A system and method for controlling a merge vehicle travelling along a highway having a merge lane and a main lane. The method includes receiving merge data about the merge vehicle and a surrounding environment of the merge vehicle. The method includes detecting an intent to perform a merge maneuver by the merge vehicle from the merge lane to the main lane based on the merge data. Further, the method includes generating a graph having a plurality of nodes connected by edges. The plurality of nodes includes a start node set to a current position of the merge vehicle and a goal node located in the main lane after the merge lane has ended. The method includes calculating a three-dimensional (3D) trajectory based on the graph by optimizing edge costs from the start node to the goal node, and controlling the merge vehicle based on the 3D trajectory.

A system and method for controlling a merge vehicle travelling along a highway having a merge lane and a main lane. The method includes receiving merge data about the merge vehicle and a surrounding environment of the merge vehicle. The method includes detecting an intent to perform a merge maneuver by the merge vehicle from the merge lane to the main lane based on the merge data. Further, the method includes generating a graph having a plurality of nodes connected by edges. The plurality of nodes includes a start node set to a current position of the merge vehicle and a goal node located in the main lane after the merge lane has ended. The method includes calculating a three-dimensional (3D) trajectory based on the graph by optimizing edge costs from the start node to the goal node, and controlling the merge vehicle based on the 3D trajectory.

A method for determining running resistance of a vehicle includes receiving, by a controller, a rotation speed of a driving source of the vehicle and an inclination angle of a road on which the vehicle runs; determining a torque of the driving source according to the rotation speed of the driving source and determining the inclination angle of the road according to the torque of the driving source; determining whether the inclination angle exceeds the inclination angle of the road; determining that there is no object towed by the vehicle, and determining the running resistance of the vehicle based on the inclination angle according to the torque of the driving source, when the inclination angle is less than or equal to the inclination angle of the road; and controlling the driving source or a transmission of the vehicle based on the determined running resistance of the vehicle.

A safety system for a vehicle may include one or more processors configured to determine, based on a friction prediction model, one or more predictive friction coefficients between the ground and one or more tires of the ground vehicle using first ground condition data and second ground condition data. The first ground condition data represent conditions of the ground at or near the position of the ground vehicle, and the second ground condition data represent conditions of the ground in front of the ground vehicle with respect to a driving direction of the ground vehicle. The one or more processors are further configured to determine driving conditions of the ground vehicle using the determined one or more predictive friction coefficients.

A safety system for a vehicle may include one or more processors configured to determine, based on a friction prediction model, one or more predictive friction coefficients between the ground and one or more tires of the ground vehicle using first ground condition data and second ground condition data. The first ground condition data represent conditions of the ground at or near the position of the ground vehicle, and the second ground condition data represent conditions of the ground in front of the ground vehicle with respect to a driving direction of the ground vehicle. The one or more processors are further configured to determine driving conditions of the ground vehicle using the determined one or more predictive friction coefficients.

A method includes collecting sensor data from a sensor associated with a vehicle, storing the sensor data in a buffer associated with the sensor, wherein the buffer stores an amount of buffer data, analyzing the sensor data for a proximate event trigger. When the proximate event trigger is not detected, the method includes purging a portion of the sensor data exceeding the amount of buffer data. When the proximate event trigger is detected, the method includes stopping the purging of any of the sensor data and storing the sensor data of the buffer and the sensor data associated with the proximate event trigger and sending the sensor data of the buffer and the sensor data associated with the proximate event trigger to a server.

A method includes collecting sensor data from a sensor associated with a vehicle, storing the sensor data in a buffer associated with the sensor, wherein the buffer stores an amount of buffer data, analyzing the sensor data for a proximate event trigger. When the proximate event trigger is not detected, the method includes purging a portion of the sensor data exceeding the amount of buffer data. When the proximate event trigger is detected, the method includes stopping the purging of any of the sensor data and storing the sensor data of the buffer and the sensor data associated with the proximate event trigger and sending the sensor data of the buffer and the sensor data associated with the proximate event trigger to a server.

A vehicle condition estimation method performed by an information processing device including a processor and a memory connected to the processor to estimate a position or a condition of a vehicle, includes: acquiring an image including a vehicle to be estimated; and estimating the position or the condition of the vehicle to be estimated, using a line segment connecting at least two points selected from a region in which the vehicle to be estimated is captured in the image, with reference to an imaging device that has captured the image.

The present disclosure provides systems and techniques directed at object detection in a vehicle. In aspects, techniques include capturing current radar image data. The current radar image data includes at least one current point cloud. The current point cloud includes at least one current object point being related to an object, and each current object point includes spatial information related to the object. The techniques further include retrieving previous radar image data. The previous radar image data includes at least one previous point cloud. The previous point cloud includes at least one previous object point being related to the object, and each previous object point includes spatial information related to the object. The techniques further include concatenating the information from the current radar image data and the information from the previous radar image data to derive enhanced radar image data using a recurrent neural network.

The present disclosure provides systems and techniques directed at object detection in a vehicle. In aspects, techniques include capturing current radar image data. The current radar image data includes at least one current point cloud. The current point cloud includes at least one current object point being related to an object, and each current object point includes spatial information related to the object. The techniques further include retrieving previous radar image data. The previous radar image data includes at least one previous point cloud. The previous point cloud includes at least one previous object point being related to the object, and each previous object point includes spatial information related to the object. The techniques further include concatenating the information from the current radar image data and the information from the previous radar image data to derive enhanced radar image data using a recurrent neural network.