A method for localizing a vehicle comprises transmitting first position data related to a first position of the vehicle at a first point in time from the vehicle to a server. The server computes second position data related to the first position of the vehicle at the first point in time based on the received first position data. The server transmits the second position data from the server to the vehicle. The vehicle computes third position data related to a second position of the vehicle at a second point in time based on the received second position data. The second point in time is later than the first point in time.

A method for localizing a vehicle comprises transmitting first position data related to a first position of the vehicle at a first point in time from the vehicle to a server. The server computes second position data related to the first position of the vehicle at the first point in time based on the received first position data. The server transmits the second position data from the server to the vehicle. The vehicle computes third position data related to a second position of the vehicle at a second point in time based on the received second position data. The second point in time is later than the first point in time.

An operational system of the present disclosure is an operational system for a plurality of fuel cell electric vehicles. The operational system is equipped with a decision unit that decides a single operational route based on supply amounts of hydrogen available from a plurality of hydrogen stations respectively, from among a plurality of candidates of an operational route of each of the fuel cell electric vehicles, and a scheduling unit that schedules the filling of each of the fuel cell electric vehicles with hydrogen at the hydrogen station or hydrogen stations included on the decided operational route. The decision unit decides the new operational route based on post-change available supply amounts of hydrogen in the case where the supply amounts of hydrogen available from the respective hydrogen stations change when each of the fuel cell electric vehicles runs on the decided operational route.

A method, system and computer program product for determining a map position of an ego-vehicle are disclosed. The method includes acquiring map data comprising a road geometry, initializing at least one dynamic landmark by measuring a position and velocity, relative to the ego-vehicle, of a surrounding vehicle, and determining a first map position of the surrounding vehicle based on this measurement and the geographical position of the ego-vehicle. Further, the method includes predicting a second map position of the surrounding vehicle, and measuring a location, relative to the ego-vehicle, of the surrounding vehicle when it is estimated to be at the second map position, whereby the geographical position of the ego-vehicle can be computed and updated.

A method, system and computer program product for determining a map position of an ego-vehicle are disclosed. The method includes acquiring map data comprising a road geometry, initializing at least one dynamic landmark by measuring a position and velocity, relative to the ego-vehicle, of a surrounding vehicle, and determining a first map position of the surrounding vehicle based on this measurement and the geographical position of the ego-vehicle. Further, the method includes predicting a second map position of the surrounding vehicle, and measuring a location, relative to the ego-vehicle, of the surrounding vehicle when it is estimated to be at the second map position, whereby the geographical position of the ego-vehicle can be computed and updated.

A control unit 15 of an in-vehicle device 1 configured to acquire, from landmark data LD that is map data including position information of one or more features, plural pieces of position information of a feature which is drawn on a road surface and which exists at or around a vehicle. Then, the control unit 15 is configured to calculate a normal vector of an approximate plane calculated based on the acquired plural pieces of the position information. Then, the control unit 15 is configured to calculate at least one of a pitch angle or a roll angle of the vehicle based on the orientation of the vehicle and the normal vector.

A control unit 15 of an in-vehicle device 1 configured to acquire, from landmark data LD that is map data including position information of one or more features, plural pieces of position information of a feature which is drawn on a road surface and which exists at or around a vehicle. Then, the control unit 15 is configured to calculate a normal vector of an approximate plane calculated based on the acquired plural pieces of the position information. Then, the control unit 15 is configured to calculate at least one of a pitch angle or a roll angle of the vehicle based on the orientation of the vehicle and the normal vector.

In one embodiment, a method for extracting point cloud features for use in localizing an autonomous driving vehicle (ADV) includes selecting a first set of keypoints from an online point cloud, the online point cloud generated by a LiDAR device on the ADV for a predicted pose of the ADV; and extracting a first set of feature descriptors from the first set of keypoints using a feature learning neural network running on the ADV, The method further includes locating a second set of keypoints on a pre-built point cloud map, each keypoint of the second set of keypoints corresponding to a keypoint of the first set of keypoint; extracting a second set of feature descriptors from the pre-built point cloud map; and estimating a position and orientation of the ADV based on the first set of feature descriptors, the second set of feature descriptors, and a predicted pose of the ADV.

A motor vehicle has a control device, a first sensor and a global positioning device. The control device has a control unit and a data memory. At least one item of information about an arrangement of an overhead line is stored on the data memory. The control unit is connected to the first sensor and to the global positioning device. The first sensor determines a position of the overhead line relative to the motor vehicle and provides the control unit with the relative position. The global positioning device determines a global position of the motor vehicle and provides the control unit with it. The control unit, on the basis of the established relative position, the established global position and the information about the arrangement of the overhead line, calculates a position of the motor vehicle.

Techniques are disclosed for collaborative map construction using multiple vehicles. Such a system may include a ground vehicle including a first computing device and a first scanning sensor, and an aerial vehicle including a second computing device and a second scanning sensor. The ground vehicle can obtain a first real-time map based on first scanning data using the first scanning sensor, and transmit a first real-time map and position information to the aerial vehicle. The aerial vehicle can receive the first real-time map and the position information from the first computing device, obtain a second real-time map based on second scanning data collected using the second scanning sensor, and obtain a third real-time map based on the first real-time map and the second real-time map.