# Smart Intersections and Connected Autonomous Vehicles for Sustainable Smart Cities: A Brief Review
## 4 Keys Technologies for Smart Intersections
However, challenges such as latency, consensus overhead, andenergy consumption raise concerns about the real-time applicability of blockchain, particularly in high-throughput V2X (Vehicle-to-Everything) scenarios. * Integration of connected and autonomous vehicles (CAVs). This is the control of CAVs' movements with traffic control devices [55] and lane-level navigation assistance that improves autonomous vehicle navigation [56; 57]. Automated hazard detection increases safety for all road users [58; 59; 60; 61]. * Dynamic traffic management: Adaptive lane reallocation: temporary lane configurations are leveraged by this approach to improve efficiency during peak hours [62; 63]. Emergency vehicle prioritization: this provides a smooth passage for ambulances and fire trucks [64; 65]. * The safety features of innovative intersections may include the following:
* Signal phasing is modified to avoid accidents [66; 67]. * Pedestrian and cyclist detection with thermal cameras and LiDAR for safe pedestrian and cyclist movement [68; 69; 70]; incident detection systems, real-time monitoring, and notifications to emergency services in real time [71; 72; 73]; and advanced concrete safety barriers capable of withstanding multiple heavy vehicle impacts also enhance intersection safety significantly [74]. Smart intersections will have the capacity to provide environmental benefits through traffic optimization that enhances fuel efficiency and reduces pollution [75; 76]. Pollutant emissions from road traffic can be checked by image processing techniques [11] and air quality monitoring systems. Also, smart intersections may comprise the integration of EV charging stations [77; 78]. Advanced intersections will be able to enhance transportation equity by providing equal opportunities, for instance, by extending pedestrian crossing times for elderly and disabled pedestrians [79; 80] and optimizing bus and tram signals for minimal delay [81; 82; 83; 84; 85]. These innovative intersections can help the mobility integration of various transport modes [5; 86] and can support ride sharing and bike sharing for green transport routes in city centers [87; 88; 89]. Table 1 summarizes the key technologies and functionalities in smart intersections. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Key Technologies** & **Functionality** & **Key Reference(s)** \\ \hline \multirow{2}{*}{Connectivity} & V2X communication & Enables real-time connectivity for & [33; 34; 35] \\  & (V2I, V2V, V2P) & improved traffic flow and safety & \\ \hline \multirow{3}{*}{Artificial intelligence} & AI-based traffic control & Optimizes signal control, reduces & \\  & (adaptive signal control, & congestion, and enhances & [11; 36; 37; 38; 90; 91; 92; 93; 94] \\  & accident prediction) & traffic efficiency & \\ \hline \multirow{3}{*}{Sensing and IoT} & IoT and multi-sensor fusion & \multirow{3}{*}{Enhances real-time monitoring} & \multirow{3}{*}{[9; 40; 41; 42; 43; 44; 45; 46]} \\  & (traffic and environmental & & and adapts traffic control to \\ \cline{1-1}  & monitoring, predictive & environmental conditions & \\ \cline{1-1}  & maintenance) & & \\ \hline \multirow{3}{*}{Computational technologies} & Edge computing and real-time & \multirow{3}{*}{Processes data at the edge for \(\,\Deep neural networks (DNNs), reinforcement learning (RL), and graph neural networks (GNNs) have been widely used as AI methods in connected autonomous vehicles (CAVs) and V2X applications. Although DNNs provide high accuracy in perception tasks, they require large datasets and computational resources, which limits their real-time application [91; 92]. Despite the need for basic training and fine-tuning, RL approaches, especially deep RL, are well suited for dynamic traffic control scenarios [93]. GNNs can be used to model complex interactions between vehicles and infrastructure, which can capture the network dynamics in V2X communications. Still, their disadvantage is their complexity, which can increase computational demands [94].