Integrate Reconstruction Networks and Adaptive Artificial Intelligence Models to Mitigate V2X Transmission Losses and Localization Errors.

Through several case studies based on field experiments, this study showed the following lessons learned:

• Integrate Reconstruction Networks and Adaptive AI Models to Mitigate V2X Transmission Losses and Localization Errors. This is needed because V2X transmission introduces latency, packet loss, and synchronization errors that degrade perception in dynamic conditions.
• Extend the framework to support additional sensor modalities such as monocular and stereo cameras to improve perception robustness.
• Harden individual components while implementing system-level safeguards that prevent vulnerabilities from propagating across interconnected systems. Because CAV operations depend on tightly integrated vehicles, networks, cloud services, and infrastructure, security approaches must address vulnerabilities across the entire system rather than focusing on individual components.
• Using shared-state or collaborative mechanisms across vehicles, teleoperators, and infrastructure can enable lightweight detection of attacks, such as sensor data injection, without significantly increasing communication latency.
• Account for imperfect network and hardware noises. Packet loss, localization errors, and synchronization delays can degrade collaborative perception. It is necessary to design perception systems to tolerate real-world communication and sensor limitations.
• Use infrastructure as an independent verification layer for cybersecurity monitoring. Infrastructure-based sensing and trajectory prediction can provide an independent perspective to detect abnormal vehicle behavior caused by cyberattacks and support safer CAV operations.