Abstract: A durability evaluation system includes a collector collecting first data for each of meshes forming a vehicle body of a target vehicle, and second data for each mesh forming a vehicle body of a virtual vehicle in which a material or a thickness of the target vehicle is adjusted and a controller determining input data for first meshes based on the lifetime data matched to each row for the meshes of data obtained by preprocessing the first data and the second data, extracting feature data including features representing the stress analysis data for each of the first meshes and each of second meshes adjacent to the first meshes and features representing a shape of each of the first meshes, generating first synthesized data based on the feature data, and learning a method of predicting the lifetime data representing durability performance based on the feature data and the first synthesized data.

Abstract: An object extraction method using an object extraction system that includes a pre-processing module, an edge module, and an optimization module, and the object extraction method includes: generating an edge depth map with respect to a 2D image by pre-processing a 2D image where a target object to be data-labeled may be included, by the pre-processing module; extracting a target edge with respect to the target object from 3D data where the target object may be included, by the edge module; and optimizing the generated target edge to a final edge to match the generated edge depth map, by the optimization module.

Abstract: A method for sensing a closing time of an injector using an artificial neural network may include: sensing, by a controller, a voltage generated by an injector; performing, by the controller, a preprocess to derive an input matrix using variation characteristics of the voltage; and performing, by the controller, a closing time prediction to derive a closing time of the injector by an artificial neural network model including an input layer including the input matrix, a hidden layer, and an output layer.

Abstract: A method for sensing a closing time of an injector using an artificial neural network may include: sensing, by a controller, a voltage generated by an injector; performing, by the controller, a preprocess to derive an input matrix using variation characteristics of the voltage; and performing, by the controller, a closing time prediction to derive a closing time of the injector by an artificial neural network model including an input layer including the input matrix, a hidden layer, and an output layer.

Abstract: An adverse situation warning device includes a route data storing portion for storing a first route data of a host vehicle including a first geohash corresponding to a moving route of the host vehicle, a route comparing portion for receiving a second route data of a remote vehicle including a second geohash corresponding to a moving route of the remote vehicle, and for comparing the geohash of the first route data and the geohash of the second route data, and a warning output portion for outputting an adverse situation warning based on the comparison result.

Abstract: An adverse situation warning device includes a route data storing portion for storing a first route data of a host vehicle including a first geohash corresponding to a moving route of the host vehicle, a route comparing portion for receiving a second route data of a remote vehicle including a second geohash corresponding to a moving route of the remote vehicle, and for comparing the geohash of the first route data and the geohash of the second route data, and a warning output portion for outputting an adverse situation warning based on the comparison result.