A Tachograph-Based Approach to Restoring Accident Scenarios From the Vehicle Perspective for Autonomous Vehicle Testing

Long-tail scenarios are of great significance in improving the testing efficacy of autonomous vehicles, but they are difficult to collect due to their inherent randomness and high risk. The low-cost, high-value videos of vehicle-perspective accidents stored in tachographs provide a potentially important source for long-tail data collection. However, the lack of multisource synchronization information and the unknown, diverse camera parameters in tachograph videos make it difficult for the existing methods to accurately extract key information. In response to these issues, this work proposes a vehicle-perspective accident scenario restoration framework based on tachograph videos. First, using preprocessed accident videos as the input, a scenario semantic understanding module was constructed to help extract the pixel trajectories, conduct parameter calibration, and obtain pixel road boundaries. Then, robust detection and tracking algorithms were used to extract the pixel trajectories. Furthermore, a high-precision calibration algorithm was proposed based on the Geometrically Salient Feature Bundle Adjustment (GSFBA). Finally, the camera parameters were used to perform an inverse perspective transformation on the pixel coordinates, the actual road boundaries were extracted based on the mapping strength of the pixel road boundaries, and the trajectory information was optimized using a systematic trajectory processing module. The proposed method was verified at multiple levels based on both two simulation scenarios and two real-world scenarios. The results show that the proposed framework in this work can obtain the key information of the scenario with a high degree of restoration accuracy. It was effectively applied to the restoration process of accident scenarios.