An object detection method includes: obtaining a first offset value and a second offset value, setting a first detection threshold value by adding the first offset value to a first reference threshold value, setting a second detection threshold value by adding the second offset value to a second reference threshold value, obtaining a detection input, and performing target detection upon the detection input according to at least the first detection threshold value and the second detection threshold value. The first offset value is different from the second offset value. The first reference threshold value is determined for detecting if at least one object with a first value of an object characteristic exists. The second reference threshold value is determined for detecting if at least one object with a second value of the object characteristic exists. The second value is different from the first value.

Methods, systems, and apparatus, including medium-encoded computer program products, for 3D flight tracking of objects include a method including determining a golf ball trajectory based on observations by sensor(s), extrapolating the trajectory backward in time, calculating distance measure(s) between the extrapolated trajectory and physical locations, estimating a systemic error for observation(s), wherein the systemic error affects observed ball positions, estimating a stochastic error associated with the observation(s), wherein the stochastic error affects an angle of a trajectory determined from observed ball positions, combining the estimated systemic and stochastic errors to form error measure(s) for the distance measure(s), identifying one of the physical locations as an origin for the golf ball when the error measure(s) satisfy a criterion, and waiting for additional observations of the golf ball by the sensor(s) when the error measure(s) do not satisfy the criterion.

Methods, systems, and apparatus, including medium-encoded computer program products, for 3D flight tracking of objects include a method including determining a golf ball trajectory based on observations by sensor(s), extrapolating the trajectory backward in time, calculating distance measure(s) between the extrapolated trajectory and physical locations, estimating a systemic error for observation(s), wherein the systemic error affects observed ball positions, estimating a stochastic error associated with the observation(s), wherein the stochastic error affects an angle of a trajectory determined from observed ball positions, combining the estimated systemic and stochastic errors to form error measure(s) for the distance measure(s), identifying one of the physical locations as an origin for the golf ball when the error measure(s) satisfy a criterion, and waiting for additional observations of the golf ball by the sensor(s) when the error measure(s) do not satisfy the criterion.

Techniques and apparatuses are described that implement a smart-device-based radar system capable of performing angular estimation using machine learning. In particular, a radar system 102 includes an angle-estimation module 504 that employs machine learning to estimate an angular position of one or more objects (e.g., users). By analyzing an irregular shape of the radar system 102's spatial response across a wide field of view, the angle-estimation module 504 can resolve angular ambiguities that may be present based on the angle to the object or based on a design of the radar system 102 to correctly identify the angular position of the object. Using machine-learning techniques, the radar system 102 can achieve a high probability of detection and a low false-alarm rate for a variety of different antenna element spacings and frequencies.

A method for tracking an object can include receiving first data input including first feature values of features that indicate a first position. The method can further include generating a first grid of cells representing an object track with the received feature values within an extent of the first grid of cells. The method can further include receiving second data input including second feature values of the features that indicate a second position. The method can further include, in response to determining the second feature values are within the extent of the first grid of cells adding a point corresponding to the second feature values to the first grid of cells to associate the point to an object track.

Disclosed is a control assistance device that includes a radar system that detects surrounding objects, and generates radar tracking information tracking a position of the surrounding object relative to a vehicle, and a camera system, that includes cameras, and generates image information, and camera tracking information tracking the position of the surrounding object. The control assistance device also includes processors that determine whether the radar tracking information is generated in real time when the surrounding object approaches within a preset distance from the vehicle based on generated tracking information that includes the radar tracking information or the camera tracking information, determine whether the camera tracking information is generated in real time, when the radar tracking information is determined as generated in real time, and control a braking system of the vehicle based on the generated tracking information, when the camera tracking information is determined as generated in real time.

Disclosed is a control assistance device that includes a radar system that detects surrounding objects, and generates radar tracking information tracking a position of the surrounding object relative to a vehicle, and a camera system, that includes cameras, and generates image information, and camera tracking information tracking the position of the surrounding object. The control assistance device also includes processors that determine whether the radar tracking information is generated in real time when the surrounding object approaches within a preset distance from the vehicle based on generated tracking information that includes the radar tracking information or the camera tracking information, determine whether the camera tracking information is generated in real time, when the radar tracking information is determined as generated in real time, and control a braking system of the vehicle based on the generated tracking information, when the camera tracking information is determined as generated in real time.

In an embodiment, a method includes: receiving reflected radar signals with a millimeter-wave radar; generating a displacement signal indicative of a displacement of a target based on the reflected radar signals; filtering the displacement signal using a bandpass filter to generate a filtered displacement signal; determining a first rate indicative of a heartbeat rate of the target based on the filtered displacement signal; tracking a second rate indicative of the heartbeat rate of the target with a track using a Kalman filter; updating the track based on the first rate; and updating a setting of the bandpass filter based on the updated track.

System, methods, and other embodiments described herein relate to adapting vehicle access by detecting a person carrying an object. In one embodiment, a method includes detecting a person near a vehicle for gaining access. The method also includes scanning the person for an object using a radar of the vehicle, wherein information from the radar indicates densities of the person and the object. Upon detecting the object using the densities, the method also includes adapting the access to a compartment of the vehicle.

Methods, systems, and products for resolving level ambiguity for radar systems of autonomous vehicles may include detecting a plurality of objects with a radar system. Each first detected object may be associated with an existing tracked object based on a first position thereof. First tracked object data based on a first height determined for each first detected object may be stored. The first height may be based on the position of the detected object, the existing tracked object, and a tile map. Second tracked object data based on a second height determined for each second detected object not associated with the existing tracked object(s) may be stored. The second height may be based on a position of each second detected object, a vector map, and the tile map. A command to cause the autonomous vehicle to perform at least one autonomous driving operation may be issued.