A Novel Approach for Estimating Vehicle Speed in Nighttime Traffic Accidents from Daytime Video Information

In vehicle accident investigations, the pre-accident speed of the vehicle is an important factor that affects the reliability of accident reconstruction results. However, it is difficult to analyze vehicle speed in nighttime videos where the vehicle and its surroundings are not clearly visible. This study proposes a novel approach to estimate the speed of an accident vehicle in nighttime videos using daytime videos. The proposed method first estimates some missing coordinates from the wheel center coordinate data extracted from all daytime video sequences. Then, the vehicle speed in the nighttime video is estimated with a calibration curve that transforms the interpixel distance in the 2D video to the distance in the real 3D space using the cross-ratio. Experimental results show that the proposed Kalman filter-based method is effective in estimating the occluded coordinates and is useful in calculating the crossratio. Additionally, the vehicle speeds in the nighttime video estimated by the proposed method are similar to the simulated and measured actual vehicle speeds, as well as the real accident analysis case. These results show that the proposed method can be used to complement and effectively cross-validate existing methods to improve reliability in the field of vehicle accident investigations.