# Smart Intersections and Connected Autonomous Vehicles for Sustainable Smart Cities: A Brief Review
## 9 Challenges and Future Directions  
While the smart intersection has the potential power to drive the transformation of the urban context, several issues must be solved before its widespread integration into urban transportation systems. In particular, the following challenges can be considered:  
* Smart component standardization gaps: incompatible data formats and protocols of various smart city components could prevent efficient data exchange and interoperability [225; 226]. * Scalability restrictions: due to more and more traffic data available, cloud and mobile data storage architectures are facing bigger scalability problems which need next-level AI-driven data compression/prioritization tactics [227; 228]. * Poor interagency coordination: smart intersection projects run into problems as a result of differences in current data-sharing policies among transport agencies, private mobility providers, and urban planners [229], and the development of standardized data protocols, interoperable APIs, and better cross-agency collaboration is needed to resolve these issues. * Data breaches and hacking possibility: hacking could lead to unauthorized real-time traffic data and AI-based control systems to command traffic signals, allowing system shut down or vehicle following [230]. * Privacy for pedestrians and drivers: camera-based monitoring, along with the AI tracking of pedestrian and biker trajectories, raises concerns over surveillance, data misuse, and enforcement compliance [13]. * Robust security protocols: effective security measures (e.g., end-to-end encryption, blockchain-based authentication, intrusion detection) are required for data protection and operational security [231; 232]. * Construction and maintenance costs: Transitioning to smart intersections gives rise to substantial investment in digital infrastructure, sensor networks, and control systems driven by AI. These include the deployment of LiDAR, edge computing units, V2X modulators, and AI-based adaptive traffic management systems that introduce financial barriers for most municipalities [233; 234]. On the other hand, these intersections require periodic firmware and software updates to stay compatible with future technologies ranging from 5G/6G networks to autonomous vehicles [235; 236]. Finally, funding shortfalls and policy impediments make it challenging for governments to secure long-term funding for smart infrastructure projects that work well with public-private partnership (PPP) requirements and new financing models [237]. Driver and pedestrian acceptability: in many countries, the current users of traffic management systems may resist AI-controlled signal optimization and intersection tuning in real time [238; 239]. Autonomous vehicle interaction concerns: while smart intersections are conceived to be interoperable in the world of connected and autonomous vehicles (CAVs), safety, reliability, and ethical decision-making in mixed-traffic conditions remain a major issue [240; 241]. Public engagement: municipalities must implement general awareness campaigns (pilot projects) and community engagement activities to inform drivers, cyclists, and pedestrians of the advantages of intelligent intersections and the importance of security measures [242; 243]. With cities moving towards intelligent mobility ecosystems and their implementation paradigms, future research must look beyond the near-term emerging technological (AI, IoT) and policy/regulatory challenges. Some of the research directions are as follows: Improving the efficiency of adaptive traffic signal control by using machine learning, deep reinforcement learning (DRL), and swarm intelligence: Future work will focus on the following research priorities: Self-tuning AI systems adapt on the fly to changing traffic situations, which means no configuration by humans, and they are continuously enabled [244; 245].