Abstract: A LIDAR-based method of determining an absolute speed of an object at a relatively longer distance from an ego vehicle, including: estimating a self speed of the ego vehicle using a first frame t?1 and a second frame t obtained from a LIDAR sensor by estimating an intervening rotation ? about a z axis and translation in orthogonal x and y directions using a deep learning algorithm over a relatively closer distance range; dividing each of the first frame t?1 and the second frame t into multiple adjacent input ranges and estimating a relative speed of the object at the relatively longer distance by subsequently processing each frame using a network, with each input range processed using a corresponding convolutional neural network; and combining the estimation of the estimating the self speed with the estimation of the estimating the relative speed to obtain an estimation of the absolute speed.

Abstract: Methods and systems include receiving invoice data associated with an open invoice under an account; determining an expected payment date for the open invoice by inputting the data to a prediction model derived from a machine learning technique; determining an expected payment date range under a confidence level for the open invoice based on the invoice data and the open invoice; determining a probability of receiving a payment for the open invoice by a predetermined date; and outputting the expected payment date, the expected payment date range under the confidence level, and the probability of receiving the payment for the open invoice by the predetermined date for risk management analysis.