A system for associating perceived and mapped lane edges can include a processor and a memory. The memory includes instructions such that the processor is configured to receive a sensor data representing a perceived object; receive map data representing a map object; determine a cost matrix a cost matrix indicative of an association cost for associating the map object to the perceived object; compare the association cost with an association cost threshold; and associate the perceived object with the map object based on the association cost.

Systems and methods for an On-Demand Autonomy (ODA) service. The system includes a selection module of a leader vehicle (Lv) connected to an ODA server to determine whether to confirm a request for an on-demand autonomy (ODA) service which has been broadcast wherein the ODA service request includes control of a follower vehicle (Fv) to a requested location by creating a virtual link between the Lv and the Fv to configure a vehicle platoon to enable transport of the Fv by the Lv wherein the vehicle platoon is a linking of the Lv to the Fv via the virtual link to enable the Lv to assume the control of the Fv to the requested location.

A method for operating at least one automated vehicle, including the steps: detecting road users by sensors with the aid of the at least one automated vehicle and/or with the aid of sensor systems in an infrastructure; ascertaining predicted traffic routes for the road users with the aid of a computing device based on defined criteria; transmitting control data corresponding to the predicted traffic route to the automated vehicle; and operating the automated vehicle according to the control data.

A method for controlling in an automated manner a motor vehicle (10) traveling on a road (12) in a current lane (14) is suggested, wherein the road (12) has at least one further lane (16). The method comprises the following steps: At least two preliminary driving maneuvers are generated and/or received, which include a lane change from the current lane (14) to the at least one further lane (16) and a starting time of the lane change. The starting times of the at least two preliminary driving maneuvers are at different times. The at least two driving maneuvers are compared taking into account the respective starting times. One of the starting times is selected based on the comparison. Further, a control device for a system for controlling a motor vehicle is also suggested.

The present application discloses a method, device and system for automatic oiling of a long-distance transport vehicle. The method provided by the present application includes: obtaining, by a transport planning system at a network side, vehicle status information, transport mission information and highway port information of the vehicle before the long-distance transport vehicle starts from a highway port of departure; generating a transport plan according to the vehicle status information, the transport mission information and the highway port information, wherein the transport plan comprises at least one highway port to be stopped at, cargo quantity to be loaded at each highway port to be stopped at and oil mass to be filled at each highway port to be stopped at; and sending the transport plan to the vehicle.

According to one aspect, systems and techniques for lane selection may include receiving a current state of an ego vehicle and a traffic participant vehicle, and a goal position, projecting the ego vehicle and the traffic participant vehicle onto a graph network, where nodes of the graph network may be indicative of discretized space within an operating environment, determining a current node for the ego vehicle within the graph network, and determining a subsequent node for the ego vehicle based on identifying adjacent nodes which may be adjacent to the current node, calculating travel times associated with each of the adjacent nodes, calculating step costs associated with each of the adjacent nodes, calculating heuristic costs associated with each of the adjacent nodes, and predicting a position of the traffic participant vehicle.

An information processing device (20) includes a route acquisition unit (202) and an automatic driving section determination unit (204). The route acquisition unit (202) acquires route information indicating a moving route of a mobile body. The automatic driving section determination unit (204) acquires adaptation coefficients for a plurality of sections included in the moving route indicated by the route information, with reference to an adaptation coefficient storage unit (206) that stores automatic driving adaptation coefficients set for the respective sections. Further, the automatic driving section determination unit (204) determines the automatic driving sections of the mobile body in the moving route, based on the acquired adaptation coefficients.

Aspects of the disclosure relate to distributing vehicles to perform scouting. This may involve receiving a request for a scouting objective for a vehicle, and in response, identifying a set of scouting objectives that the vehicle is eligible to visit. Each scouting objective of the set is associated with one or more scouting quests, and each scouting quest is associated with a plurality of scouting objectives. For each given scouting objective in the set of scouting objectives, an overall weight may be determined using combined weights for the given scouting objective and any scouting quests with which the given scouting objective is associated. One or more scouting objectives of the set of scouting objectives may be selected using the determined overall weights. The one or more selected scouting objectives may be provided to the vehicle.

Techniques to predict object behavior in an environment are discussed herein. For example, such techniques may include determining a trajectory of the object, determining an intent of the trajectory, and sending the trajectory and the intent to a vehicle computing system to control an autonomous vehicle. The vehicle computing system may implement a machine learned model to process data such as sensor data and map data. The machine learned model can associate different intentions of an object in an environment with different trajectories. A vehicle, such as an autonomous vehicle, can be controlled to traverse an environment based on object's intentions and trajectories.

A method for generating navigation routes for a vehicle includes: receiving an origin position, in a road network, of the vehicle; receiving a destination position, in the road network; receiving a user preference from a user interface device, the user preference indicating a tradeoff value having one of a plurality of values between travel time and energy efficiency; computing a time value for each of a plurality of road segments of the road network using a time consumption model; computing an energy consumption for each of the plurality of road segments of the road network using an energy consumption model; identifying a weighted route from the origin position to the destination position based on the time value and the energy consumption for the road segments and based on the user preference; and supplying the identified weighted route to a navigation system of the vehicle.