A vehicle includes a controller, programmed to responsive to detecting, via a vehicle sensor, an obstacle blocking a route on which the vehicle is traversing, report the obstacle and blockage to a server via a wireless connection; responsive to receiving a command from the server instructing to perform an exploratory maneuver to remove the obstacle from the route, execute the command via an autonomous driving controller; and report an implementation result of the exploratory maneuver to the server.

A driving assistance system includes a plurality of vehicles on which a plurality of microphones is mounted respectively and a server having an acquisition unit configured to acquire sound signals recorded by the microphones and position information of the vehicles. The server further has an estimation unit configured to estimate a position of one or more sound sources based on the sound signals and the position information and a providing unit configured to provide the estimated position of the one or more sound sources to the vehicles.

The present invention relates to a computer-implemented method and a system for testing the output of a neural network (1) having a plurality of layers (11), which detects or classifies objects. The method comprises the step (S1) of reading at least one result from at least one first layer (11) and the confidence value thereof, which is generated in the first layer (11) of a neural network (1), and the step (S2) of checking a plausibility of the result by taking into consideration the confidence value thereof so as to conclude whether the object detection by the neural network (1) is correct or false. The step (S2) of checking comprises comparing the confidence value for the result with a predefined threshold value. In the event that it is concluded in the checking step (S2) that the object detection is false, output of the object falsely detected by the neural network is prevented.

The present disclosure provides a method and a device for controlling a vehicle, a device and a storage medium, and relates to the field of unmanned vehicle technologies. The method includes: acquiring a vehicle environment image by an image acquirer during traveling of the vehicle; extracting a static environment image included in the vehicle environment image; obtaining a planned vehicle traveling trajectory by taking the static environment image as an input of a trajectory planning model; and controlling the vehicle to travel according to the planned vehicle traveling trajectory.

An issue reporting and detection system is discussed. A user can capture an image with a mobile device of an issue at a location in a facility. An application executing on the mobile device can receive an input associated with the image. The mobile application can transmit image and the input associated with the image to a computing system which can determine a location at which the image was taken in the facility. The computing system can transmit a command to a UAGV to navigate to the determined location of the facility where the UAGV can capture an image using an image capturing device coupled to the UAGV, of the reported issue at the location in the facility. The UAGV can transmit the image to the computing system to confirm the type of the facility issue based on the image captured by the UAGV. The computing system can transmit an appropriate alert after confirming the issue.

A vehicle for collecting image data of a target object for generating a classifier. The vehicle includes an image sensor and an electronic processor. The electronic processor is configured to determine a plurality of potential trajectories of the vehicle, determine, for each of the plurality of potential trajectories of the vehicle, a total number of views including the target object that would be captured by the image sensor as the vehicle moved along the respective trajectory, and determine a key trajectory of the vehicle from the plurality of potential trajectories based on the total number of views including the target of the key trajectory.

Systems, methods, and apparatus related to dynamically adjusting sensing and/or processing resources of a vehicle. In one approach, sensor data is collected by sensing devices of the vehicle. A controller of the vehicle uses the sensor data to control one or more functions of the vehicle. The controller evaluates the sensor data to determine a context of operation (e.g., weather, lighting, and/or traffic) for the vehicle. Based on the context of operation, the controller adjusts the operation of one or more of the sensing or processing devices in real-time during operation of the vehicle. In one example, the adjustment reduces power consumption by the vehicle.

A survey system includes a controller storing a map comprising a plurality of nodes representing data capture points of the environment. The controller is configured to segment the plurality of nodes into a plurality of communities, where each community from among the plurality of communities includes a set of nodes from among the plurality of nodes. The controller is configured to generate, for each community from among the plurality of communities, one or more traversability scores. The controller is configured to assign, for each community from among the plurality of communities, at least one robot from among a plurality of robots to survey the community based on the one or more traversability scores. The controller is configured to deploy, for each community from among the plurality of communities, at least one of the plurality of robots based on the plurality of robots assigned to the community.

Techniques pertaining to an autonomous police vehicle are described. A method may involve a processor associated with an autonomous vehicle obtaining an indication of violation of one or more traffic laws by a first vehicle. The method may also involve the processor maneuvering the autonomous vehicle to pursue the first vehicle. The method may further involve the processor remotely executing one or more actions with respect to the first vehicle.

A method for surveilling with a vehicle includes receiving an area-of-interest data from a vehicle user. The area-of-interest data delineates a virtual fence around the vehicle. The virtual fence is boundary of an area of interest around the vehicle. The vehicle is parked adjacent to an infrastructure. The method further includes receiving sensor data from a sensor of the vehicle that is monitoring the area of interest. Further, the method includes detecting a predetermined activity by a predetermined object of interest within the area of interest based on the sensor data received from the sensor of the vehicle. Also, the method includes notifying the vehicle user of the predetermined activity sensed using the sensor of the vehicle.