RASID: a secure UAV-based platform for intelligent traffic accident assessment with cryptographic verification and AI-driven analysis

Traffic accident management typically deals with delays from the time an accident is reported to the time of the actual submission of the final report, and this ultimately causes traffic congestion. The process can be done in a significantly shorter time compared to the traditional way by utilizing unmanned aerial vehicles (UAVs) in accident management, especially drones. This project aims to provide a simulation of a secure drone platform to assess vehicle traffic accidents. This approach eliminates the demand for an investigator's presence on the scene, which speeds up the process of submitting accident reports and cuts down on response time. Furthermore, the research proposes security measures to ensure the integrity and confidentiality of all gathered data by a drone in both aspects of in-transmission and storage. The common risks of gathering data by drone include unauthorized interception, access, and possible alteration of data in transmission between the drone and the ground station. The current traffic accident management mostly experiences delays between the incident reporting and final documentation, which creates a jam on the streets and ineffective response by authorities. This study introduces RASID, a secure drone-based system that aims to automate the incident assessment process, assure the integrity and confidentiality of data, and speed up reporting. The project simulates realistic drones through the employment of the AirSim tool, the authentication and encryption methods were professionally verified using ProVerif, and utilized YOLOv8-based AI models for incident investigation and automated liability assessments. High-resolution photographs of the incident scene are automatically taken by the drones, and TLS encryption is implemented to transfer the data to a secure cloud. After that, the data is encrypted with AES-256 and verified using OpenID Connect. The ProVerif results showed that messages could not be accessed or altered without authorization, proving that the exchanges among the nodes were private and authentic. The AI module achieved a precision of 0.6919, a recall of 0.6244, F1 score of 0.6564, and mAP@50 of 0.6717. It was most precise in two scenarios: rear-end and front-end collisions. The findings demonstrate that the RASID system is capable of securely collecting, transmitting, and analyzing accident data, enabling nearly real-time crash assessments. This study provides the improvements of efficiency, accuracy, and cybersecurity of traffic accident management via the integration of secure drone operations, well-known and proven encryption mechanisms, along with AI-powered analytics, when compared to the traditional crash assessment methods.