A virtual reality method for digitally reconstructing traffic accidents from videos or still images

Multiobjective optimization algorithm for accurate MADYMO reconstruction of vehicle-pedestrian accidents

In vehicle-pedestrian accidents, the preimpact conditions of pedestrians and vehicles are frequently uncertain. The incident data for a crash, such as vehicle deformation, injury of the victim, distance of initial position and rest position of accident participants, are useful for verification in MAthematical DYnamic MOdels (MADYMO) simulations. The purpose of this study is to explore the use of an improved optimization algorithm combined with MADYMO multibody simulations and crash data to conduct accurate reconstructions of vehicle-pedestrian accidents. The objective function of the optimization problem was defined as the Euclidean distance between the known vehicle, human and ground contact points, and multiobjective optimization algorithms were employed to obtain the local minima of the objective function. Three common multiobjective optimization algorithms-nondominated sorting genetic algorithm-II (NSGA-II), neighbourhood cultivation genetic algorithm (NCGA), and multiobjective particle swarm optimization (MOPSO)-were compared. The effect of the number of objective functions, the choice of different objective functions and the optimal number of iterations were also considered. The final reconstructed results were compared with the process of a real accident. Based on the results of the reconstruction of a real-world accident, the present study indicated that NSGA-II had better convergence and generated more noninferior solutions and better final solutions than NCGA and MOPSO. In addition, when all vehicle-pedestrian-ground contacts were considered, the results showed a better match in terms of kinematic response. NSGA-II converged within 100 generations. This study indicated that multibody simulations coupled with optimization algorithms can be used to accurately reconstruct vehicle-pedestrian collisions.

Analysis of Pedestrian Fractures in Collisions Between Small Cars and Pedestrians Based on Surveillance Videos

OBJECTIVE

To discuss the collision relationship and the cause of the fracture caused by traffic accidents in which the front of a small car collides with the side of a pedestrian while braking.

METHODS

The surveillance videos of 42 traffic accidents involving the front of a small car colliding with the side of a pedestrian while braking were collected. By analyzing the surveillance videos and the paths, the speed of the collision, the relationship between the vehicle and the pedestrian upon collision, and the movement trajectory of the human body were clearly identified. The type and severity of the injuries were also determined through autopsy. The characteristics of the human injuries and vehicle paths were analyzed according to the collision speed (<40 km/h, 40-60 km/h, 60-90 km/h), and the correlations between the fracture and the height of the pedestrian, the height of the hood and the length of the hood were discussed.

RESULTS

When a small car hits the side of a pedestrian, the front bumper first hits the lower limbs of the pedestrian, and then, the human body falls to the side of the vehicle, causing a secondary collision with the hood and front windshield; thus, the pedestrian is thrown at a speed similar to the speed of the vehicle, finally falling to the ground and sliding forward a certain distance. (1) When V is less than 40 km/h (n = 10), the pedestrian's head did not collide with the windshield, and the fatal injuries were caused by the individual striking the ground. (2) When V is greater than 40 km/h (n = 32), the majority (97%) of cases showed collision with the windshield. (3) When 40 to 60 km/h (n = 16), the pedestrian's head collided with the windshield, which can cause fatal injuries, and pelvic fractures and rib fractures occurred in 56.25% of patients. (4) When V is less than 60 km/h (n = 26), the ratio of the height of the pedestrian to the height of the hood was significantly smaller in the pelvic fracture group than in the nonpelvic fracture group (P < 0.01). (5) When 60 to 90 km/h (n = 16), there were holes in the windshield, and the pedestrians experienced severe head injuries, with cervical spine fracture occurring in 37.5% of patients, pelvic fractures occurring in 43.75% of patients, and rib fractures occurring in 31.25% of patients.

CONCLUSIONS

When V is less than 40 km/h, the vehicle does not cause severe injuries in pedestrians; when V is greater than 40 km/h, the collisions of the pedestrian's head with the windshield lead to severe head injuries and the accident can cause severe pelvic and rib fractures; when V is greater than 60 km/h, the collisions of the pedestrian's head with the windshield can cause cervical spine fracture in addition to head injuries. The occurrence of human injuries is related to not only the vehicle speed but also factors such as the height of the pedestrian, the height of the hood and the length of the hood.

Simulation-based reconstruction of traffic incidents from moving vehicle mono-camera

Video recordings from digital cameras implemented in road vehicles present a valuable source of data concerning various dangerous traffic situations, i.e., road accidents or near-miss incidents. This data is readily available and numerous methods for forensic reconstruction of traffic situations from vehicle video were presented in the past. New alternative method for reconstruction of traffic situations from vehicle video is presented in this article. The method is based on the fusion of kinetic vehicle trajectory simulation within 3D laser scanner point cloud, projective geometry, and processing of video footage from moving vehicle camera. The method offers accurate reconstruction of general vehicle motion within relevant time domain whereby in-depth technical information about traffic incident can be quantitatively extracted from video footage. The result is physics-based 3D projection of simulated vehicle motion on the motion of real vehicle recorded by moving monocular camera. The method was validated within performed experimental test runs with respect to vehicle speed, distance travelled, acceleration/deceleration and directional quantities (yaw rate, yaw angle). The method was further applied in the reconstruction of real-world traffic events.

Using the scanners and drone for comparison of point cloud accuracy at traffic accident analysis

The purpose of the paper is to describe, compare and analyse the instruments used, time needed and accuracy of gathered data, sketches, 3D models and to enhance the extracted information about the accident. Simple sketches and tape measurements were performed. Also complex 3D measurements and 3D modelling of the scene with Terrestrial Laser Scanners (TLS) and Unmanned Aerial Vehicle (UAV) technology were used. A classical police work dealing with a simulated traffic accident was compared to sketches obtained from 3D models from Riegl VZ-400i 3D, Faro Focus S70, Geoslam ZebRevo 3D TLS and Topcon Falcon 8 drone. For 3D modelling an orthophoto from drone photos and point clouds were obtained. 3D models were graphically compared in CloudCompare software. Sketches were made for each measuring method and their accuracies were also compared one to each other. The graphical distance accuracy in scene measurements ranged up to 17 cm in comparison to police measurement but in the most course point cloud. Average absolute difference in compared distances amounts up to 6 cm. As expected, more points in the cloud means better 3D model and easier analysis. There is considerable reduction of time needed for collecting the accident scene data. The obtained 3D model is a permanent archive of the scene of a traffic accident. From the cadre, both visual and dimensional information subsequently can be obtained.