This disclosure introduces methods and devices for robotic police officer assistance, incorporating an articulating spotlight, removable camera, spike strip delivery, and chemical agent dispenser, aiming to enhance officer safety during common police activities by allowing remote operation and minimizing the need for officers to be physically present in dangerous situations. Presented herein are methods and devices for robotic police officer assistance. The device comprises an articulating spotlight, a removeable camera, a spike strip delivery mechanism, and a chemical agent dispenser. The robot is controlled by a police officer so that the actions of the robot can be controlled from a position that is safe for the officer. By directing the robotic device, it is not necessary for an officer to be placed in in a dangerous position to conduct some of the most common police activities. When an officer is not required to put their personal safety on the line during common police activities, serious injury to police officers can be avoided. Also disclosed herein are methods of using the robotic device during police activities or emergencies that would otherwise require an officer to be put in harm's way.

A positioning processing unit measures the position of a work vehicle by a predetermined positioning method on the basis of a GNSS signal received from a satellite. A travel processing unit causes the work vehicle to travel automatically on the basis of position information of the work vehicle. A switching processing unit can switch between an RTK method and a DGPS method. A setting processing unit sets the work mode of the work vehicle to either a work accuracy preferential mode or a work continuity preferential mode. The switching processing unit switches between the RTK method and the DGPS method on the basis of the work mode and the positioning state.

A method of executing a charging plan for at least one vehicle in a transportation system includes: receiving, at a charging infrastructure control system, operational state and energy consumption information for a plurality of network-enabled vehicles; predicting a geolocation of one or more vehicles of the plurality of network-enabled vehicles in a geographic region; allocating the one or more vehicles of the plurality of network-enabled vehicles to charging infrastructure in the geographic region; and optimizing, at an artificial intelligence system, a parameter of the charging plan based on the prediction of the geolocation of the one or more vehicles in the geographic region.

A transportation system to optimize an operating parameter of a vehicle based on a physiological state of an occupant of the vehicle. The transportation system includes a sensor to sense a physiological condition of the occupant and to output data based on the sensed physiological condition. The sensor includes a movement sensor to detect physical actions of the vehicle occupant. The transportation system further includes a real-time control system to receive and process the data to determine an emotional state of the occupant based on the detected physical actions and to optimize, for achieving a favorable emotional state of the occupant, at least one operating parameter of the vehicle in response to the detected emotional state of the occupant.

A method of optimizing a state of a rider of a vehicle. The method includes: receiving data from a wearable sensor worn by the rider of the vehicle indicative of an emotional state of the rider; comparing the data received from the wearable sensor to stored wearable sensor data in which quantitative patterns present in the stored sensor data are labelled as emotional states; determining a pattern of an emotional state based on the data received from the wearable sensor and the comparison to the stored wearable sensor data; detecting a change in emotional state of the rider based on a detected change in the data received from the wearable sensor indicative of a change in the emotional state of the rider; and adjusting an operational parameter of the vehicle in real time in response to the detected change in the emotional state of the rider.

A system may collect human operator interactions with a vehicle control system interface operatively connected to a vehicle, and may collect vehicle response and operating conditions associated at least contemporaneously with the human operator interaction. Environmental information is collected contemporaneously with the human operator interaction. An artificial intelligence system is trained to control the vehicle with an optimized margin of safety while mimicking the human operator, the training including instructing the artificial intelligence system to take input from an environment data collection module about instances of environmental information associated with the contemporaneously collected vehicle response and operating conditions, where the optimized margin of safety is achieved by training the artificial intelligence system to control the vehicle based on a set of human operator interaction data collected from interactions of an expert human vehicle operator and a set of outcome data from a set of vehicle safety events.

A system may collect human operator interactions with a vehicle control system interface operatively connected to a vehicle, and may collect vehicle response and operating conditions associated at least contemporaneously with the human operator interaction. Environmental information is collected contemporaneously with the human operator interaction. An artificial intelligence system is trained to control the vehicle with an optimized margin of safety while mimicking the human operator, the training including instructing the artificial intelligence system to take input from an environment data collection module about instances of environmental information associated with the contemporaneously collected vehicle response and operating conditions, where the optimized margin of safety is achieved by training the artificial intelligence system to control the vehicle based on a set of human operator interaction data collected from interactions of an expert human vehicle operator and a set of outcome data from a set of vehicle safety events.

A system may receive social data from a plurality of social data sources. A system may process the social data using semantic analysis to detect keywords in the social data indicative of a group transportation need. A system may identify a plurality of individuals who share a group transportation need. A system may predict the group transportation need using a neural network trained to predict transportation needs based on the detected keywords. A system may provide a transportation recommendation based on the prediction.

A system may receive social data from a plurality of social data sources. A system may process the social data using semantic analysis to detect keywords in the social data indicative of a group transportation need. A system may identify a plurality of individuals who share a group transportation need. A system may predict the group transportation need using a neural network trained to predict transportation needs based on the detected keywords. A system may provide a transportation recommendation based on the prediction.

A method of altering an operating state of a transportation system may include: receiving social data from a plurality of social data sources about at least one occupant of a vehicle of the transportation system; analyzing, at a first neural network, the social data and associating the social data with a route plan for the vehicle occupied by the at least one occupant; predicting, by the first neural network, an effect on a satisfaction of the at least one occupant based on the route plan of the vehicle through an analysis of the social data; and altering, by the first neural network, a route plan of the transportation system responsive to the predicted effect.