A sensor-cluster apparatus, in which a sensor configured to detect and collect external environment information is mounted in a case. The sensor-cluster apparatus includes a body member on which one kind or more of sensors are mounted on one surface thereof, a case in which an inner space is provided, and one surface thereof is opened to define an opening and on which the body member is mounted so that each of the sensors is exposed to the opening, and a position control device mounted inside the case to adjust a mounting position or a mounting angle of the body member.

A sensor-cluster apparatus, in which a sensor configured to detect and collect external environment information is mounted in a case. The sensor-cluster apparatus includes a body member on which one kind or more of sensors are mounted on one surface thereof, a case in which an inner space is provided, and one surface thereof is opened to define an opening and on which the body member is mounted so that each of the sensors is exposed to the opening, and a position control device mounted inside the case to adjust a mounting position or a mounting angle of the body member.

A monitoring system for an aircraft can include an image sensor and a radar sensor. The system can provide noise compensation to a radar sample corresponding to a return radar signal received by the radar sensor based on information detected by the image sensor. The system can identify one or more object types in the image captured by the image sensor and then translate the identified object types to corresponding positions on a map. The system can correlate the radar sample to a position on the map and any object type located at that position can be identified. The system can then select a noise pattern that corresponds to the identified object type from the map and use the selected noise pattern to compensate the radar sample.

An image processing device 10 includes an image interface 18, a memory 19, and a controller 20. The image interface 18 acquires a captured image. The positions of specific feature points in a world coordinate system and reference positions of the specific feature points are stored in the memory 19. The controller 20 detects the specific feature points in the captured image. In a case where discrepancy between the position in the captured image and the reference position is found with regard to a predetermined percentage or more of the specific feature points, the controller 20 recalculates a calibration parameter.

An image processing device 10 includes an image interface 18, a memory 19, and a controller 20. The image interface 18 acquires a captured image. The positions of specific feature points in a world coordinate system and reference positions of the specific feature points are stored in the memory 19. The controller 20 detects the specific feature points in the captured image. In a case where discrepancy between the position in the captured image and the reference position is found with regard to a predetermined percentage or more of the specific feature points, the controller 20 recalculates a calibration parameter.

A mechanism is provided to reduce interference between vehicular radar systems through communicating radar parameters and physical orientation between vehicles and then using directional information to form clusters of radars, which will have consistent modulation parameters. Radar modulation parameters, such as starting frequency, center frequency, bandwidth, slope, ramp direction, timing, and the like for frequency-modulated continuous-wave (FMCW) radars, are adjusted to reduce or eliminate inter-cluster direct interference between clusters oriented in different directions. For other types of radars, in some embodiments, other operational parameters can be adjusted. In some embodiments, some modulation parameters also can be adjusted to reduce or eliminate intra-cluster indirect interference.

A method for determining a geographical location of a vehicle (10) includes using a camera/sensor device (20) of the vehicle for recording (S10) first image and sensor data (30) from surroundings of the vehicle (10) while the vehicle (10) is traveling a route. The first image and sensor data (30) are assigned geographical coordinates and are sent to a data evaluation unit (50) for creating a digital map. The method continues by using a second camera and sensor device (20) for recording (S40) second image and sensor data (30) from surroundings while the vehicle (10) is traveling the same route and sending (S50) the recorded second image and sensor data (30) to the data evaluation unit (50). The data evaluation unit (50) compares (S60) the recorded second image and sensor data (30) with the digital map of the surroundings (70) and determines (S70) a position of the vehicle (10).

A computer includes a processor and a memory storing instructions executable by the processor to receive radar data including a radar pixel having a radial velocity from a radar; receive camera data including an image frame including camera pixels from a camera; map the radar pixel to the image frame; generate a region of the image frame surrounding the radar pixel; determine association scores for the respective camera pixels in the region; select a first camera pixel of the camera pixels from the region, the first camera pixel having a greatest association score of the association scores; and calculate a full velocity of the radar pixel using the radial velocity of the radar pixel and a first optical flow at the first camera pixel. The association scores indicate a likelihood that the respective camera pixels correspond to a same point in an environment as the radar pixel.

This document describes techniques and systems for generating a fused object bounding box based on uncertainty. At least two bounding boxes, each associated with a different sensor, is generated. A fused center point and yaw angle as well as length, width, and velocity can be found by mixing the distributions of the parameters from each bounding box. A discrepancy between the center points of each bounding box can be used to determine whether to refine the fused bounding box (e.g., find an intersection between at least two bounding boxes) or consolidate the fused bounding box (e.g., find a union between at least two bounding boxes). This results in the fused bounding box having a confidence level of the uncertainty associated with the fused bounding box. In this manner, better estimations of the uncertainty of the fused bounding box may be achieved to improve tracking performance of a sensor fusion system.

A vehicle includes a front camera, a front radar, a corner radar, a corner LiDAR, and a controller configured to generate a first fusion mode by processing image data and radar data or to generate a second fusion mode by processing radar data and LiDAR data, wherein the controller changes the first fusion mode to the second fusion mode when the controller detects an abnormality of the front camera while performing avoidance control of the vehicle based on the first fusion mode, and performs the avoidance control based on the second fusion mode for a predetermined time period.