This paper proposes a method for calibrating the external parameters of the LDS sensor and ToF depth camera based on three cylinders. This method obtains the scanning data of the side surfaces of the three cylinders at different postures by changing the posture of the robot. For the single-line laser plane scanned by the LDS sensor, three elliptical contours are obtained by intersecting with the side surfaces of the three cylinders respectively. The Random Sample Consensus (RANSAC) algorithm is used to obtain the coordinates of the center points of the three elliptical contours and two random points on each elliptical contour. For the three-dimensional point cloud image of the cylinder scanned by the ToF depth camera, the RANSAC algorithm is used to fit the central axis of the three cylinders. The nonlinear optimization equation is established using the three center points obtained from the three elliptical contours and the distances from the two random points on each elliptical contour to their corresponding central axes. In this paper, we propose to use a fusion method of the Powell algorithm and the BFGS algorithm to solve the nonlinear optimization equations to obtain the transformation matrix between the LDS sensor and the ToF depth camera. Finally, simulation and actual test are carried out based on the proposed method, and the influence of the initial value of the calibration parameter on the calibration result is discussed. The accuracy of the calibration algorithm in this paper is verified through comparative experiments of the calibration algorithm. The results show that the calibration accuracy of the proposed method is better than that of the traditional planar calibration method, and it has the characteristics of simple operation and high calibration accuracy.

Real time traffic crash risk prediction is very important for proactive safety management systems. In this paper, a new model is developed to predict real time traffic crash risk based on some elaborately selected characteristics of traffic flow. Compared with the dominant conventional matched case-control logistic regression model which is based on the assumption that all the traffic crashes share the same contributing factors and ignores the variability of traffic flow and heterogeneity of crash causations, the latent class logit model can considerably account for the unobserved heterogeneity. Data from Shanghai urban expressway between April to June 2014 are utilized to build the latent class logit model. The collected data are divided into a training set for model building and a validation set for model evaluation by random selection. Some criteria including Akaike information criterion (AIC), area under receiver operating characteristic curve (AUC), sensitivity and specificity are used to evaluate the model quality. The experimental results show that the overall accuracy of the latent class logit model is 70.23% and AUC of the model is 0.7576, which are both higher comparing to the traditional widely used case-control logistic regression model.