A device may receive emergency data, traffic data, network performance data, crime data, and gunshot data associated with a geographical area and may identify a location within the geographical area based on the emergency data, the traffic data, the network performance data, the crime data, and the gunshot data. The device may determine, based on the emergency data, the traffic data, the network performance data, the crime data, and the gunshot data for the location, a risk level for the location and may identify an autonomous vehicle based on the risk level, the traffic data, and the network performance data for the location. The device may determine a route for the autonomous vehicle to the location based on the traffic data and the network performance data for the location, and may perform actions based on the autonomous vehicle and the route.

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.

A testing system of a perimeter intrusion detection system established in relation to a barrier comprises a mobile base; a member extendable from the mobile base towards a subject portion of the barrier; and a sensor for determining when the member contacts the barrier.

Systems and methods are provided for adding supervised stops to an autonomous vehicle route. In particular, systems and methods are provided for allowing a primary passenger, who is accompanied by one or more other passengers, to pause a ride, exit the vehicle, and request supervision of the other passengers while the primary passenger is away from the vehicle. Supervision of the other passengers can include monitoring vehicle temperature, making sure the other passengers remain safely inside the vehicle, preventing strangers from accessing the vehicle, providing any requested feedback regarding the other passengers to the primary passenger, and enabling communication between the first passenger and the other passengers.

Autonomous vehicles may utilize neural networks for image classification in order to navigate infrastructures and foreign environments, using context dependent transfer learning adaptation. Techniques include receiving a transferable output layer from the infrastructure, which is a model suitable for the infrastructure and the local environment. Sensor data from the autonomous vehicle may then be passed through the neural network and classified. The classified data can map to an output of the transferable output layer, allowing the autonomous vehicle to obtain particular outputs for particular context dependent inputs, without requiring further parameters within the neural network.

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 neighborhood watch system includes a central control unit (CCU), an environment sensing units mounted on one or more autonomous vehicles which are self-driven vehicles, alarm units mounted on the autonomous vehicles, the environment sensing units being operable to continuously obtain 360° views of surroundings of the autonomous vehicle, and to send the 360° views of the surroundings to the CCU. The CCU is configured to receive the 360° views of the surroundings and to analyze the 360° views to obtain at least one object of interest stored in the CCU. When the CCU obtains the one object of interest, the CCU informs a security agency about time and location of the one object of interest, and selectively activates the alarm unit. The neighborhood watch system further includes a communication unit including one of a handheld device and a computer operatively connected with the CCU.

An operation management system includes an automatic traveling vehicle and a management server. The vehicle includes a first processor, and is configured to transport at least one of people or luggage. The management server includes a second processor, and is configured to communicate with the vehicle and manage the operation thereof. The first processor or second processor is configured to: determine whether there is a transport task in which the vehicle transports at least one of people or luggage; and, when there is no transport task, execute a task switching process of causing the vehicle to execute any one of a patrol task in which the vehicle performs a patrol of an operating area of the vehicle, a cleaning task in which the vehicle performs a cleaning of the operating area, and a patrol cleaning task in which the vehicle performs both the patrol and the cleaning.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting occupancies of agents. One of the methods includes obtaining scene data characterizing a current scene in an environment; and processing a neural network input comprising the scene data using a neural network to generate a neural network output, wherein: the neural network output comprises respective occupancy outputs corresponding to a plurality of agent types at one or more future time points; the occupancy output for each agent type at a first future time point comprises respective occupancy probabilities for a plurality of locations in the environment; and in the occupancy output for each agent type at the first future time point, the respective occupancy probability for each location characterizes a likelihood that an agent of the agent type will occupy the location at the first future time point.