The invention relates to a method for detecting at least one road user on a traffic route by means of a radar sensor and an optical detector, wherein with said method radar radiation is emitted by at least one radar transmitter of the radar sensor and reflected by the at least one road user, the reflected radar radiation is detected by means of at least one radar receiver of the radar sensor, the detected radar radiation is evaluated in such a way that at least one distance and one radial velocity of the at least one road user relative to the radar sensor is determined, an optical image of the at least one road user is detected by means of the optical detector, and the optical image is evaluation,
wherein at least one parameter of the at least one road user is determined both from the detected radar radiation and the optical image.

An object information acquisition unit acquires object information including an object distance between a radar device and a reflection object and an object azimuth angle at which the reflection object is located. A roadside object extraction unit extracts roadside object information on a roadside object from the object information. An axis deviation angle estimation unit estimates a vertical axis deviation angle from the roadside object information. The vertical axis deviation angle is an angle of deviation of an actual mounting direction from a reference mounting direction in a vertical direction. The actual mounting direction is an actual direction of the radar device, and the reference mounting direction is a direction of the radar device when the radar device is mounted in a reference state.

A millimeter wave radar apparatus determining an obstacle on a railway is applied to the railway and the obstacle. The millimeter wave radar apparatus includes a user interface and a millimeter wave radar. The user interface is configured to control the millimeter wave radar. The millimeter wave radar is configured to transmit a radar wave to a predetermined range on the railway. The millimeter wave radar is configured to receive a reflected radar wave reflected from the predetermined range on the railway based on the radar wave. The user interface is configured to determine whether the obstacle is in the predetermined range on the railway based on the reflected radar wave. If the user interface determines that the obstacle is in the predetermined range on the railway, the user interface is configured to provide a warning.

The described aspects and implementations enable fast and accurate object identification in autonomous vehicle (AV) applications by combining radar data with camera images. In one implementation, disclosed is a method and a system to perform the method that includes obtaining a radar image of a first hypothetical object in an environment of the AV, obtaining a camera image of a second hypothetical object in the environment of the AV, and processing the radar image and the camera image using one or more machine-learning models MLMs to obtain a prediction measure representing a likelihood that the first hypothetical object and the second hypothetical object correspond to a same object in the environment of the AV.

An apparatus includes a processor configured to be disposed with a vehicle and a memory coupled to the processor. The memory stores instructions to cause the processor to receive, at least two of: radar data, camera data, lidar data, or sonar data. The sensor data is associated with a predefined region of a vicinity of the vehicle while the vehicle is traveling during a first time period. At least a portion of the vehicle is positioned within the predefined region during the first time period. The method also includes detecting that no other vehicle is present within the predefined region. An environment of the vehicle during the first time period is classified as one state from a set of states that includes at least one of dry, light rain, heavy rain, light snow, or heavy snow, based on at least two of the sensor data to produce an environment classification. An operational parameter of the vehicle based on the environment classification is modified.

In one embodiment, a method includes determining an operational status of a vehicle including a radar antenna. The operational status is related to autonomous-driving operations of the vehicle in an environment. The method includes determining an expected amount of signaling resources associated with the radar antenna to be utilized by the vehicle while the vehicle performs the autonomous-driving operations, based at least on the operational status of the vehicle and the environment. The method includes determining to switch one or more of the signaling resources associated with the radar antenna from a first mode to a second mode based on the expected amount of signaling resources to be utilized by the radar antenna while the vehicle performs the autonomous-driving operations. The method includes causing the one or more of the signaling resources associated with the radar antenna to switch from the first mode to the second mode.

A video recording and communication device includes a camera, a processor, a radar sensor, and memory. The processor executes instructions from memory to cause the device to operate the sensor in a first operational mode in which the camera is maintained in a low-power mode. The processor further detects, using the sensor in the first mode, possible motion of an object within a threshold distance from the sensor. Responsive to the detecting, the processor transitions the sensor from the first mode to a second operational mode. The second mode consumes more power than the first mode. The processor determines, using the radar sensor in the second mode, that the possible motion of the object occurred in a region of interest, in response to which, it transitions the sensor from the second mode to a third operational mode. The third mode consumes more power mode than the second mode.

An autonomous guidance system that operates a vehicle in an autonomous mode includes a camera module, a radar module, and a controller. The camera module outputs an image signal indicative of an image of an object in an area about a vehicle. The radar module outputs a reflection signal indicative of a reflected signal reflected by the object. The controller determines an object-location of the object on a map of the area based on a vehicle-location of the vehicle on the map, the image signal, and the reflection signal. The controller classifies the object as small when a magnitude of the reflection signal associated with the object is less than a signal-threshold.

An autonomous vehicle (AV) can include a set of sensors generating sensor data corresponding to a surrounding environment of the AV. The AV can further include a control system that determines imminent lighting conditions for one or more cameras of the set of sensors, and executes a set of configurations for the one or more cameras to preemptively compensate for the imminent lighting conditions.

A method for controlling a medical device may be provided. The method may include obtaining, via one or more cameras, first data regarding a first motion of a subject in an examination space of the medical device. The method may include obtaining, via one or more radars, second data regarding a second motion of the subject. The method may further include generating, based on the first data and the second data, a control signal for controlling the medical device to scan at least a part of the subject.