A method for tracking a lane on a road is presented. The method comprises receiving, by one or more processors from an imaging system, a set of pixels associated with lane markings. The method further includes generating, by the one or more processors, a predicted spline comprising (i) a first spline and (ii) a predicted extension of the first spline in a direction in which the imaging system is moving. The first spline describes a boundary of a lane and is generated based on the set of pixels. The predicted extension of the first spline is generated based at least in part on a curvature of at least a portion of the first spline.

A method and apparatus for calibrating a camera are provided. A specific embodiment of the method includes: acquiring an image-point cloud sequence, the image-point cloud sequence including at least one group of an initial image and point cloud data collected at a same time, the initial image being collected by a camera provided on an autonomous vehicle and the point cloud data being collected by a radar provided on the autonomous vehicle; determining target point cloud data of initial images corresponding to groups of image-point cloud in the image-point cloud sequence; and matching the target point cloud data with the corresponding initial images in the image-point cloud sequence to determine a correction parameter of the camera.

The present disclosure relates generally to identification of drivable surfaces in connection with autonomously performing various tasks at industrial work sites and, more particularly, to techniques for distinguishing drivable surfaces from non-drivable surfaces based on sensor data. A framework for the identification of drivable surfaces is provided for an autonomous machine to facilitate it to autonomously detect the presence of a drivable surface and to estimate, based on sensor data, attributes of the drivable surface such as road condition, road curvature, degree of inclination or declination, and the like. In certain embodiments, at least one camera image is processed to extract a set features from which surfaces and objects in a physical environment are identified, and to generate additional images for further processing. The additional images are combined with a 3D representation, derived from LIDAR or radar data, to generate an output representation indicating a drivable surface.

Methods and devices for determining axis of symmetry of self-driving vehicle (SDV) and for calibrating a Light Detection and Ranging (LIDAR) system are disclosed. One of the axes of the system of coordinates of the LIDAR system extends along a normal direction of a ground surface. the method includes acquiring a subset of detected points in the system of coordinates; generating a subset of mirror-image points based on the subset of detected points; projecting the subset of mirror-image points onto the subset of detected points so as to define pairs of overlapping data points; using symmetrically opposite detected points for determining the axis of symmetry of the SDV in the system of coordinates of the LIDAR system; and calibrating the LIDAR system using an angular offset between the axis of symmetry of the SDV and an other one of the axes of the system of coordinates of the LIDAR system.

A first blind spot information acquisition unit inputs road geometry information and traveling environment information which are acquired by an information acquisition unit to a first blind spot inference model, and thereby acquires, using the first blind spot inference model, first blind spot information indicating a blind spot area of a road along which a vehicle is traveling, the first blind spot inference model being configured to output first blind spot information indicating a blind spot area of a road when receiving road geometry information about geometry of the road and traveling environment information about a traveling environment of the road. A parking position determination unit determines a parking position of the vehicle by using the first blind spot information acquired by the first blind spot information acquisition unit.

A vehicle includes a transceiver configured to communicate with a server; and a controller programmed to responsive to detecting a first trip pattern of the vehicle, create a first geofence associated with the first trip pattern, wherein the first geofence includes a geographic character and a temporal character, send the first geofence to the server, and responsive to receiving, from the server, a first message indicative of the first geofence being in common with a second geofence associated with an entity, establish a connection with the entity via the transceiver.

In embodiments, a method positions and aligns an autonomous tractor coupling with an articulated trailer located in a pick-up spot. A staging path that terminates at a staging point corresponding to the pick-up spot is determined, and the tractor is controlled to follow the staging path to the staging point and then couple with the trailer. In embodiments, a method positions and aligns an autonomous tractor coupled to an articulated trailer in preparation for the tractor to reverse the trailer into a drop-off spot. A staging path having a shape and a staging point is determined, and the autonomous tractor is controlled to follow the staging path to the staging point. The staging path is shaped such that, after following the staging path to the staging point, the tractor and trailer are positioned for reversing into the drop-off spot.

In an electricity supply system including a plurality of pieces of electricity supply equipment and a plurality of vehicles, when a second vehicle being supplied with electricity from first electricity supply equipment receives a request to surrender the first electricity supply equipment from a first vehicle, the second vehicle surrenders the first electricity supply equipment to the first vehicle and moves, through automated driving, to second electricity supply equipment selected from among the plurality of pieces of electricity supply equipment when the second vehicle is able to travel as far as the second electricity supply equipment.

A method for operating a motor vehicle having a camera device that records the area ahead of the vehicle and an associated control device for evaluating images taken by the camera device, wherein the control device evaluates the images in order to detect at least one hand gesture performed by a person shown in the images, wherein, when a hand motion describing a calling gesture that indicates a stop request is detected, in the case of a self-driving motor vehicle, the motor vehicle is controlled to stop in the vicinity of the person, or in the case of a non-self-driving motor vehicle, the driver of the motor vehicle is given a signal indicating the stop request.

A vehicle control interface connects a vehicle platform including a first computer that performs travel control of a vehicle and an autonomous driving platform including a second computer that performs autonomous driving control of the vehicle. The vehicle control interface includes a control unit configured to execute: acquiring, from the second computer, a first control command including a plurality of commands for the vehicle platform; removing, from the first control command, a command that does not correspond to a predetermined kind of command by filtering the plurality of commands included in the first control command; converting the first control command, after filtering the plurality of commands, into a second control command for the first computer; and transmitting the second control command to the first computer.