A prediction method and apparatus for an autonomous driving manual takeover, and a system are provided. One example method includes: A first vehicle sends a first message to a second vehicle when detecting that the first vehicle has a manual takeover requirement, where the first message includes information about a first location of the first vehicle, and the information about the first location is used to indicate a location of the first vehicle when the first vehicle detects that the first vehicle has the manual takeover requirement.

A platooning controller, a vehicle system including the same, and a method thereof are provided. The platooning controller includes a processor that identifies information about outside vehicles around a platooning line based on sensing information of platooning vehicles, determines whether views of the outside vehicles are obstructed by the platooning line based on the information about the outside vehicles, controls the platooning vehicles such that the views of the outside vehicles are obtained, and performs collision avoidance control and a storage storing the sensing information or a result of determination of whether a view is obstructed.

Mobile wireless terminals, such as vehicles in traffic, can determine the relative positions of other vehicles with improved precision by arranging to acquire GNSS (global navigational satellite system) signals simultaneously, and then analyzing the various data sets differentially. Simultaneous acquisition can cancel many important errors such as motional errors of the vehicles, atmospheric distortions, and satellite timebase errors. Differential analysis to determine the relative positions of vehicles (as opposed to their overall geographical coordinates) can reduce errors related to satellite ephemeris and velocity, as well as roundoff errors. Localization with a precision of less than 1 meter can greatly improve collision avoidance while discriminating near-miss scenarios from imminent collisions, according to some embodiments. Messaging examples, in 5G and 6G, to manage the simultaneous acquisition and differential analysis, are provided in examples. Many other aspects are disclosed.

A method for modifying a queue of vehicles. In one embodiment, the method includes at least one computer processor determining respective distance values between a first vehicle and one or more adjacent vehicles within a queue of vehicles. The method further includes determining a threshold distance value that corresponds to a distance required to extract the first vehicle from within the queue of vehicles. The method further includes determining a change of position corresponding to at least one adjacent vehicle to the first vehicle within the queue of vehicles based on the determined respective distance values, wherein the determined change in position moves the at least one adjacent vehicle to a distance value from the first vehicle that exceed the threshold distance value. The method further includes transmitting respective requests to the at least one adjacent vehicle to move to the determined change of position.

Disclosed are an apparatus and a method for controlling a lane change using V2V communication information and an apparatus for calculating tendency information for the same. According to the apparatuses and the method, it is possible to improve safety when changing lanes by receiving diving information of drivers of other vehicles from communication modules of the other vehicles, generating tendency information of the drivers of the other vehicles on the basis of the driving information, and performing lane change control using the tendency information.

A system for navigating a vehicle automatically from a current location to a destination location without a human operator is provided. The system of the vehicle includes a global positioning system (GPS) for identifying a vehicle location and a communications system for communicating with a server of a cloud system. The server is configured to identify that the vehicle location is near or at a parking location. The communications system is configured to receive mapping data for the parking location from the server, and the mapping data is at least in part used to find a path at the parking location to avoid a collision of the vehicle with at least one physical object when the vehicle is automatically moved at the parking location. The mapping data is processed by electronics of the vehicle so that when the vehicle is automatically moved collision with the at least one physical object is avoided and the electronics of the vehicle is configured to process a combination of sensor data obtained by sensors of the vehicle. The processing of the sensor data uses image data obtained from one or more cameras and light data obtained from one or more optical sensors.

A three-dimensional data creation method includes: creating first three-dimensional data from information detected by a sensor; receiving encoded three-dimensional data that is obtained by encoding second three-dimensional data; decoding the received encoded three-dimensional data to obtain the second three-dimensional data; and merging the first three-dimensional data with the second three-dimensional data to create third three-dimensional data.

A method for determining a communications scenario corresponding to an action for producing, by a first movable element situated in a traffic lane, a response to an event. The method includes: determining an event in a vicinity of the first movable element, depending on at least one neighbouring element from a list of neighbouring elements positioned in the vicinity; determining a series of actions able to be performed in response to the event, by consulting a lookup table between at least one event and at least one series of actions; for at least one action of the series of actions, determining a communications scenario associated with the action, the determining a scenario including a sub-step of selecting, in the list of elements, for at least one communications scenario message, at least one neighbouring element receiving the message.

Embodiments herein can determine an optimal route for an autonomous electric vehicle. The system may score viable routes between the start and end locations of a trip using a numeric or other scale that denotes how viable the route is for autonomy. The score is adjusted using a variety of factors where a learning process leverages both offline and online data. The scored routes are not based simply on the shortest distance between the start and end points but determine the best route based on the driving context for the vehicle and the user.

One variation of a method for accessing supplemental data from other vehicles includes, at an autonomous vehicle: recording a scan image of a scene around the autonomous vehicle at a first time; detecting insufficient perception data in a region of the scan image; in response to detecting insufficient perception data in the region, defining a ground area of interest containing the region and wirelessly broadcasting a query for perception data representing objects within the ground area of interest; in response to receiving supplemental perception data—representing objects within the ground area of interest detected by the second vehicle at approximately the first time—from a second vehicle proximal the scene, incorporating the supplemental perception data into the scan image to form a composite scan image; selecting a navigational action based on objects in the scene represented by the composite scan image; and autonomously executing the navigational action.