A vehicle position correction apparatus and a method thereof may include a learner that deep learns a model which predicts a position of a probe vehicle based on driving information of the probe vehicle traveling on a road, a communication device that receives driving information of a target vehicle from the target vehicle, and a controller that obtains a predicted position of the target vehicle based on the model on which the deep learning is completed and corrects an actually measured position of the target vehicle to the predicted position of the target vehicle.

Disclosed is a method for controlling the powertrain of a motor vehicle between the current location of the vehicle and an arrival point, including calculating a theoretical optimal traction force, determining a friction force applied to the vehicle, calculating an actual optimal force to be applied to the wheels as far as the arrival point, and applying a traction force to the wheels of the vehicle when the calculated actual optimal force is strictly greater than a predetermined threshold value or else not applying a force to the wheels of the vehicle when the calculated actual optimal force is greater than or equal to zero and less than or equal to the predetermined threshold value, or else applying a braking force to the wheels of the vehicle when the calculated actual optimal force is strictly less than zero.

A vehicle system includes an electronic control unit. The electronic control unit is configured to execute a first program, a second program, and a third program. The first program is configured to recognize an object present around a vehicle, the second program is configured to store information related to the recognized object as time-series map data, and the third program is configured to predict a future position of the object based on the stored time-series map data. The first program and the third program are configured to be (i) first, individually optimized based on first training data corresponding to output of the first program and second training data corresponding to output of the third program, and (ii) then, collectively optimized based on the second training data corresponding to the output of the third program.

A method for predicting traffic light information by using a LIDAR is provided. The method includes steps of: (a) on condition that each of metadata has been allocated for each of virtual boxes included in a region covered by the LIDAR, obtaining, by a server, at least part of start timing information and stop timing information of a plurality of vehicles for each of the virtual boxes; and (b) predicting, by the server, each of pieces of the traffic light information respectively corresponding to each of the virtual boxes by referring to at least part of the start timing information and the stop timing information of the vehicles for each of the virtual boxes.

A method for predicting traffic light information by using a LIDAR is provided. The method includes steps of: (a) on condition that each of metadata has been allocated for each of virtual boxes included in a region covered by the LIDAR, obtaining, by a server, at least part of start timing information and stop timing information of a plurality of vehicles for each of the virtual boxes; and (b) predicting, by the server, each of pieces of the traffic light information respectively corresponding to each of the virtual boxes by referring to at least part of the start timing information and the stop timing information of the vehicles for each of the virtual boxes.

A vehicle row follow system may include a vehicle comprising at least one sensor to output signals serving as a basis for a three-dimensional (3D) point cloud and to output signals corresponding to a two-dimensional (2D) image of a region forward the vehicle. The system may further include a non-transitory computer readable medium containing instructions to direct a processor to: determine plan row lines in a 2D image; determine a yaw of the vehicle based upon a slope of a heading line relative to a centerline between the plant row lines in the 2D image; determine a lateral offset of the vehicle from the centerline between the consecutive plant rows based upon an identity of the consecutive plant rows in the 3D point cloud; and output steering control signals based upon the determined yaw and lateral offset of the vehicle.

A vehicle row follow system may include a vehicle comprising at least one sensor to output signals serving as a basis for a three-dimensional (3D) point cloud and to output signals corresponding to a two-dimensional (2D) image of a region forward the vehicle. The system may further include a non-transitory computer readable medium containing instructions to direct a processor to: determine plan row lines in a 2D image; determine a yaw of the vehicle based upon a slope of a heading line relative to a centerline between the plant row lines in the 2D image; determine a lateral offset of the vehicle from the centerline between the consecutive plant rows based upon an identity of the consecutive plant rows in the 3D point cloud; and output steering control signals based upon the determined yaw and lateral offset of the vehicle.

A control system for a human-powered vehicle includes a suspension device, at least one detector, an electronic controller. The suspension device includes a first suspension and a second suspension. The at least one detector is configured to detect information related to at least two conditions of the human-powered vehicle. The conditions include an operating state of the suspension device, a power input to the human-powered vehicle, a torque of a power transmission component, and a rotating state of the power transmission component. The electronic controller is configured to selectively control the suspension device between a first operating state and a second operating state in accordance with a detection result obtained by the at least one detector.

A vehicle restart control apparatus includes a processor configured to generate an override torque according to the environment information of the host vehicle when the vehicle is restarted after stopping; and a storage configured to store data and algorithms driven by the processor.

A vehicle restart control apparatus includes a processor configured to generate an override torque according to the environment information of the host vehicle when the vehicle is restarted after stopping; and a storage configured to store data and algorithms driven by the processor.