Towards automated traffic accident reconstruction using ITS and multi-sensor data fusion

Traffic accident reconstruction is essential for understanding collision dynamics and supporting forensic, insurance, and legal investigations. This paper presents a unified software platform that integrates heterogeneous data sources, including Event Data Recorder (EDR) telemetry, Light Detection and Ranging (LiDAR), drone photogrammetry, and Cooperative Intelligent Transport Systems (ITS) data, into a single analytical environment. The proposed solution fuses spatial, temporal, and contextual information to enable consistent, evidence-based 3D reconstruction of crash scenarios. A prototype implementation demonstrates the feasibility of multi-source data harmonization and visualization, supporting reproducible and objective analysis. The architecture, data flow, and functional results are described, highlighting the system’s potential contribution to more transparent and data-driven accident investigation. • Integration of heterogeneous data sources (EDR, LiDAR, drone photogrammetry, and ITS data) into a unified analytical platform. • Novel multi-source data-fusion methodology ensuring spatial–temporal synchronization and contextual consistency. • High-fidelity 3D visualization designed for forensic, insurance, and legal applications. • Reduction of human interpretation and dependence on assumptions through objective, sensor-derived data.