International Business Machines Corporation

Ting Yuan

Wei Shan Dong

Jian Ding

Peng Gao

Chang Li

Ning Duan

Renjie Yao

Wei Sun

Zhi Hu Wang

Chun Ma

Wen Han Luo

Ren Jie Yao

Xin Zhang

Jian Li

Kai Yang

Kai Lin Li

Guo Qiang Hu

Shi Wan Zhao

Guoqiang Hu

A method, system, and computer program product for obtaining a first route traversed by a target object, performing at least one prediction for a second route to be traversed by the target object based on the first route, the at least one prediction being performed with at least one of an object-specific prediction model, an object group-specific prediction model, and an object-independent prediction model, and determining, according to a decision rule, a prediction result of the second route based on the at least one prediction.

Route prediction based on adaptive hybrid model

Systems and methods for training a neural network model are disclosed. In the method, training data is obtained by a deep neural network (DNN) first, the deep neural network comprising at least one hidden layer. Then features of the training data are obtained from a specified hidden layer of the at least one hidden layer, the specified hidden layer being connected respectively to a supervised classification network for classification tasks and an autoencoder based reconstruction network for reconstruction tasks. And at last the DNN, the supervised classification network and the reconstruction network are trained as a whole based on the obtained features, the training being guided by the classification tasks and the reconstruction tasks.

Feature extraction using multi-task learning

Embodiments are described for minimizing a wait time for a rider after sending a ride request for a vehicle. An example computer-implemented method includes receiving a ride request, the request being for travel from a starting location to a zone in a geographic region during a specified timeslot. The method further includes predicting travel demand based on a number of ride requests in the zone during the specified timeslot. The method further includes requesting transport of one or more vehicles to the zone in response to the predicted number of ride requests when the travel demand is predicted to exceed a number of vehicles in the zone during the specified timeslot.

Neural network computing systems for predicting vehicle requests

A system for tracking cumulative motor vehicle risk includes a satellite navigation system receiver disposed within a motor vehicle and configured to determine a present location of the motor vehicle. A computer processor receives the determined present location of the motor vehicle from the satellite navigation system receiver and generates a traveled route therefrom. A first computer server receives a plurality of motor vehicle claims records, determines a plurality of motor vehicle accident locations from the plurality of motor vehicle claims records, and generates a motor vehicle accident heat map from the plurality of motor vehicle accident locations. A second computer server determines a cumulative risk exposure of the motor vehicle based on the generated traveled route and the generated motor vehicle accident heat map.

System and method for determining motor vehicle collison risk based on traveled route and displaying determined risk as a map

A system, a computer readable storage medium, and a method for detecting insurance fraud or for comparing references use visual analytics-based techniques. The method can include identifying a scratch and a scratch location on a vehicle in a 3-dimensional rendering in comparison to a base image or in comparison to a stored image in a database, comparing one or more features of the scratch such as color features of the scratch in the scratch location in comparison to the scratch location or comparing one or more texture features of the scratch in the scratch location in comparison to the scratch location in the base image or the stored image. The method can further generate a similarity calculation based on the one or more comparisons for the scratch, the scratch location, the one or more scratch features of the scratch and presents a result in response to the similarity calculation.

Visual analytics based vehicle insurance anti-fraud detection

A method according to the present invention includes predicting a vehicular route. GPS data of a vehicle's position on a road network is received. A digital map representing the road network is received. The digital map includes a plurality of partitioned regions. Each of the partitioned regions includes a plurality of geographic nodes. A starting node is selected. At least one partitioned region is selected based on a predetermined travel-time horizon of the vehicle from the starting node. Route simulation is performed between the plurality of geographic nodes of the selected at least one partitioned region and a plurality of potential future routes is generated. An actual route of the vehicle is detected. The actual route of the vehicle is compared with the plurality of potential future routes. A probability of the vehicle traveling along each potential future route is determined. A future route of the vehicle is predicted.

Predicting a vehicular route of travel without historical route data

Systems and methods for obtaining vehicle operational data and driving context data from one or more monitoring systems, including converting the obtained vehicle operational data and driving context data into sequential vehicle operational feature data and sequential driving context feature data, calibrating the sequential vehicle operational feature data and the sequential driving context feature data temporally to form calibrated sequential vehicle operational feature data and calibrated sequential driving context feature data, constructing a sequence table of temporal sample points based on the calibrated sequential vehicle operational feature data and the calibrated sequential driving context feature data, feeding the sequence table into a deep neural network model for applying network learning to form a trained deep neural network model, extracting driving behavior features from the trained deep neural network model and analyzing the extracted driving behavior features to determine driving behavior characteristics of the driver.

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Beijing, China

Ting Yuan