# Smart Intersections and Connected Autonomous Vehicles for Sustainable Smart Cities: A Brief Review
## 8 Smart Pavements in Smart Intersections
The issue is not just the detection of surface defects; it is the integration of real-time pavement intelligence into the digital environment of the new generation of these intersections. - High-resolution multi-sensor pavement surveillance. Current Pavement Condition Assessments (PCAs) are not very frequent or detailed. Therefore, the new generation of automated intersections must incorporate embedded multi-modal sensor grids (fiber optic, piezoelectric, acoustic emission, and thermal sensors) to monitor the condition of the surface, loading and stresses, and microstructural fatigue. This aligns with the advancements in sensor-grid infrastructure discussed by [209], which highlights multi-modal sensing for pavement health monitoring. Real-time data processing will be crucially important for edge computing-based intelligent decision-making to generate the required maintenance plans [210]. - Al-powered predictive degradation models. The nonlinear and multivariable nature of pavement deterioration makes it necessary to develop AI-enabled digital twins that can predict the degradation trends as a function of vehicle traffic, the climate, and vehicle automation [12]. It is thus possible to use machine learning models trained with historical and real-time sensor data to predict the time and place of maintenance needs to avoid failures and minimize maintenance costs. - The pavement-CAV symbiosis. CAVs depend on good localization, strong vehicle-to-infrastructure communication, and a good surface [211]. Nevertheless, pavement flaws, cracks, and variations in load and dynamic deflections affect LiDAR, radar, and camera-based perception systems [212]. Therefore, it is crucial for smart intersections to incorporate \"CAV-sensitive pavement intelligence\", i.e., the real-time modification of signal phasing, lane closure, and vehicle paths based on the pavement condition. - Embedded energy harvesting and smart self-healing materials. The next level of intelligent intersections is self-sustaining pavement systems. The next generation of intersections will incorporate piezoelectric materials that can convert the kinetic energy of vehicular movement into electricity to power the embedded IoT networks and data transmission [213; 214; 215; 216]. At the same time, the development of nanoscale self-healing asphalt [217; 218] and bio-inspired concrete [219] will allow pavements to heal themselves from small cracks and avoid major failures [220]. - Cyber-resilient and blockchain-based pavement data. As pavement monitoring is shifting from periodic assessment to real-time analysis, properly managing data credibility, proof, and security is necessary. Using blockchain technology in pavement monitoring can generate immutable and shared records of the infrastructure's health [221; 222], which can help multiple stakeholders avoid fraud and make better decisions about the need for maintenance. - Smart intersections and sentient infrastructure. The next generation of AI-driven intersections must move beyond the traditional break/fix model of pavement management and move towards proactive, autonomous infrastructure. This transformation can only be realized by changing urban mobility principles and making roadways not only physical paths for vehicles but also an intelligent infrastructure of the transportation network that can sense, decide, and heal itself [223; 224]. - Thus, integrating AI, edge computing, self-healing materials, and cybersecurity data networks will make these intersections not only the traffic nodes but also the intelligent control points of the urban environment that will guarantee the integration of classical and automated transport. Figure 5 presents a layout for intersection systems that combine sensors embedded in the pavement (such as acoustic sensors or fiber optic sensors), IoT processing capabilities, and edge AI technology.