A sensor information fusion method of an embodiment includes obtaining N sensor tracks from each of a plurality of sensors with respect to a target located around a vehicle, calculating association costs of the N sensor tracks with respect to M reference tracks, and storing the association costs in a matrix form, and calculating an arrangement of reference tracks and sensor tracks that minimize the association costs with respect to the matrix, and outputting a sensing information result with respect to the target according to the arrangement of the reference tracks and the sensor tracks calculated by the plurality of sensors.

Examples of imaging systems are described herein which may implement microwave or millimeter wave imaging systems. Examples described may implement partitioned inverse techniques which may construct and invert a measurement matrix to be used to provide multiple estimates of reflectivity values associated with a scene. The processing may be partitioned in accordance with a relative position of the antenna system and/or a particular beamwidth of an antenna. Examples described herein may perform an enhanced resolution mode of imaging which may steer beams at multiple angles for each measurement position.

A sensing system provided in a vehicle capable of running in an autonomous driving mode, includes: a LiDAR unit configured to acquire point group data indicating surrounding environment of the vehicle; and a LiDAR control module configured to identify information associated with a target object existing around the vehicle, based on the point group data acquired from the LiDAR unit. The LiDAR control module is configured to control the LiDAR unit so as to increase a scanning resolution of the LiDAR unit in a first angular area in a detection area of the LiDAR unit, wherein the first angular area is an area where the target object exists.

Provided is a sensor recognition integration device capable of reducing the load of integration processing so as to satisfy the minimum necessary accuracy required for vehicle travel control, and capable of improving processing performance of an ECU and suppressing an increase in cost. A sensor recognition integration device B006 that integrates a plurality of pieces of object information related to an object around an own vehicle detected by a plurality of external recognition sensors includes: a prediction update unit 100 that generates predicted object information obtained by predicting an action of the object; an association unit 101 that calculates a relationship between the predicted object information and the plurality of pieces of object information; an integration processing mode determination unit 102 that switches an integration processing mode for determining a method of integrating the plurality of pieces of object information on the basis of a positional relationship between a specific region (for example, a boundary portion) in an overlapping region of detection regions of the plurality of external recognition sensors and the predicted object information; and an integration target information generation unit 104 that integrates the plurality of pieces of object information associated with the predicted object information on the basis of the integration processing mode.

Sensors coupled to a vehicle are calibrated, optionally using a dynamic scene with sensor targets around a motorized turntable that rotates the vehicle to different orientations. One vehicle sensor captures a representation of one feature of a sensor target, while another vehicle sensor captures a representation of a different feature of the sensor target, the two features of the sensor target having known relative positioning on the target. The vehicle generates a transformation that maps the captured representations of the two features to positions around the vehicle based on the known relative positioning of the two features on the target.

Systems and methods of adjusting zone associated risks of a coverage zone covered by one or more sensors of an autonomous driving vehicle (ADV) operating in real-time are disclosed. As an example, the method includes defining a performance limit detection window associated with a first sensor based on a mean time between failure (MTBF) lower limit of the first sensor and a MTBF upper limit of the first sensor. The method further includes determining whether an operating time of the ADV operating in autonomous driving (AD) mode is within the performance limit detection window associated with the first sensor. The method further includes in response to determining that the operating time of the ADV operating in AD mode is within the performance limit detection window of the first sensor, adjusting a zone associated risk of the coverage zone to a performance risk of a second sensor.

The present disclosure is directed to processing data associated with a non-radar type sensing device to identify data points associated with a radar type sensing device that are likely secondary radar reflections such that a processor of a sensing apparatus can direct processing resources to processing radar data that are associated with primary radar reflections. The receipt of secondary radar reflections may cause a processor of a sensing apparatus to identify that an object is located at a location when there is no object in that location. Because of this, methods and apparatus of the present disclosure identify and avoid processing radar data that are likely to be associated with a object that does not exist. Eliminating false radar data, therefore, can prevent a processor of a sensing apparats from performing unnecessary processing tasks and can help prevent that processor from making false determinations.

A service verification system for a rear loading refuse vehicle includes a motion sensor on a tailgate of the refuse vehicle. The motion sensor is configured to sense refuse being loaded into a tailgate hopper and to provide a time-stamped triggering signal corresponding to a refuse loading event. An onboard processor is configured to receive the triggering signal and evaluate other sensor data to correlate the refuse loading event with an entity.

A parking management system that facilitates motorist guidance, payment, violation detection, and enforcement using highly accurate space occupancy detection, unique vehicle identification, guidance displays, payment acceptance, violation detection, enforcement data generation, electronic booting, and towing management is described. The system enables reduced time to find parking, congestion mitigation, accurate violation detection, and easier enforcement, and increased payment and enforcement revenues to cities.

Methods, systems, and apparatus, including medium-encoded computer program products, for 3D flight tracking of objects includes, in at least one aspect, a method including obtaining two dimensional image data of a golf ball in flight, the two dimensional image data originating from a camera; obtaining radar data of the golf ball in flight, the radar data originating from a Doppler radar device associated with the camera; interpolating the radar data to generate interpolated radar data of the golf ball in flight; and blending radial distance information derived from the interpolated radar data of the golf ball in flight with angular distance information derived from the two dimensional image data of the golf ball in flight to form three dimensional location information of the golf ball in three dimensional space.