This disclosure relates to a distributed data center that includes resources carried by a fleet of vehicles. The system includes sensors configured to generate output signals conveying information related to the vehicles and/or the surroundings of vehicles. The system includes a remote computing server configured to maintain map data and distribute it to the fleet, including local map data to individual vehicles pertaining to their surroundings. Individual vehicles may compare the local map data with the information related to their individual surroundings. Based on such comparisons, individual vehicles may detect discrepancies between the local map data and the information related to their individual surroundings. The remote computing server may modify and/or update the map data based on the detected discrepancies.

Various technologies described herein pertain to routing autonomous vehicles based upon spatiotemporal factors. A computing system receives an origin location and a destination location of an autonomous vehicle. The computing system identifies a route for the autonomous vehicle to follow from the origin location to the destination location based upon output of a spatiotemporal statistical model. The spatiotemporal statistical model is generated based upon historical data from autonomous vehicles when the autonomous vehicles undergo operation-influencing events. The spatiotemporal statistical model takes, as input, a location, a time, and a direction of travel of the autonomous vehicle. The spatiotemporal statistical model outputs a score that is indicative of a likelihood that the autonomous vehicle will undergo an operation-influencing event due to the autonomous vehicle encountering a spatiotemporal factor along a candidate route. The autonomous vehicle then follows the route from the origin location to the destination location.

A method determines an anticipated occupation of charging points and a charging strategy for a specified route. The method provides traffic data which is representative for the current traffic density on the route specified. An anticipated occupation of charging points along the specified route can be determined on the basis of the traffic data. A charging strategy can be determined on the basis of the traffic data and the determined anticipated occupation of charging points. The provision of information regarding a charging strategy to a driver allows the time required for the specified route to be reduced.

A drive assistance apparatus predicts demand for vehicles in each area including a driving route, based on a demand prediction model for predicting the demand in the area. The drive assistance apparatus also predicts the frequency of occurrence of an obstacle in each of the regions obtained by dividing the area, based on a frequency model for predicting the frequency of occurrence of the obstacle in the region, and generates a driving route of the vehicle, based on the predicted demand in the area and the predicted frequency of occurrence of the obstacle.

A method of directing traffic flow includes receiving, by a processor, navigation information from a vehicle in a multi-edge communication network, and receiving, by the processor, a status of an edge node traffic control device of the multi-edge communication network. The edge node traffic control device is configured to regulate traffic flow on a path segment. The method includes determining, by the processor, a navigation command based on the navigation information and the status, and outputting, by the processor, the navigation command to the vehicle through the multi-edge communication network.

To implement a configuration to calculate a manual driving recoverable time required for a driver who is executing automatic driving in order to achieve a requested recovery ratio (RRR) for each road section, and issue a manual driving recovery request notification on the basis of the calculated time. A data processing unit is included, which calculates a manual driving recoverable time required for a driver who is executing automatic driving in order to achieve a predefined requested recovery ratio (RRR) from automatic driving to manual driving and determines notification timing of a manual driving recovery request notification on the basis of the calculated time. The data processing unit acquires the requested recovery ratio (RRR) for each road section set as ancillary information of a local dynamic map (LDM), and calculates the manual driving recoverable time for each road section scheduled to travel, using learning data for each driver.

A vehicle control system includes: an on-board control module, configured to control driving of the vehicle according to a control instruction sent by a vehicle dispatching command platform; a vehicle state module, configured to collect state information of the vehicle; an environment sensing module, configured to collect first road environment information of the vehicle; a UAV scanning module, configured to collect second road environment information of the vehicle; a data center module, configured to generate fusion information according to the state information, the first road environment information, and the second road environment information; a map module, configured to generate a driving route map of the vehicle according to the fusion information; and a vehicle dispatching command platform, configured to generate the control instruction according to the fusion information and the driving route map.

A method for regulating traffic emissions across a street network comprises calculating, by an external control entity, a real-time location-dependent immission load across the street network based on at least one of environmental data, traffic data and configuration data of the street network, providing, by motor vehicles using the street network, navigation data characterizing a route of each respective motor vehicle along the street network and emission data characterizing exhaust emission levels of each respective motor vehicle along its route, and calculating an optimized driving route for each motor vehicle along the street network based on the calculated immission load and the exhaust emission levels of the motor vehicles. The optimized driving route is calculated by the external control entity and transmitted to each motor vehicle via a wireless communication network, or wherein the optimized driving route is calculated by an internal control unit of each respective motor vehicle.

An information processing apparatus acquires vehicle state information for each of a plurality of vehicles. The information processing apparatus estimates road surface state information of a road surface on which each of the vehicles has traveled, based on the acquired vehicle state information for each of the vehicles. The information processing apparatus estimates the road surface state information by inputting the acquired vehicle state information to a trained model that outputs the road surface state information in a case where the vehicle state information is input and that has been trained in advance based on training data in which the vehicle state information and the road surface state information are associated with each other.

A system including a processor and memory may provide for automatically activating or deactivating a feature of a fleet vehicle. For example, one or more fleet vehicles may include one or more of a global-positioning system, a speed governor, electronically-controlled brakes, an electronically-controlled accelerator, a speed limiter, or an on-board computer with a processor and memory. One or more features may be activated by a local or remote computing device or system. For example, a system may determine one or more recommended routes between two or more locations. The system may track a fleet vehicle's progress along a route, and activate a feature of the fleet vehicle based on the fleet vehicle following or not following the recommended route. For example, the system may cause activation of a speed limiter on the fleet vehicle, disable the fleet vehicle, and/or activate or deactivate autonomous features of the fleet vehicle.