Mobility is undergoing a profound transformation. Vehicle automation, until recently viewed as a standalone technological advancement, is now being deployed in real-world environments, revealing a structural reality: the autonomous vehicle is just one component within a broader system, whose central pillar is infrastructure.
Road safety data clearly illustrates the scale of the challenge. Globally, approximately 1.35 million people die each year in traffic accidents, and between 20 and 50 million suffer non-fatal injuries, according to the latest estimates from the World Health Organization and other international sources. More than 90% of these accidents are directly or indirectly attributable to human error—such as distraction, excessive speed, or driving under the influence of substances. This context has been one of the primary drivers, for over two decades, behind the development of increasingly automated vehicles.
Advances in artificial intelligence, sensing technologies, and high-performance computing have enabled the emergence of vehicles that not only incorporate advanced driver assistance systems, but are also capable of operating autonomously under real-world conditions. Recent announcements by technology companies and manufacturers—introducing certified autonomous systems—signal the beginning of a global-scale deployment of thousands of vehicles with advanced autonomous driving capabilities as early as 2026.
However, the most significant barrier to large-scale adoption is not purely technological. Legislative constraints, societal challenges, and business model debates all play a role. Yet among these, the most critical and urgent challenge is the transformation of road infrastructure.
Infrastructure in the Era of Connected and Autonomous Vehicles
Today’s vehicles rely on conventional road networks designed for human drivers—who interpret signals, make decisions, and ensure safety. Autonomous vehicles, by contrast, are highly sensitive machines that generate and process vast amounts of data, and whose safety performance depends not only on onboard sensors but also on cooperative capabilities—namely communication and synchronization with other vehicles and infrastructure.
To unlock this potential at scale, infrastructure must evolve across three key dimensions:
1. Digital Infrastructure
Highly precise digital models of the road network—digital twins or high-definition (HD) maps—are required to provide richer information than what vehicle sensors alone can deliver. These models reduce uncertainty and enhance the prediction of both vehicle behavior and that of other agents in the environment.
For autonomous vehicles, navigation is no longer a simple route calculation problem; it becomes a critical function requiring centimeter-level accurate HD mapping, as well as dynamic information on lane status, roadworks, variable signage, temporary speed limits, and real-time incidents. In this sense, digital infrastructure becomes an extension of the vehicle’s perception system, enabling it to anticipate scenarios beyond its line of sight and improve decision-making.
Moreover, this digital layer does not only benefit vehicles. For infrastructure managers—public authorities and operators—digital twins enable new use cases: predictive maintenance planning, traffic scenario simulation, impact assessment of roadworks or regulatory changes, and investment optimization. The digitalization of road assets transforms infrastructure into a data-driven, actively managed system rather than one reliant solely on physical inspection.
2. Cooperative Communication Networks
Technologies such as Cooperative Intelligent Transport Systems (C-ITS) enable information exchange between vehicles (V2V), between vehicles and infrastructure (V2I), and between vehicles and other actors in the environment (V2X). This communication layer is essential for services such as early hazard warnings, dynamic speed management, and congestion notifications.
A cooperative network allows each vehicle not only to perceive its immediate surroundings but also to receive aggregated, system-wide information in real time. This includes incidents beyond sensor range, road surface conditions, temporary obstacles, the presence of emergency vehicles, and changes in variable signage. Through this connectivity, vehicles can anticipate critical situations and make optimal driving decisions before they fully materialize—significantly improving both safety and traffic efficiency.
3. Automated Traffic Management
By integrating data from sensors, vehicles, and digital platforms, it becomes possible to develop automated traffic control systems capable of optimizing traffic flow in real time—reducing congestion and enhancing safety beyond the capabilities of traditional fixed signaling systems.
However, this is not merely an evolution of existing traffic management centers. Automated traffic management represents a paradigm shift: much like autonomous vehicles themselves, control systems will operate autonomously, relying on optimization algorithms and machine learning to make real-time decisions without direct human intervention.
This has profound implications for system design. In a scenario where traffic is predominantly composed of connected autonomous vehicles, optimization is no longer limited to controlling traffic lights or variable message signs—it can directly influence vehicle routing. Infrastructure becomes an active participant in dynamic trajectory planning, redistributing traffic flows before bottlenecks emerge.
This systemic coordination capability is key to addressing the structural problem of congestion. Whereas current models react to traffic jams, the new paradigm enables anticipation and prevention through cooperative algorithms that optimize the entire system, rather than individual vehicles in isolation.
Together, these three elements form the foundation of an active, cooperative infrastructure—moving beyond the traditional paradigm of passive physical infrastructure.
Two Approaches to Infrastructure Transformation
The gradual deployment of autonomous vehicles inevitably requires infrastructure adaptation. Two strategic approaches are emerging: bottom-up and top-down.
A) Bottom-up Approach: Incremental Evolution
This is the predominant model in Europe. It involves progressively implementing specific C-ITS services and use cases on existing infrastructure, following standards defined by organizations such as ETSI and coordination platforms like C-Roads.
C-Roads brings together multiple EU Member States and infrastructure operators to harmonize the deployment of cooperative transport services, ensuring interoperability across regions and manufacturers. Within this framework, C-ITS services are developed in stages—from basic notification services (“Day 1”) to more advanced applications (“Day 3”).
A notable example is the European SCALE project (Strengthening C-ITS Adoption and Lining-up across Europe), funded by the Connecting Europe Facility (CEF) and involving entities from multiple countries. Its objective is to accelerate large-scale deployment of mature C-ITS services, validate interoperability, and assess their impact on safety and efficiency.
The strength of this approach lies in its alignment with standards and its ability to test solutions in real-world contexts before scaling. However, its main limitation is that incremental implementation can slow down deployment timelines, create regulatory fragmentation, and lead to dispersed investments that may not converge into a unified long-term architecture.
B) Top-down Approach: Designing for an Automated Future
In contrast, an alternative approach is based on a deterministic assumption: that 100% of traffic will eventually become automated in the medium term, whether this takes 10 or 20 years. Under this model, infrastructure transformation is not incremental—it is a redesign from the outset to support a fully connected and automated ecosystem.
This approach entails:
- Designing road networks as integrated data platforms, with communication and sensing capabilities as native components
- Embedding low-latency connectivity (5G / ITS-G5), edge computing capabilities, and management nodes along strategic corridors
- Developing predictive traffic management architectures based on big data and cooperative algorithms
Some Asian countries—particularly China—are closer to this model. The coordinated deployment of 5G infrastructure, smart corridors, and autonomous driving pilot cities reflects a nationally integrated strategy aligned with broader digitalization and industrial innovation goals. Centralized planning and the ability to mobilize public investment enable rapid scaling, shortening the gap between pilot projects and mass deployment.
This approach is based on a clear strategic premise: if the end state is a predominantly autonomous system, designing infrastructure for that future from the outset avoids redundancy and prevents transitional investments from becoming obsolete.
The strategic question is therefore clear: should we adapt infrastructure originally designed for human drivers, or design a new architecture optimized for cooperative algorithms?
Conclusion: A Holistic Vision for Future Infrastructure
The transition to automated mobility is not merely a technological challenge centered on vehicles. It is fundamentally a systems challenge, where road infrastructure must evolve from a passive physical support into an active, digital, and cooperative platform designed to maximize safety, efficiency, and sustainability. Roads must incorporate a new layer of intelligence.
This transformation will not happen overnight—it will require coordination between public authorities, manufacturers, operators, and harmonized regulatory frameworks. But the direction is clear: the full potential of autonomous vehicles cannot be realized without infrastructure capable of supporting them both physically and digitally. And the strategy adopted for this transformation will ultimately determine who leads the future of automated mobility.
