An automated valet parking system performs automated parking in response to a user request for entry or pick-up from a user and causes the self-driving vehicle to perform automated driving along a target route to enter or pick up. The automated valet parking system, when a self-driving vehicle stops in an emergency or experiences a communication interruption and becomes a failed vehicle, sets a restricted area based on parking place map information and a position of the failed vehicle and sets an allowed area in a parking place, including a parking space(s) located outside the restricted area, provides an instruction, based on a position of a subject vehicle that is a self-driving vehicle as a subject of the user request and the set restricted area, to the subject vehicle, and sets a target route such that the subject vehicle parks in the parking space located in the allowed area.

Embodiments relate to a system, computer program product, and method for determining shelter areas for two-wheeler vehicles, and, more specifically, for dynamically distinguishing the behavior of two-wheeler vehicles and non-two-wheeler vehicles as an indicator of shelter areas from inclement weather. The behavior of the vehicles is distinguished through a plurality of two-wheeler vehicles slowing down and congregating at a particular location as a shelter against inclement weather, while non-two-wheeler vehicles may slow down, however, not stop proximate this location.

A processing apparatus for generating route navigation data is provided, to, generate training data based on road network data corresponding to a network of roads in a defined geographical area, and journey data sets, each journey data set comprising data indicative of a journey by a road user through the network of roads and being derived using geolocation transmissions from a communications device of the road user, train a classifier model based on the training data, apply the trained classifier model on road data corresponding to a road in the defined geographical area, for the trained classifier model to predict a direction of traffic flow on the road, and generate the route navigation data indicative of the predicted direction of the traffic flow on the road. A method for generating route navigation data is also provided.

An example operation includes one or more of determining, by a transport, an energy transfer condition exists along a route, routing, by the transport, to a location on the route based on the energy transfer condition exceeding an energy transfer value and based on one or more traffic conditions, aligning, by the transport, a position of the transport at the location to wirelessly receive an energy transfer, and receiving, by the transport, the energy transfer while the transport is in motion.

A vehicle driving control method, comprising: during vehicle driving, acquiring location information of a vehicle for communication within a predetermined distance range (101); determining, based on the location information, a location relationship with the vehicle for communication (102); determining, according to the location relationship, a driving path of the vehicle for communication (103); and sending a communication instruction to the vehicle for communication such that the vehicle for communication drives according to the driving path (104).

The present invention provides specific systems, methods and algorithms based on artificial intelligence expert system technology for determination of preferred routes of travel for electric vehicles (EVs). The systems, methods and algorithms provide such route guidance for battery-operated EVs in-route to a desired destination, but lacking sufficient battery energy to reach the destination from the current location of the EV. The systems and methods of the present invention disclose use of one or more specifically programmed computer machines with artificial intelligence expert system battery energy management and navigation route control. Such specifically programmed computer machines may be located in the EV and/or cloud-based or remote computer/data processing systems for the determination of preferred routes of travel, including intermediate stops at designated battery charging or replenishing stations. Expert system algorithms operating on combinations of expert defined parameter subsets for route selection are disclosed. Specific fuzzy logic methods are also disclosed based on defined potential route parameters with fuzzy logic determination of crisp numerical values for multiple potential routes and comparison of those crisp numerical values for selection of a particular route. Application of the present invention systems and methods to autonomous or driver-less EVs is also disclosed.

Vehicle position data from vehicles on a roadway are received. Affected nodes of the roadway are identified based on the vehicle position data. The roadway graph, representative of the roadway, is updated based on the affected nodes of the roadway. Routes of each vehicle are optimized based on updates to the roadway graph. An indication of change in the route of each vehicle may be provided for display.

An assistance control system performs assistance control for causing a moving object to move to a destination based on map information. The assistance control system includes an electronic control unit. The electronic control unit is configured to generate or update the map information based on input from a sensor mounted on the moving object, acquire a plurality of route candidates to the destination, evaluate certainty of the map information for each location or each section, and calculate a map information evaluation value, evaluate accuracy of the assistance control in the acquired route candidates based on the calculated map information evaluation value, and present a route candidate having the highest priority among the route candidates to an occupant of the moving object, or control the moving object along the route candidate having the highest priority.

Aspects of the disclosure relate to pre-computing routes for autonomous vehicles using map shards. For example, a shard from a plurality of shards of a map may be selected. Each shard including a plurality of nodes and edges connecting pairs of nodes of the plurality of nodes, and each node of the plurality represents a location. A plurality of port nodes for the shard are identified. Each port node has an edge that enters into the selected shard or exists the selected shard. For each port node of the plurality having an edge that enters into the selected shard, optimal routes to each other port node of the plurality having an edge that exits the selected shard may be determined. The optimal routes for the selected shard may be sent to the autonomous vehicles in order to enable the autonomous vehicles to use the optimal routes to determine routes.

The vehicle route prediction apparatus includes a lane recognition unit configured to recognize a type of each of a left lane and a right lane with respect to a driving vehicle, a position recognition unit configured to obtain latitude and longitude coordinates of the driving vehicle, a storage unit configured to store map data, including road information, and a route prediction model, and an arithmetic operation unit configured to check an identifier of an intersection which the driving vehicle intends to enter, based on a position of the driving vehicle and the map data stored in the storage unit, check a position of a lane, based on the type of each of the left lane and the right lane recognized by the lane recognition unit, predict a route of the driving vehicle by using the route prediction model corresponding to an ID of the intersection.