A computer can execute instructions to: receive, power-receiving vehicle data identifying a power-receiving vehicle; identify one or more power-supplying vehicles for providing charging to the power-receiving vehicle; determine a rank for each of the identified one or more power-supplying vehicles based on the received power-receiving vehicle data and data received from the one or more power-supplying vehicles; upon selecting one of the one or more power-supplying vehicles, provide a navigation instruction to navigate at least one of the power-receiving vehicle and the selected power-supplying vehicle to a charging location based on a power-receiving vehicle location and a selected power-supplying vehicle location; and send access data to the power-receiving vehicle to access a charge port of the selected power-supplying vehicle; send a first light actuation instruction to the power-supplying vehicle based on a charging status; and send a second light actuation instruction to the power-receiving vehicle based on charging status.

Provided is a multi-vehicle collaborative trajectory planning method, apparatus (600) and system, and a device, a storage medium, and a computer program product. The method comprises: determining a specific number of different multi-vehicle priority schemes for multiple vehicles (S101); determining, by using a sequential planning policy, a corresponding collaborative planning scheme for each multi-vehicle priority scheme (S102); performing quality evaluation on each collaborative planning scheme to obtain a quality evaluation result (S103); and according to the quality evaluation result, determining a target collaborative planning scheme from the specific number of collaborative planning schemes (S104).

A method for predicting an ego-lane for a vehicle. The method includes: receiving at least one image captured by at last one camera sensor of the vehicle, which depicts a lane that may be used by a vehicle; ascertaining a center line of the lane, which extends through a center of the lane, by implementing a trained neural network on the captured image, the neural network being trained via regression to ascertain a center line of a lane, which extends in a center of the lane, based on captured images of the lane; outputting a plurality of parameters, which describe the center line of the lane, via the neural network; generating the center line based on the parameters of the center line; identifying the center line of the lane as the ego-lane of the vehicle; and providing the ego-lane.

Informed towing is provided. Towing information is identified, by a towing vehicle, with respect to a towed vehicle to be towed by the towing vehicle. A towed configuration of the towed vehicle is monitored. Responsive to the towed configuration of the towed vehicle being incorrect according to the towing information, a warning is displayed in the HMI indicating the incorrect towing configuration.

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 of unsupervised learning of a metric representation and a corresponding system for a mobile device determines a metric position information for a mobile device from an environmental representation. The mobile device comprises at least one sensor for acquiring sensor data and an odometer system configured to acquire displacement data of the mobile device. An environmental representation is generated based on the acquired sensor data by applying an unsupervised learning algorithm. The mobile device moves along a trajectory and the displacement data and the sensor data are acquired while the mobile device is moving along the trajectory. A set of mapping parameters is calculated based on the environmental representation and the displacement data. A metric position estimation is determined based on a further environmental representation and the calculated set of mapping parameters.

Techniques are provided for autonomous vehicle operation using linear temporal logic. The techniques include using one or more processors of a vehicle to store a linear temporal logic expression defining an operating constraint for operating the vehicle. The vehicle is located at a first spatiotemporal location. The one or more processors are used to receive a second spatiotemporal location for the vehicle. The one or more processors are used to identify a motion segment for operating the vehicle from the first spatiotemporal location to the second spatiotemporal location. The one or more processors are used to determine a value of the linear temporal logic expression based on the motion segment. The one or more processors are used to generate an operational metric for operating the vehicle in accordance with the motion segment based on the determined value of the linear temporal logic expression.

A vehicle navigation apparatus includes a vehicle navigation unit and a route guidance control unit. The vehicle navigation unit includes a route setting unit and a navigation control unit. The route setting unit sets a route to a destination point on the basis of information on a position of a vehicle, information on a destination point, and first map information stored in a storage. The navigation control unit performs guidance on the route and controls the form of displaying the route on a display. The route guidance control unit includes at least one processor determining the driving entity of a vehicle. In a case where the at least one processor determines that the driving entity of the vehicle is the vehicle itself and the vehicle deviates from the route, the at least one processor stops the guidance until the vehicle reaches a next waypoint of the route.

A method and apparatus for recommending a route. The route recommending method includes obtaining user's current body information; obtaining geographic information from a current position to a destination; and determining a recommended route to the destination on the basis of the body information and the geographic information.

Example on-demand driver (ODD) systems and methods are described herein. An example method includes generating, with an ODD system, a softkey for a vehicle associated with an agreement between a driver-in-need (DIN) and an ODD, monitoring, with the ODD system, a location of an ODD device carried by the ODD, and transmitting, with the ODD system, the softkey to the ODD device when the ODD device is detected as being within a proximity of the vehicle. In the example method, the softkey is used to unlock the vehicle.