Patent classifications
G01S13/723
FLEXIBLE MULTI-CHANNEL FUSION PERCEPTION
A method may include obtaining first sensor data from a first sensor system and second sensor data from a second sensor system. The first and the second sensor systems may capture sensor data from a total measurable world. The method may include identifying a first object included in the first sensor data and a second object included in the second sensor data and determining first parameters corresponding to the first object and second parameters corresponding to the second object. The first parameters may be compared with the second parameters and whether the first object and the second object are a same object may be determined based on the comparing the first parameters and the second parameters. Responsive to determining that the first object and the second object are the same object, a set of objects representative of objects in the total measurable world including the same object may be generated.
APPARATUS AND METHOD FOR DETECTING TARGET USING RADAR
In an apparatus for detecting a target using a radar according to one aspect of the present invention, a first radar and a second radar, which are multi-channel radars each including a plurality of transmitting antennas and a plurality of receiving antennas, are installed to be spaced apart from each other, and position information of a target and velocity vector information of the target are calculated from first position information and first velocity information of the target acquired from the first radar and second position information and second velocity information of the target acquired from the second radar and then are used to detect and track the target.
Target tracking during acceleration events
Vehicles and methods for tracking an object and controlling a vehicle based on the tracked object. A Radar-Doppler (RD) map is received from the radar sensing system of the vehicle and relative acceleration of an object with respect to the vehicle is detected based on the RD map so as to provide acceleration data. A current frame of detected object data is received from a sensing system of the vehicle. When the relative acceleration has been detected, a tracking algorithm is adapted to reduce the influence of the predictive motion model or the historical state of the object and the object is tracked using the adapted tracking algorithm so as to provide adapted estimated object data based on the object tracking. One or more vehicle actuators are controlled based on the adapted estimated object data.
Systems and methods for radar false track mitigation with camera
Systems and methods for operating radar systems. The methods comprise, by a processor: receiving point cloud information generated by at least one radar device; generating a radar track initializer using the point cloud information; determining whether the radar track initializer includes false information; generating a radar track for a detected object when a determination is made that the radar track initializer does not include false information; and/or using the radar track to control operations of a vehicle.
ASSOCIATING RADAR DATA WITH TRACKED OBJECTS
Sensors, including radar sensors, may be used to detect objects in an environment. In an example, a vehicle may include one or more radar sensors that sense objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. A plurality of radar points from one or more radar scans are associated with a sensed object and a representation of the sensed object is determined from the plurality of radar points. The representation may be compared to track information of previously-identified, tracked objects. Based on the comparison, the sensed object may be associated with one of the tracked objects, and, alternatively, the track information may be updated based on the representation. Conversely, the comparison may indicate that the sensed object is not associated with any of the tracked objects. In this instance, the representation may be used to generate a new track, e.g., for the newly-sensed object.
TRACKING OBJECTS WITH RADAR DATA
Sensors, including radar sensors, may be used to detect objects in an environment. In an example, a vehicle may include one or more radar sensors that sense objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. A plurality of radar points from one or more radar scans are associated with a sensed object and a representation of the sensed object is determined from the plurality of radar points. The representation may be compared to track information of previously-identified, tracked objects. Based on the comparison, the sensed object may be associated with one of the tracked objects, and, alternatively, the track information may be updated based on the representation. Conversely, the comparison may indicate that the sensed object is not associated with any of the tracked objects. In this instance, the representation may be used to generate a new track, e.g., for the newly-sensed object.
Dynamic object detection indicator system for an automated vehicle
A system includes a tracking system, a controller-circuit, and a device. The tracking system is configured to detect and track an object, and includes one or more of a computer vision system, a radar system, and a LIDAR system. The controller-circuit is disposed in a host vehicle, and is configured to receive detection signals from the tracking system, process the detection signals, determine, whether an object is detected based on the processed detecting signals, and in accordance with a determination that an object is detected, output command signals. The device is adapted to be mounted to the host vehicle, and is configured to receive the command signals and thereby provide a dynamic visual indication adapted to change in accordance with orientation changes between the host vehicle and the object. The dynamic visual indication is viewable from outside of the host vehicle.
Systems and methods for high velocity resolution high update rate radar for autonomous vehicles
An autonomous vehicle (AV) includes a radar sensor system and a computing system that computes velocities of an object in a driving environment of the AV based upon radar data that is representative of radar returns received by the radar sensor system. The AV can be configured to compute a first velocity of the object based upon first radar data that is representative of the radar return from a first time to a second time. The AV can further be configured to compute a second velocity of the object based upon second radar data that includes at least a portion of the first radar data and further includes additional radar data representative of a radar return received subsequent to the second time. The AV can further be configured to control one of a propulsion system, a steering system, or a braking system to effectuate motion of the AV based upon the computed velocities.
SEMANTIC UNDERSTANDING OF DYNAMIC IMAGERY USING BRAIN EMULATION NEURAL NETWORKS
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving sensor data generated by one or more sensors that characterizes motion of an object over multiple time steps, providing the sensor data characterizing the motion of the object to a motion prediction neural network having a brain emulation sub-network with an architecture that is specified by synaptic connectivity between neurons in a brain of a biological organism, and processing the sensor data characterizing the motion of the object using the motion prediction neural network having the brain emulation sub-network to generate a network output that defines a prediction characterizing the motion of the object.
Method for determining the position of a vehicle
A computer implemented method for determining the position of a vehicle, wherein the method comprises: determining at least one scan comprising a plurality of detection points, wherein each detection point is evaluated from a signal received at the at least one sensor and representing a location in the vehicle environment; determining, from a database, a predefined map, wherein the map comprises a plurality of elements in a map environment, each of the elements representing a respective one of a plurality of static landmarks in the vehicle environment, and the map environment representing the vehicle environment; matching the plurality of detection points and the plurality of elements of the map; determining the position of the vehicle based on the matching; wherein the predefined map further comprises a spatial assignment of a plurality of parts of the map environment to the plurality of elements, and wherein the spatial assignment is used for the matching.