Systems, methods, tangible non-transitory computer-readable media, and devices associated with the motion prediction and operation of a device including a vehicle are provided. For example, a vehicle computing system can access state data including information associated with locations and characteristics of objects over a plurality of time intervals. Trajectories of the objects at subsequent time intervals following the plurality of time intervals can be determined based on the state data and a machine-learned tracking and kinematics model. The trajectories of the objects can include predicted locations of the objects at subsequent time intervals that follow the plurality of time intervals. Further, the predicted locations of the objects can be based on physical constraints of the objects. Furthermore, indications, which can include visual indications, can be generated based on the predicted locations of the objects at the subsequent time intervals.

In a case where a person moves away in a longitudinal direction from the vicinity of a vehicle, a condition for generating a warning is satisfied due to a change in a speed in a transverse direction. Thus, a warning system generates an erroneous warning. In order to solve this problem, there are proposed a target detection system for a vehicle and a target detection method for a vehicle, both of which are capable of computing a final risk level, taking into consideration not only results of recomputing a time-to-collision and an impact point, but also the presence or absence of a target that is detected by a camera sensor. The time-to-collision and the impact point are recomputed, taking into consideration a change in a speed in a transverse direction that occurs when the target moves in the longitudinal direction.

The techniques and systems herein enable target-velocity estimation using position variance. Specifically, a plurality of detections of a target are received for respective times as the target moves relative to a host vehicle. Based on the detections, two-dimensional positions of the target relative to the host vehicle are determined for the respective times. Based on the positions of the target at the respective times, a first variance is determined for a first dimension of the positions, and a second variance is determined for a second dimension of the positions. Based on the first and second variances, an estimated velocity of the target is calculated. By basing the estimated velocity on the variances of the positions, more-accurate estimated velocities may be generated sooner, thus enabling better performance of downstream operations.

Methods, devices, and systems for vehicle tracking are described herein. In some examples, one or more embodiments include a computing device comprising a memory and a processor to execute instructions stored in the memory to receive tracking data for an vehicle at an airport, where the tracking data includes a data record including a number of data fields, determine whether the vehicle is being actively tracked using the tracking data, and generate a mapped data record using the tracking data to track the vehicle at the airport in response to the vehicle being actively tracked.

Disclosed are techniques for determining a motion state of a target object. In an aspect, an on-board computer of an ego vehicle detects the target object in one or more images, determines one or more first attributes of the target object based on measurements of the one or more images, determines one or more second attributes of the target object based on measurements of a map of a roadway on which the target object is travelling, and determines the motion state of the target object based on the one or more first attributes and the one or more second attributes of the target object.

A ground-based radar system for weather sensing and aircraft tracking includes a ground-based radar that is configured to scan a volume of space associated with a particular aircraft for detecting a weather event in the volume of space, and an electronic control system that is configured to control the ground-based radar. The control system is adapted to track the particular aircraft via tracking data associated with the particular aircraft, and is adapted to detect the weather event via weather data associated with signals from the ground-based radar. The control system is configured to control the ground-based radar to adjust the scan of the volume of space in response to at least the tracking data associated with the particular aircraft being tracked. A geographically diverse radar network that includes multiple ground-based radar systems that communicate with each other also is provided.

Object detection systems and methods are provided. An object detection system comprises a plurality of nodes, each node having a transmitter configured to transmit a radar signal as a beam, and one or more receivers configured to receive a reflected radar signal. The nodes and transmitters are arranged such that the radar beam of one transmitter at least partly overlaps with the radar beam from the transmitter at an adjacent one of the nodes. The object detection system comprises a processor configured to receive a digitised signal from each node, process the digitised signal to detect characteristics of any Doppler effects created by the movement of an object through one or more of the radar beams, compare the Doppler characteristics with Doppler signatures associated with known objects, and thereby classify the object.

A method is provided for estimating a velocity of an object located in the environment of a vehicle. Detections of a range, an azimuth angle and a range rate of the object are acquired for at least two different points in time via a sensor. A cost function is generated which depends on a first source and a second source. The first source is based on a range rate velocity profile which depends on the range rate and the azimuth angle, and the first source depends on an estimated accuracy for the first source. The second source is based on a position difference which depends on the range and the azimuth angle for the at least two different points in time, and the second source depends on an estimated accuracy for the second source. By minimizing the cost function, a velocity estimate is determined for the object.

A driving support apparatus according to the invention estimates the position of a moving body by controlling a position estimation unit when the tracking-target moving body leaves a first area or a second area to enter a blind spot area and detects the position of the moving body by controlling a position detection unit when the moving body leaves the blind spot area to enter the first area or the second area. In this manner, the trajectory of the tracking-target moving body is calculated so that the trajectory of the moving body detected in the first area or the second area and the trajectory of the moving body estimated in the blind spot area are continuous to each other and driving support is executed based on the calculated trajectory of the tracking-target moving body.

A track fusion method for an unmanned surface vehicle includes: (a) obtaining perception information of the unmanned surface vehicle, where the perception information includes GPS data information and radar data information; (b) pre-processing the radar data information to obtain target radar information; (c) constructing a track correlation model; and performing track correlation between the GPS data information and the target radar information based on the track correlation model; and (d) constructing a fusion data weight allocation model; and subjecting between the GPS data information and the target radar information correlated therewith to track fusion based on the fusion data weight allocation model. This application further provides a track fusion device for unmanned surface vehicles.