The present technology pertains to predicting trajectories of objects near an autonomous vehicle. The predictions may be obtained as output from a trajectory prediction machine learning model. The inputs to the trajectory prediction machine learning model may be based on a first map of an area surrounding an autonomous vehicle, and a second map of an area around an object within the first area. The second map may have a smaller area and a higher resolution relative to the first map.

Systems and methods are provided for vehicle navigation. In one implementation, a host vehicle-based sparse map feature harvester system may include at least one processor programmed to receive a plurality of images captured by a camera onboard the host vehicle as the host vehicle travels along a road segment in a first direction, wherein the plurality of images are representative of an environment of the host vehicle; detect one or more semantic features represented in one or more of the plurality of images, the one or more semantic features each being associated with a predetermined object type classification; identify at least one position descriptor associated with each of the detected one or more semantic features; identify three-dimensional feature points associated with one or more detected objects represented in at least one of the plurality of images; receive position information, for each of the plurality of images, wherein the position information is indicative of a position of the camera when each of the plurality of images was captured; and cause transmission of drive information for the road segment to an entity remotely-located relative to the host vehicle, wherein the drive information includes the identified at least one position descriptor associated with each of the detected one or more semantic features, the identified three-dimensional feature points, and the position information.

Methods and apparatuses associated with updating a map using images are described. An apparatus can include a processing resource and a memory resource having instructions executable to a processing resource to monitor a map including a plurality of locations, receive, at the processing resource, the memory resource, or both, and from a first source, image data associated with a first location, identify the image data as being associated with a missing portion, an outdated portion, or both, of the map, and update the missing portion, the outdated portion, or both, of the map with the image data.

A system and method for recalling and utilizing location data are presented. A connection between a device and a vehicle is detected. After the connection is detected, a first set of data stored on the device is identified. The first set of data includes a last known location of the vehicle. A current value of a vehicle sensor is compared to a historical value of the vehicle sensor. The historical value was captured from the vehicle sensor at about a time the first set of data was stored on the device. When the current value of the vehicle sensor is the same as the historical value of the vehicle sensor, the first set of data is used to define a current location of the vehicle.

A system and a method for updating and sharing crossroad dynamic map data are disclosed. The method includes steps of receiving a detection information outputted from an on-vehicle detecting device, wherein the detection information includes a host-vehicle absolute coordinate, a host-vehicle course, a host-vehicle speed, a relative speed between an object and the host vehicle, and an initial relative coordinate between the object and the host vehicle; respectively performing matching procedures to the host-vehicle absolute coordinate and the initial relative coordinate by respectively adding estimated coordinate shifts to obtain a matched host-vehicle absolute coordinate and a matched relative coordinate; performing a coordinate rotation transformation to the matched relative coordinate to obtain a matched transformed coordinate; merging the matched host-vehicle absolute coordinate and the matched transformed coordinate into crossroad-section map data to form crossroad dynamic map data; and sharing the crossroad dynamic map data.

A lane estimation apparatus including a microprocessor. The microprocessor is configured to perform determining whether a precision of positioning is equal to or larger than a predetermined value based on precision information, identifying a traveling lane based on position information and road map information when it is determined that the precision of positioning is equal to or larger than the predetermined value, then when it is determined that the precision of positioning is smaller than the predetermined value, determining whether a lane change has been made from the traveling lane identified when it has been determined that the precision of positioning is equal to or larger than the predetermined value, based on driving information and information on a road surface profile included in the road map information, and identifying the traveling lane in accordance with a determination result.

The present disclosure provides a map data processing method, an electronic device and a storage medium, relates to a technical field of data processing, and in particular to the field of map data processing. A specific implementation solution is as follows: receiving first data encapsulated in a form of offline data, the first data being used to characterize low-frequency data in the map data; obtaining second data after initiating a first online request, the second data being used to characterize high-frequency data in the map data; and performing merging processing on the first data and the second data to obtain target data to be displayed in a map. By adopting the present disclosure, the timeliness of map data display may be improved.

Systems and methods for an autonomous vehicle are provided. In one aspect, an autonomous vehicle includes a perception sensor and a processor configured to: receive detected roadway conditions data including roadway grade data from the perception sensor, retrieve mapped data having grade data, and determine that the roadway has a grade based on the detected roadway grade data and the retrieved roadway grade data. The processor can be further configured to, in response to determining that the roadway has a grade, determine that the grade of the roadway is greater than or equal to a predetermined high grade value and less than a predetermined grade limit, and in response to determining that the grade of the roadway is greater than or equal to the predetermined high grade value and less than the predetermined grade limit, operate the autonomous vehicle to change lane to a right-most lane.

A sensor calibration system periodically receives scene data from a detector in a calibration scene. The calibration scene includes calibration targets. The sensor calibration system generates a calibration map based on the scene data. The calibration map is a virtual representation of the calibration scene and includes features of the calibration targets that can be used as ground truth features for calibrating AV sensors. The sensor calibration system can periodically update the calibration map. For instance, the sensor calibration system receives the scene data at a predetermined frequency and updates the calibration map every time it receives new scene data. The predetermined frequency may be a frequency of the detector completing a full scan of the calibration scene. The sensor calibration system provides a latest version of the calibration map for being used by an AV to calibrate a sensor on the AV 110.

One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.