban infrastructure. They increase connectivity resilience, particularly in logistics or remote areas. Nevertheless, latency and technical limitations prevent satellite links from supporting critical real-time driving functions. Hybrid connectivity combining mobile networks and satellite coverage is therefore a complementary tool rather than the foundation of autonomy.
Beyond technology and infrastructure, additional barriers remain.
- The first is legal liability. In the event of an accident involving an active Level 3 system, determining responsibility becomes complex: is it the manufacturer, the owner, the software developer, or the insurer? In many countries, the regulatory framework is still evolving. Legal uncertainty remains one of the primary constraints on large-scale deployment.
- The second is economics. A fully redundant sensor stack including LiDAR units, backup systems, and high-performance computing modules remains expensive. A mass transition to Level 4 will only occur when the cost structure becomes economically viable for broad consumer adoption.
- The third is the human factor. Levels 2 and 3 create a paradox: the system performs most of the driving tasks, yet the human must remain attentive. Cognitively, this is demanding. Research shows that overreliance on driver-assistance systems contributes to a share of incidents. True autonomy requires not only technological readiness but also a transformation in user behavior and expectations.
- The fourth is infrastructure standardization. Moving from a “smart vehicle” to a fully “connected transport ecosystem” requires integration with traffic management systems, unified data exchange protocols, robust cybersecurity, and interoperability between manufacturers. Without these elements, autonomy will remain a localized solution rather than a systemic transformation.
Autopilot is therefore not a leap into the future but a gradual evolution. Each year, vehicles gain more algorithms, more data, and broader functionality. Yet the transition to full autonomy depends on three simultaneous shifts: technological maturity, regulatory clarity, and economic feasibility. Fully autonomous vehicles have not yet become the norm. But the trajectory is irreversible. Progress unfolds incrementally through limited zones, controlled operational scenarios, and gradual capability expansion. When algorithms, infrastructure, and regulation finally align, autonomy will move beyond technological demonstration and become part of everyday reality.
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