A smart street parking management system includes a smart street parking meter a smart cloud parking management server connected to the smart street parking meter through a wireless networking technology and configured to regularly receive parking information of a vehicle parked on a smart street parking grid and status information of the smart street parking meter uploaded by the smart street parking meter. The status information of the smart street parking meter includes an empty-space status of the street parking grid detected by the smart street parking meter and a battery status of a rechargeable battery of the smart street parking meter. The parking information of the vehicle includes the plate number of the vehicle, the parking fee of the vehicle, and a payment status of the parking fee of the vehicle.

A bad weather judgment apparatus and a bad weather judgment method thereof are disclosed. The apparatus includes a target recognizer configured to recognize targets in detection areas of a plurality of heterogeneous sensors based on sensor recognition information received from the heterogeneous sensors, a counter configured to count the number of cases based on detection states of the heterogeneous sensors about a same target among the targets, and a bad weather judger configured to determine whether the same target is present in bad weather judgment zones of the detection areas of the heterogeneous sensors, control the counter to increment or decrement the number of the cases based on detection states of the heterogeneous sensors about whether the same target is present in the bad weather judgment zones, and judge current weather to be bad weather when the number of the cases is greater than a threshold value.

A measurement system is provided for predicting a future status of a refractory lining that is lined over an inner surface of an outer wall of a manufacturing vessel and exposed to an operational cycle during which the refractory lining is exposed to a high-temperature environment for producing a non-metal and the produced non-metal. The system includes one or more laser scanners and a processor. The laser scanners are configured to conduct one or more pre-operational laser scans of the refractory lining prior to the operational cycle to collect data related to pre-operational cycle structural conditions, and one or more post-operational laser scans of the refractory lining after the operational cycle to collect data related to post-operational cycle structural conditions of the refractory lining. The processor is configured to predict future status of the refractory lining after subsequent operational cycles based on the determined exposure impact of the operational cycle.

A safety system for the localization of at least one spatially variable object having at least one control and evaluation unit, having at least one radio location system, having at least one spatially resolving sensor for the detection of an object in a detection zone of the spatially resolving sensor, wherein the radio location system has at least three arranged radio stations, wherein at least one radio transponder is arranged at the object, wherein position data of the radio transponder and thus position data of the object can be determined by means of the radio location system, wherein the position data can be transmitted from the radio station of the radio location system to the control and evaluation unit, wherein the control and evaluation unit is configured to cyclically detect the position data of the radio transponder and information on the object in the detection zone can be determined by means of the spatially resolving sensor.

An automatic wall climbing type radar photoelectric robot system for damages of a bridge and tunnel structure, mainly including a control terminal, a wall climbing robot and a server. The wall climbing robot generates a reverse thrust by rotor systems, moves flexibly against the surface of a rough bridge and tunnel structure by adopting an omnidirectional wheel technology, and during inspection by the wall climbing robot, bridges and tunnels do not need to be closed, and the traffic is not affected. Bridges and tunnels can divide into different working regions only by arranging a plurality of UWB base stations, charging and data receiving devices on the bridge and tunnel structure by means of UWB localization, laser SLAM and IMU navigation technologies, a plurality of wall climbing robots supported to work at the same time, automatic path planning and automatic obstacle avoidance realized, and unattended regular automatic patrolling can be realized.

A method is described which includes receiving a point cloud having a plurality of data points each representing a 3D location in a 3D space, the point cloud being obtained using a detection and ranging (DAR) sensor. For each data point, associating the data point with a 3D volume containing the 3D location of the data point, the 3D volume being defined using a 3D lattice that partitions the 3D space based on spherical coordinates. For at least one 3D volume, the data points are sorted within the 3D volume based on at least one dimension of the 3D lattice; and the sorted data points are stored as a set of ordered data points. The method also includes performing feature extraction on the set of ordered data points to generate a set of ordered feature vectors and providing the set of ordered feature vectors to perform a machine learning inference task.

A method and electronic device for determining relevant signals in radar signal processing. The electronic device includes a radar transceiver, a memory, and a processor. The processor is configured to cause the electronic device to obtain, via the radar transceiver of the electronic device, radar measurements for one or more modes in a set of modes; process the radar measurements to obtain a set of radar images; identify relevant signals in the set of radar images based on signal determination criteria for an application; and perform the application using only the relevant signals.

Provided is an object recognition device including a prediction processing unit, a temporary setting unit, and a association processing unit. The prediction processing unit predicts, as a prediction position on an object model obtained by modeling a tracking target, a position of a movement destination of the tracking target based on a trajectory formed by movement of at least one object of a plurality of objects as the tracking target. The temporary setting unit sets, based on specifications of a sensor that has detected the tracking target, a position of at least one candidate point on the object model. The association processing unit sets, based on the position of the candidate point and the prediction position, a reference position on the object model. The association processing unit determines whether the position of the detection point and the prediction position associate with each other based on a positional relationship between a association range which is set so that the association range has a reference position on the object model as a reference and a detection point at a time when the sensor has detected the at least one object of the plurality of objects.

Sensor data is received from an array of sensors configured to capture one or more objects in an external environment of an autonomous vehicle. A first sensor group is selected from the array of sensors based on proximity data or environmental contexts. First sensor data from the first sensor group is prioritized for transmission based on the proximity data or environmental contexts.

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.