A computer includes a processor and a memory storing instructions executable by the processor to receive a series of sample coordinate points of a projected path of travel of a vehicle, generate interpolated coordinate points along the projected path between the sample coordinate points, fit a curve to the sample coordinate points and interpolated coordinate points, and output a curvature of a lane at a reported coordinate point along the projected path based on the curve.

Described herein is an example method for aligning an aircraft with a centerline of a runway during takeoff. The method includes accessing a first image captured by a first camera mounted on a first side of the aircraft; accessing a second image captured by a second camera mounted on a second side of the aircraft that is opposite the first side; determining a first angle between a first marked line on the runway in the first image and a first reference line in the first image; determining a second angle between a second marked line on the runway in the second image and a second reference line in the second image; and based on the first angle and the second angle, moving a control surface of the aircraft such that the aircraft moves closer to the centerline of the runway.

A processor implemented method includes: calculating edge direction information of edge component pixels extracted from an input image; determining an inlier pixel among the edge component pixels based on the edge direction information and virtual horizontal line information; estimating a rotation reference point based on the inlier pixel; and correcting localization information of the apparatus based on the estimated rotation reference point.

The present invention relates to a system for steering or guiding a sod harvesting machine (sod harvester) with a high degree of precision, without the need for the sod harvesting machine to have a guide stick or shoe in physical or mechanical contact with the sod to be harvested or to be in connection with remote navigation systems, such as GPS. The system includes a sensor mounted at the front of the harvester and a processor for processing information from the sensor to determine a boundary line and for steering the sod harvester along the boundary line.

Techniques for determining a location of a vehicle in an environment using sensors and determining calibration information associated with the sensors are discussed herein. A vehicle can use map data to traverse an environment. The map data can include semantic map objects such as traffic lights, lane markings, etc. The vehicle can use a sensor, such as an image sensor, to capture sensor data. Semantic map objects can be projected into the sensor data and matched with object(s) in the sensor data. Such semantic objects can be represented as a center point and covariance data. A distance or likelihood associated with the projected semantic map object and the sensed object can be optimized to determine a location of the vehicle. Sensed objects can be determined to be the same based on matching with the semantic map object. Epipolar geometry can be used to determine if sensors are capturing consistent data.

A processing device includes a first processor configured to detect a bounding box of a distant vehicle, in an input image generated by imaging the distant vehicle, and extract at least one feature of the distant vehicle. A second processor is configured to estimate a geometry of a road on which the distant vehicle is located, based on a position of at least one feature relative to at least a portion of the bounding box.

Lighting-invariant imaging produces consistent output for all weather conditions and all lighting conditions within a scene. Reflectivity values of objects are produced in real-time, non-real-time, or cloud processing based on radiance values of pixels and objects. Image vectors describing segmented objects are produced, and spatial attributes are added to produce scene vectors for objects. The lighting-invariant system performs object recognition for one or more images of a scene and can be used for both object identification and object motion determination based on vector representations of objects in the scene.

Systems and methods for segmenting scan data are disclosed. The methods include receiving scan data representing a plurality of points in an environment associated with a ground surface and one or more objects, and creating a graph from the scan data. The graph includes a plurality of vertices corresponding to the plurality of points. The method further includes assigning a unary potential to each of the plurality of vertices that is a cost of assigning that vertex to a ground label or a non-ground label, and assigning a pairwise potential to each pair of neighboring vertices in the graph that is the cost of assigning different labels to neighboring vertices. The methods include using the unary potentials and the pairwise potentials to identify labels for each of the plurality of points, and segmenting the scan data to identify points associated with the ground based on the identified labels.

A vehicular driving assist system includes a sensor disposed at a vehicle and sensing forward of the equipped vehicle. With the equipped vehicle traveling along a traffic lane of a road, the system, responsive to processing of sensor data captured by the sensor, determines a leading vehicle traveling along the traffic lane ahead of the equipped vehicle. The system determines position and heading information of the leading vehicle relative to the equipped vehicle. As the equipped vehicle approaches locations that correspond with the determined position and heading information of the leading vehicle, the system adapts the position and heading information of the leading vehicle to the current location and heading of the equipped vehicle and controls lateral movement of the equipped vehicle to follow the leading vehicle in and long the traffic lane of the road.

The invention relates to a method for determining a parameter indicative of a road capability of a road segment (16) supporting a vehicle (10). The vehicle (10) comprises a sensor (18) adapted to generate an information package on the basis of signals (20, 22) reflected from the surface of a portion of the road segment (16) in front of the vehicle (10), as seen in an intended direction of travel of the vehicle (10). The vehicle (10) further comprises a plurality of ground engaging members (12, 14). The method comprises: —determining a reference content measure indicative of the information content of an information package supplied by the sensor (18) at a reference time instant (T1); —comparing the reference content measure to a previous content measure indicative of the information content of an information package supplied by the sensor (18) at a previous time instant (T0), the previous time instant (T0) occurring before the reference time instant (T1); —upon detecting that a difference between the reference content measure and the previous content measure is outside a predetermined tolerance range, allowing the execution of a determination procedure for determining the parameter indicative of the road capability of the road segment (16) supporting the vehicle (10); —upon detecting that the difference between the reference content measure and the previous content measure is within the predetermined tolerance range, not allowing the execution of the determination procedure, and —wherein the determination procedure comprises modifying the operation of one or more of the ground engaging members (12, 14).