@article{LI2026, 
author = {Yi LI and Gang HUANG and Langlang QIN and Yong YAN},
title = {Research on Accident Investigation and Analysis Technology of Assisted Driving Vehicles Based on On-board Video and Operational Data},
year = {2026},
journal = {Chinese Journal of Forensic Sciences},
volume = {2026},
number = {5},
pages = {91-100},
keywords = {operational data, object detection, YOLOv8, assisted driving, perception system, accident investigation},
url = {https://www.sciopen.com/article/10.3969/j.issn.1671-2072.2026.05.012},
doi = {10.3969/j.issn.1671-2072.2026.05.012},
abstract = {ObjectiveGiven the limitations of conventional methods for the quantitative assessment of the perception capability of advanced driver assistance systems (ADAS), an investigation and analysis technology was established to retrospectively evaluate the object recognition functional performance prior to a crash, providing technical support for accident causation determination and ADAS-related function investigation.MethodsOn-board video from the accident vehicle was used as the primary data source. A YOLOv8 object detection model was trained using the publicly available KITTI 2D dataset and a traffic cone dataset. Frame-by-frame detection was performed, and the detection time, model output category, and temporal changes of key objects were extracted and cross-compared with the ADAS perception data recorded in the accident vehicle's operation log.ResultsThe YOLOv8 model detected the vehicles, the road workers, and the traffic cones in the accident vehicle's on-board video, with the first detection frame of the vehicle object approximately 4.97 s from the collision frame. At each sampling time within 7 s before the collision, the accident vehicle's operation log recorded the object recognition type as “no object”. Comparison of the two sets of data showed differences between the external video detection results and the object recognition records in the log data, and the accident vehicle missed relevant objects in the actual scenario, indicating that the accident vehicle's perception system may have deficiencies in the perception and recognition of critical objects, including stationary vehicles, traffic cones, and road workers.ConclusionThe method proposed in this study can reconstruct the detection history of key objects in accident videos and analyze differences in ADAS object recognition before the accident, enabling retrospective analysis of the object recognition function of the accident vehicle's perception system. It provides technical support for accident investigation involving assisted-driving vehicles and for the improvement of ADAS perception performance.}
}