# Smart Intersections and Connected Autonomous Vehicles for Sustainable Smart Cities: A Brief Review
## 4 Keys Technologies for Smart Intersections  
Smart intersections are important junctions in the traffic network of a city that are controlled and monitored with the help of modern technologies to increase safety, traffic capacity [31; 32], and environmental friendliness. They integrate next-generation vehicle communication, artificial intelligence, IoT sensors, edge computing, and real-time traffic modeling to maximize efficiency and enable next-generation transportation systems. The main technologies that support these intersections are as follows:  
* V2X communication, enabling connectivity for traffic optimization. The technology known as V2X establishes real-time communication between vehicles, infrastructure, and pedestrians to improve traffic flow and safety. Other communication systems include vehicle-to-infrastructure (V2I), which improves capacity and reduces congestion through dynamic signal control [33]; vehicle-to-vehicle (V2V), which may prevent accidents and optimize lane usage [34]; and vehicle-to-pedestrian (V2P), which can be used, for instance, for real-time pedestrian signals [35]. * Traffic management and control with the help of artificial intelligence. This topic includes AI and machine learning algorithms that leverage camera, sensor, and vehicle-connected data to enhance traffic efficiency; adaptive signal control: real-time operation based on traffic congestion levels [36]; accident prediction and emergency response: AI detects potential dangers and acts before the danger occurs [37; 38]; and enhanced signal control efficiency, i.e., the application of machine learning in congestion modeling [39]. Recent advances in image processing techniques also enable the precise reconstruction and measurement of the transverse profiles of worn-out tracks, significantly contributing to predictive maintenance and the early detection of railway and tramway track deterioration in an urban context [11; 40]. * IoT and multi-sensor data fusion for traffic sensing. IoT-based systems improve intersection control by integrating data from multiple sources: real-time traffic monitoring, peak-hour traffic flow, and speed [40]. Environmental monitoring: sensors that detect fog, rain, and air pollution can be used to modify traffic controls [9]. Predictive maintenance allows for the immediate identification of potential failures in infrastructure for timely replacement [41]. * Advanced sensing technologies: improving situational awareness. These technologies include LiDAR (Light Detection and Ranging), which gives a very clear view of traffic flow and movement [42]; thermal and radar imaging, through which vehicles and pedestrians can be detected during night and fog conditions [43; 44]; and multi-sensor fusion: LiDAR, cameras, and radar for better safety [45; 46]. * Edge computing: real-time data processing for low-latency decisions. Decentralized traffic management reduces latency in traffic light learning processes [47]. Cooperative vehicle maneuvering enhances the coordination between conventional and automated vehicles and real-time congestion management. * Cybersecurity in smart intersections. End-to-end encryption ensures secure Vehicle-to-Everything connectivity [48]. Intrusion detection systems (IDSs) prevent security threats [49] and ensure data integrity with blockchain features to prevent data tampering [50; 51]. Tamper-proof identity management for vehicles and infrastructure units is one of the most important outcomes of blockchain-based authentication systems enabled by distributed ledgers [52]. For resource-constrained automotive environments, lightweight blockchain models such as Hyperledger Fabric and IOTA's Tangle are suitable [53]. Secure and auditable data sharing requires enforcing access rules and using time-stamped transactions--both of which can be effectively implemented through smart contracts [54].