Video-based trajectory extraction with deep learning for High-Granularity Highway Simulation(HIGH-SIM)

High-granularity vehicle trajectory data can help researchers develop traffic simulation models,understand traffic flow characteristics,and thus propose insightful strategies for road traffic management.This paper proposes a novel vehicle trajectory extraction method that can extract high-granularity vehicle trajectories from aerial videos.The proposed method includes video calibration,vehicle detection and tracking,lane marking identification,and vehicle motion characteristics calculation.In particular,the authors propose a Monte-Carlo-based lane marking identification approach to identify each vehicle's lane.This is a challenging problem for vehicle trajectory extraction,especially when the aerial videos are taken from a high altitude.The authors applied the proposed method to extract vehicle trajectories from several high-resolution aerial videos recorded from helicopters.The extracted dataset is named by the High-Granularity Highway Simulation(HIGH-SIM)vehicle trajectory dataset.To demonstrate the effectiveness of the proposed method and understand the quality of the HIGHSIM dataset,we compared the HIGH-SIM dataset with a well-known dataset,the NGSIM US-101 dataset,regarding the accuracy and consistency aspects.The comparison results showed that the HIGH-SIM dataset has more reasonable speed and acceleration distributions than the NGSIM US-101 dataset.Also,the internal and platoon consistencies of the HIGH-SIM dataset give lower errors compared to the NGSIM US-101 dataset.To benefit future research,the authors have published the HIGH-SIM dataset online for public use.