An automatic parking system includes an infrastructure sensor that is able to detect a state of a parking lot. A blind spot area that does not allow the state of the parking lot to be detected by the infrastructure sensor is identified in the parking lot, and the state of the parking including the blind spot area, the state of the parking lot being detected by an on-board sensor of a vehicle in the parking lot, is acquired. Based on the state of the parking lot detected by the infrastructure sensor and the state of the parking lot that includes the blind spot area and that is detected by the on-board sensor of the vehicle in the parking lot, a travel route for the vehicle traveling in the parking lot is generated.

An automatic parking system includes an infrastructure sensor that is able to detect a state of a parking lot. A blind spot area that does not allow the state of the parking lot to be detected by the infrastructure sensor is identified in the parking lot, and the state of the parking including the blind spot area, the state of the parking lot being detected by an on-board sensor of a vehicle in the parking lot, is acquired. Based on the state of the parking lot detected by the infrastructure sensor and the state of the parking lot that includes the blind spot area and that is detected by the on-board sensor of the vehicle in the parking lot, a travel route for the vehicle traveling in the parking lot is generated.

A system includes an aerially mounted video capture device and a processor. The processor is operable to direct the video capture device to obtain an image of a monitored area and process the image to identify objects of interest represented in the image. The processor is also operable to generate bounding perimeter virtual objects for the identified objects of interest, which substantially surround their respective objects of interest. The processor is further operable to determine danger zones for the identified objects of interest based on the bounding perimeter virtual objects. Each danger zone represents a distance threshold about a respective object of interest. The processor is further operable to determine at least one near-miss condition based at least in part on an actual or predicted overlap of danger zones for two or more objects of interest, and to generate an alert at least partially in response to the near-miss condition.

A system includes an aerially mounted video capture device and a processor. The processor is operable to direct the video capture device to obtain an image of a monitored area and process the image to identify objects of interest represented in the image. The processor is also operable to generate bounding perimeter virtual objects for the identified objects of interest, which substantially surround their respective objects of interest. The processor is further operable to determine danger zones for the identified objects of interest based on the bounding perimeter virtual objects. Each danger zone represents a distance threshold about a respective object of interest. The processor is further operable to determine at least one near-miss condition based at least in part on an actual or predicted overlap of danger zones for two or more objects of interest, and to generate an alert at least partially in response to the near-miss condition.

A method of operating a monitoring system may include receiving details of an area to be monitored, and receiving one of a) position details of an asset moving within a field of view of the area to be monitored, or b) position details of a camera configured to monitor the area. The method may include determining one of c) a position of the camera responsive to receiving the position details of the asset moving within the field of view of the area; or d) a position of the asset moving within the field of view responsive to receiving the position details of the camera.

A communication system that determines a context and an intent of a specific remote vehicle located in a surrounding environment of a host vehicle includes one or more controllers for receiving sensed perception data including sensed perception data. The one or more controllers execute instructions to determine a plurality of vehicle parameters related to the specific remote vehicle. The the one or more controllers execute instructions to associate the specific remote vehicle with a specific lane of travel of a roadway based on map data. The one or more controllers determines possible maneuvers, possible egress lanes, and a speed limit for the specific remote vehicle for the specific lane of travel based on the map data, and determines the context and the intent of the specific remote vehicle based on the plurality of vehicle parameters, the possible maneuvers, the possible egress lanes for the specific remote vehicle, and the speed limit related to the specific remote vehicle.

A communication system that determines a context and an intent of a specific remote vehicle located in a surrounding environment of a host vehicle includes one or more controllers for receiving sensed perception data including sensed perception data. The one or more controllers execute instructions to determine a plurality of vehicle parameters related to the specific remote vehicle. The the one or more controllers execute instructions to associate the specific remote vehicle with a specific lane of travel of a roadway based on map data. The one or more controllers determines possible maneuvers, possible egress lanes, and a speed limit for the specific remote vehicle for the specific lane of travel based on the map data, and determines the context and the intent of the specific remote vehicle based on the plurality of vehicle parameters, the possible maneuvers, the possible egress lanes for the specific remote vehicle, and the speed limit related to the specific remote vehicle.

An image processing system includes at least one memory configured to store video data, and a processor configured to perform image processing on the video data. The processor is configured to select a preregistered target vehicle from among vehicles included in the video data. The processor is configured to clip, in the video data, a plurality of frames from the video data before the preregistered target vehicle is selected, and generate an image including the target vehicle by using the clipped frames.

An image processing system includes at least one memory configured to store video data, and a processor configured to perform image processing on the video data. The processor is configured to select a preregistered target vehicle from among vehicles included in the video data. The processor is configured to clip, in the video data, a plurality of frames from the video data before the preregistered target vehicle is selected, and generate an image including the target vehicle by using the clipped frames.

Disclosed is a technique for a railway disaster monitoring system for monitoring a foreign matter on a railway, which includes a camera and an image processing unit that receives a railway image captured by the camera. The image processing unit includes a segmented image acquisition unit that obtains a plurality of segmented images including a rail from the railway image received from the camera and scales the segmented images to obtain segmented image blocks of a predetermined size, and a segmented image determination unit which includes deep neural network (DNN) discriminators trained with deep learning neural networks and inputs the segmented image blocks to the DNN discriminators to determine whether the railway is in a normal state in which there is no foreign matter on the railway except a train passing by. Using the segmented images, a foreign matter on a railway can be rapidly and easily determined using a deep learning neural network even with a small number of resources.