No abstract

With the increase of per capita car ownership, traffic accidents frequently occur, in which rear-end collision accounts for 30% to 40% of the total accidents; thus, rear-end collision has become the primary factor of traffic environment deterioration. Therefore, how to improve road traffic safety and reduce the probability of rear-end collision has become a major social concern. In this study, based on the safety pre-warning algorithm, a vehicle collision model was built, and a vehicle anti-collision warning system was established. The calculation was performed based on the sample data to obtain the prediction value of vehicle collision time under different driving speeds, so as to provide drivers with effective response time and reduce the casualties and property losses caused by a vehicle collision. The experimental results showed that the accuracy rate of the pre-warning reached 80% when the speed was regarded as a variable, and the simulation results showed that the early pre-warning or delayed pre-warning rate was very low, and the timeliness rate reached 89%, which enables drivers to react quickly in the appropriate time and effectively reduces the risk of vehicle rear-end collision.

This study aims to evaluate the probabilities of pedestrian detection within the response time of vehicle collision avoidance system of a car. For this purpose, the carpedestrian accident scenarios were analyzed using selected data from the IVAC accident database, in which the cases were collected from in depth investigations of the accidents in Changsha of China. The selection criteria were: (1) the accident occurred between 2001 and 2008; (2) the accident involved a passenger car, SUV, MPV or pick-up truck; (3) the pedestrian was not standing still before impact. Based on these criteria, 389 car-pedestrian cases were selected. The two most common scenarios (F1 and F2) were identified as the pedestrian crossing a straight road from the left (F1) or the right (F2) of the drivers. A mathematical model was developed with the frontal impact cases of F1 or F2 scenario. The following four parameters describing the configuration before the accident were studied: the trajectory and speed for both the car and the pedestrian. Considering the different half detective angles of the sensor system (15 degree, 30 degree, 45 degree), the probabilities of pedestrian detection were calculated. It was found that when the half detective angle was equal or larger than 30 degrees the sensor system could detect more than 94% of the pedestrians in both evaluated scenarios. Keywords-trafficsafety; vehicle-pedestrian accident scenarios; pedestrian detection model; vehicle collision avoidance system I.

The objective of the present study is to investigate the stiffness of the knee joint, in order to provide a basis for developing a biofidelic pedestrian legform impactor. A biofidelic lower limb model was employed to replicate structural responses of the human knee joint by finite element simulations. In the simulation, a single displacement was imposed on the thigh or leg, and constrained six freedoms of the other part. As a result, nonlinear load-displacement data were approximated by a linear regression to determine the stiffness. Considering knee joint kinematics under lateral car-pedestrian impacts, a stiffness matrix was established including lateral bending, lateral shearing and torsion effects that significantly influence pedestrian lower limb injuries. Then, this stiffness matrix was applied in a developed legform model. The structural responses of the legform were obtained by the impact with a family car model. Finally, the legform biofidelity was evaluated by comparing the global kinematics of the pedestrian lower limb.