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 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.

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.

Provided is a navigation system for a leader vehicle leading follower vehicles, including: the leader vehicle, configured to transmit, real-time movement data to follower vehicles; and, the follower vehicles, each comprising: a signal receiver for receiving the data from the leader vehicle; sensors configured to detect at least one maneuverability condition; a memory; a vehicle maneuver controller; a distance sensor; and a processor configured to: determine a route for navigating the local follower vehicle from an initial location; determine a preferred range of distances from the vehicle in front of the respective follower vehicle that the respective follower vehicle should stay within; determine a set of active maneuvering instructions for the respective follower vehicle based on at least a portion of the data received from the guiding vehicle; determine a lag in control commands; and, execute the set of active maneuvering instructions in the respective follower vehicle.

An example operation includes one or more of determining a characteristic of an occupant in a transport and a current driving environment of the transport, wherein the characteristic includes a position of the occupant, determining that the current driving environment will lead to a collision of the transport, and based on a predicted result of the collision, sending a predicted state of the occupant based on the position, to an emergency service node.

A method for a vehicle, in particular a vehicle which is operated in an at least partially automated manner, for detecting an impact event. The method includes of: a. developing a driving environment model for the vehicle as a function of first sensor signals from at least one driving environment sensor system of the vehicle; b. using the driving environment model to determine a probability of contacting an object; c. opening a measurement window for second signals of a contact sensor system as a function of the determined contact probability; d. detecting an impact event as a function of the second sensor signals, in particular within the measurement window.

System for automatically recognising anomalous situations along roads travelled by motor-vehicles for intelligent motor-vehicle driving speed control along roads.

The motor-vehicles are configured to transmit data allowing anomalous situations to be recognised along roads travelled by the motor-vehicles.

The system comprises data processing resources configured to:

receive and process data transmitted by the motor-vehicles to recognise anomalous situations along the roads travelled by the motor-vehicles based on a recognition algorithm,

when anomalous situations are recognised along roads travelled by the motor-vehicles, generate associated alert events and compute reference driving speeds along the roads recognised to be affected by anomalous situations, and

transmit data representative of the alert events and of the reference driving speeds along the roads recognised to be affected by anomalous situations.

The motor-vehicles are further configured to:

receive data representative of alert events and reference driving speeds, and

use the received data to implement one or both of the following actions: inform the drivers of motor-vehicles, through automotive user interfaces of motor-vehicles of the anomalous situations recognised along roads travelled by motor-vehicles, and cause current driving speeds of the motor-vehicles to be adjusted to the reference driving speeds along roads recognised to be affected by anomalous situations.

A method of controlling an autonomous vehicle and an electronic device are provided, which relate to a field of artificial intelligence technology, and in particular to a field of autonomous driving technology. The method includes: acquiring a vehicle state configuration information from a cloud; enabling a task receiving function of the vehicle, in response to the vehicle state configuration information indicating that the vehicle is in an automatic operation state; acquiring a task information from the cloud in response to the task receiving function being enabled; and controlling the vehicle according to the task information.

A controller of an information processing apparatus acquires, in a case in which a previous user of a vehicle has not given consent to disclose history data for the vehicle, information indicating whether a current user of the vehicle has given consent to disclose the history data for the vehicle, and determines to grant the current user an incentive in a case in which it is determined, based on the acquired information, that the current user has given consent to disclose the history data for the vehicle.

Methods and systems for distributed detection of road conditions and damage are described. In one embodiment, a method for distributed detection of road conditions and damage is provided. The method includes receiving, from one or more mobile devices, a plurality of reports of anomalies associated with roads in a geographic area. The method also includes storing the received reports of anomalies in a database and comparing each report of an anomaly to stored reports of previous anomalies in the database. The method further includes determining whether each report of an anomaly indicates road damage or a temporary problem. The method includes generating a prioritized list of locations of anomalies associated with one or more roads that have been determined to have road damage that needs maintenance and/or repair.