The refueling vehicle routing system can use a refueling vehicle routing application to compute a set of fuel delivery routes for a fleet of refueling vehicles for multiple different target vehicles to be serviced via cooperating with a refueling scenario simulator. The refueling scenario simulator can run scenarios of different sets of refueling routes to refuel target vehicles in light of an available set of assets, and then the application can supply results of how the versions of the set of refueling routes for the fleet of refueling vehicles are going to satisfy anticipated refueling needs for the target vehicles; to indicate any refueling stops that could be late, early, and/or missed entirely, for a service window of time; as well as to show a measure of how cost effective each of the refueling routes are at the satisfying of the needs for the target vehicles.

A mobile apparatus receives a route response including information identifying a starting location and a target location of a route and an encoding data structure encoding the route. The encoding data structure is a probabilistic data structure configured to not provide false negatives. The mobile apparatus uses the information identifying the starting and target locations to identify a decoded origin traversable map element (TME) and a decoded target TME of the mobile version of the digital map for the route; accesses map information for determining a cost value for TMEs of the digital map, wherein a TME that satisfies the encoding data structure is assigned a minimal cost value; determines a decoded route from the decoded starting TME to the decoded target TME based on the cost value assigned to the TMEs using a cost minimization route determination algorithm; and performs at least one navigation function using the decoded route.

Various embodiments of the present application relate to a system architecture that employs vector-based data structures to translate a vector space into at least two dimensions so as to reduce computational expense, and increase operational speed during a search in the vector-space of points of interest along a user's pre-planned route. Further, the vector-based data structures allow for dynamic re-evaluation and re-routing of the user's existing route in substantially real-time, based on user feedback data including distance, location, velocity, and time.

Using a planning circuit of a vehicle, a map is accessed that includes information identifying at least one lane on which vehicles can travel. Using the planning circuit and from the map, a graph representing a driving environment of the vehicle is generated. The graph includes multiple trajectories. At least one trajectory includes a lane change. Each trajectory is a path for the vehicle to move from a first spatiotemporal location to a second spatiotemporal location. The trajectory includes at least one lane alone which the vehicle can move. Using the planning circuit, a trajectory of the multiple trajectories for the vehicle to travel is selected based on an initial vehicle trajectory of the vehicle. The selected trajectory includes a stem. The stem is a portion of the selected trajectory to which the vehicle is configured to adhere. Using the control circuit, the vehicle is moved along the selected trajectory.

Provided are a method, a server, and a computer program for creating a road network map to design a driving plan for an autonomous driving vehicle. A method of creating a road network map to design a driving plan for an autonomous driving vehicle is performed by a computing device and includes: generating road network data for an area; generating lattice road network data for a short-term driving plan for an autonomous driving vehicle using the generated road network data; and generating a crossable dipole graph for a long-term driving plan for the autonomous driving vehicle using the generated lattice road network data.

Embodiments of the present disclosure provide a multipath generation method, an apparatus, a device and a storage medium, and relate to the field of artificial intelligence, in particular to the field of intelligent transportation. A specific implementation solution is: in response to a path generation request, generating M recommended paths from a starting node to a destination node, where the M recommended paths are generated through m path generation processes including: in an i-th path generation process, generating n.sub.i recommended paths based on a constructed search tree, and for each recommended path of the n.sub.i recommended paths, determining traffic costs of road segments of the recommended path in an (i+1)-th path generation process according to penalty factors, the traffic costs being associated with a recommendation priority of path; where m≥i≥1, M>n.sub.i>1.

The disclosed technology provides solutions for optimizing route calculations in autonomous vehicles (AVs). Some aspects of the disclosed technology provide features for determining optimal routes using a node-graph, where edge weights are determined based on AV capability information. A process of the disclosed technology can include steps for: receiving map data specifying two or more routes between a first location and a second location, calculating a first set of cost metrics for two or more routes between the first location and the second location, and selecting a first route for navigation of the AV to the second location. Systems and machine-readable media are also provided.

The present disclosure provides methods, devices and systems for route deviation quantification and vehicular route learning based thereon. In some examples, there is provided a method for route deviation quantification of a suggested route. The method comprises: obtaining a ground truth route based on a filtered trajectory, the filtered trajectory including an inferred location of origin and an inferred location of destination; obtaining a suggested route generated based on the inferred location of origin and the inferred location of destination; quantifying a deviation of the suggested route from the ground truth route by calculating an off course ratio based on a combined length of road segments in the suggested route that are matched to corresponding road segments in the ground truth route and a combined length of road segments in the ground truth route.

System and methods described herein relate to a driving speed matrix and a traffic control point matrix based on breadcrumb data. Standardized routes and times can be determined based on the breadcrumb data, and the standardized routes and times can be mapped onto a plurality of routes. Based on standardized times for routes, routes can be adjusted or optimized.

The present invention provides a method, system, terminal, and storage medium for rapid generation of reference lines. Path planning points are classified according to driving difficulty of different road segments, and the segments with low driving difficulty are assigned reference lines obtained by geometric processing; the segments with high driving difficulty are assigned reference lines obtained by algorithmic processing in combination with vehicle dynamics constraints. Then the reference lines of all segments are combined to form a complete reference line. This method requires little system resource and the algorithm consumes less time.