Route planning for a hybrid electric vehicle (HEV) includes obtaining respective engine activation actions for at least some road segments of a route between an origin and a destination by optimizing for at least one of a noise level or energy consumption of an engine of the HEV that is used to charge a battery of the HEV. The HEV is then controlled to follow the at least some of the road segments of the route and to activate the engine according to the respective engine activation actions. Controlling the HEV to follow the at least some of the road segments includes masking at least one of the respective engine activation actions for a current road segment by increasing a volume of an entertainment system of the HEV.

A method for: obtaining route request information characterizing a route to be provided to a terminal; determining, at least partially based on the route request information, an intermediate route comprising a plurality of link identifiers respectively identifying links in respective current versions of map regions; determining a final route by replacing, in the intermediate route, link identifiers by respective indications, wherein each indication of the indications respectively indicates whether or not a link or considered-to-be-suited link corresponding to a link, which is identified by the link identifier replaced by the indication and which is contained in a current version of a map region, is considered to be contained in a non-current-version of the map region available to the terminal, wherein the considered-to-be-suited-link is a link that is considered to be suited to be used for a route guiding process at the terminal; and outputting the final route.

A method of controlling a path providing device for a vehicle, where the method includes: receiving high-definition map data from a server; generating forward path information for the vehicle based on the high-definition map data; receiving, from sensors in the vehicle, sensing information related to an object outside the vehicle; and determining a validity of the object based on the forward path information, wherein the validity of the object relates to whether the object is likely to affect driving operations of the vehicle. Generating the forward path information includes: based on a destination having been set for the vehicle, generating the forward path information to include a path to the destination; and based on the destination not having been set for the vehicle, generating the forward path information to include a path on which the vehicle is most likely to travel.

Systems and methods for route mapping with familiar routes include striking a balance between optimal routes from standard navigation systems minimizing time and distance and a mapping component that suggests familiar routes based on a user's route history. New routes including one or more familiar routes may be suggested to the user when they are not too far out of the way or take too long compared to the optimal routes and when they may be preferable or more comfortable.

Examples relate to systems, method and systems, methods and computer programs for efficiently determining an order of driving destinations and for training a machine-learning model using distance-based input data. A system for determining an order of a plurality of driving destinations is configured to obtain information on a distance between the plurality of driving destinations, the distance being defined for a plurality of routes between the plurality of driving destinations, with the routes being defined separately in both directions between each combination of driving destinations of the plurality of driving destinations within the information on the distance. The system is configured to provide the information on the distance between the plurality of driving destinations as input to a machine-learning model, the machine-learning model being trained to output information on an order of the plurality of routes based on the information on the distance provided at the input of the machine-learning model. The system is configured to determine the information on the order of the plurality of driving destinations based on the information on the order of the plurality of routes.

A system includes one or more processors to obtain a transportation event and a transportation event time of a vehicle system at a current location on a route from an origin to a destination. The one or more processors determine transportation event conditions based on historical transportation data and predict, by mathematical optimization methods, optimal transportation routes based on one or more of historical transportation routes, contractual routes, contractual junctions, and station master data. The one or more processors cluster from the historical transportation data, by a machine learning classification method, transportation event data clusters and match at the current location the transportation event data to historical transportation data machine learning classification clusters. The one or more processors predict, by a machine learning model, an estimated time of arrival (ETA) of the vehicle system to the destination.

The present disclosure provides a method comprising identifying at least one of a characteristic and an identity of an item for delivery from an origin to a destination; identifying a plurality of possible routes between the origin and the destination using mapping information, the mapping information including for each of the plurality of possible routes, a characterization of each of a plurality of route segments comprising the possible route; evaluating the plurality of possible routes in view of the identified at least one of the item characteristic and the item identity to select one of the plurality of possible routes; and providing the selected one of the plurality of possible routes to a vehicle, wherein the vehicle delivers the item from the origin to the destination via the identified route.

Disclosed herein are methods for decoding path data. The methods may comprise providing a navigation database for storing topological information of a road network. The navigation database comprises a plurality of road links, and a plurality of link nodes, each link node defining a topological connection between two or more of the road links. The methods may further comprise receiving path data indicative of a path within the road network, wherein the path data comprises a start identifier, a link count, and at least one exit number. The methods may further comprise selecting, from the navigation database, one road link of the plurality of the road links as a start road link of the path based the start identifier. Moreover, the methods may comprise iteratively expanding the path with selected road links until the link count is equal to the number of road links of the path.

An autonomous vehicle can obtain state data associated with an object in an environment, obtain map data including information associated with spatial relationships between at least a subset of lanes of a road network, and determine a set of candidate paths that the object may follow in the environment based at least in part on the spatial relationships between at least two lanes of the road network. Each candidate path can include a respective set of spatial cells. The autonomous vehicle can determine, for each candidate path, a predicted occupancy for each spatial cell of the respective set of spatial cells of such candidate path during at least a portion of a prediction time horizon. The autonomous vehicle can generate prediction data associated with the object based at least in part on the predicted occupancy for each spatial cell of the respective set of spatial cells for at least one candidate path.

Route provisioning techniques for an autonomous driving feature of a vehicle include receiving a user-generated route generated by a user using a second map database that differs from a first map database of the vehicle that stores a road graph specifying nodes and links representative of a map of roads proximate to the vehicle and specifies an array of geo-points between a current geo-location of the vehicle and a final geo-location for the vehicle, determining, using the road graph maintained by the first map database, a set of valid routes from the current geo-location to the final geo-location ordered based on relative likelihood, determining a best route from the set of valid routes based on a weighted comparison between each route of the set of valid routes and the user-generated route, and utilizing the selected route for control of the autonomous driving feature.