Vehicle collision risk estimation based on RGB-D camera for urban road

Abstract

Traffic violation is the main cause of traffic accidents. To reduce the incidence of traffic accidents, the common practice at present is to strength the penalties for traffic violation. However, little attention has been paid to issue warning for dangerous driving behaviors, especially for the case where two vehicles have a good chance of collision. In this paper, a framework for collision risk estimation using RGB-D camera is proposed for vehicles running on the urban road, where the depth information is fused with the video information for accurate calculation of the position and speed of the vehicles, two essential parameters for motion trajectory estimation. Considering that the motion trajectory or its differences can be considered as a steady signal, a method based on autoregressive integrated moving average (ARIMA) models is presented to predict vehicle trajectory. Then, the collision risk is estimated based on the predicted trajectory. The experiments are carried out on the data from the real vehicles. The result shows that the accuracy of position and speed estimation can be guaranteed within urban road and the error of trajectory prediction is very minor which is unlikely to have a significant impact on calculating the probability of collision in most situations, so the proposed framework is effective in collision risk estimation.