A system for operating an autonomous agent with incomplete environmental information can include and/or interface an autonomous operating system and an autonomous agent. A method for operating an autonomous agent with incomplete environmental information includes any or all of: receiving a set of inputs; determining a set of known objects in the ego vehicle's environment; determining a set of blind regions in the ego vehicle's environment; and inserting a set of virtual objects into the set of blind regions; selecting a set of virtual objects based on the set of blind regions; operating the autonomous agent based on the set of virtual objects; and/or any other suitable processes.

Method and systems for generating vehicle motion planning model simulation scenarios are disclosed. The method receives a base simulation scenario with features of a scene through which a vehicle may travel, defines an interaction zone in the scene, generates an augmentation element that includes an object and a behavior for the object, and adds the augmentation element to the base simulation scenario at the interaction zone to yield an augmented simulation scenario. The augmented simulation scenario is applied to a vehicle motion planning model to train the model.

A method of processing data for a driving automation system, the method comprising steps of: obtaining image data from a camera of an autonomous vehicle, AV; image processing the image data to obtain a vehicle registration mark, VRM, of another vehicle within the surrounding area of the AV; looking up the VRM in a vehicle information database to obtain information indicative of the make, the model and the date of manufacture of the other vehicle; looking up information indicative of the make, the model and the date of manufacture of the other vehicle in a vehicle dimensions database to obtain at least one dimension of the other vehicle; and updating a context of the autonomous vehicle based on said at least one dimension of the other vehicle.

Disclosed is an electronic apparatus for vehicles, including; a processor configured to receive sensor data including an image of the outside of a vehicle, to identify a danger-factor from the sensor data through a first learning model, to learn a danger determination criterion depending on the danger-factor through a second learning model, and, when the danger-factor satisfies the danger determination criterion, to generate a warning signal for warning a user of presence of the danger-factor. One or more of the autonomous vehicle of the present disclosure, a user terminal and a server may be connected to or combined/integrated with an Artificial Intelligence module, an Unmanned Aerial Vehicle (UAV), such as a drone, a robot, an Augmented Reality (AR) apparatus, a virtual reality (VR) apparatus, an apparatus related to 5G service, etc.

A method for transmitting data between a trailer and a road user in a vehicle environment is provided, wherein the data are transmitted according to a V2X standard with low latency via a wireless V2X data connection between a trailer communication module of the trailer and a subscriber communication module of the road user. The wireless data connection is between the trailer communication module and the subscriber communication module, or indirectly via a distribution station. The distribution station forwards the transmitted data directly, wherein the trailer communication module autonomously selects and activates an operating mode depending on the respective road user. As a function of the activated operating mode; trailer data relating to the trailer are selected and autonomously transmitted according to the V2X standard via the wireless V2X data connection and/or subscriber data provided by road users are received and autonomously processed.

The disclosure relates to a vehicle velocity control method. The method includes: determining, by an onboard sensor, drivable distances in different directions in front of a current vehicle, and obtaining, at least based on types of targets in the different directions, an area of a drivable space in front of the current vehicle; determining, based on the area of the drivable space and a current vehicle velocity, a result of a safety degree in a current driving scenario; and controlling the vehicle velocity of the current vehicle based on the result of the safety degree. The disclosure further relates to a vehicle control device, a computer storage medium, and a vehicle.

Disclosed is a method for a vehicle transmitting a signal in a wireless communication system. The method may comprise: receiving information on a road environment; driving a vehicle along a selected path on the basis of the information on the road environment; and, on the basis of satisfying a predetermined condition, transmitting a message for reserving a lane change to a specific lane among at least one lane included in the path. In addition, the message may include information on a virtual vehicle corresponding to the vehicle when in the specific lane. In addition, whether or not the predetermined condition is satisfied may be determined on the basis of: i) the right of way of the vehicle with respect to the lane changing; or ii) a back-off counter.

In various examples, an end-to-end perception evaluation system for autonomous and semi-autonomous machine applications may be implemented to evaluate how the accuracy or precision of outputs of machine learning models—such as deep neural networks (DNNs)—impact downstream performance of the machine when relied upon. For example, decisions computed by the system using ground truth output types may be compared to decisions computed by the system using the perception outputs. As a result, discrepancies in downstream decision making of the system between the ground truth information and the perception information may be evaluated to either aid in updating or retraining of the machine learning model or aid in generating more accurate or precise ground truth information.

A device may receive accelerometer data and video data for a vehicle and may identify bounding boxes and object classes for objects near the vehicle. The device may identify tracks for the objects and may filter out tracks that are not associated with vehicles or vulnerable road users to generate one or more tracks or an indication of no tracks. The device may generate a collision cone identifying a drivable area of the vehicle to identify objects more likely to be involved in a collision and may filter out tracks from the one or more tracks, based on the bounding boxes, and to generate a subset of tracks or another indication of no tracks. The device may determine scores for the subset of tracks and may identify a track of the subset of tracks with a highest score. The device may perform actions based on the identified track.

Systems and methods for motion planning by a vehicle computing system of an autonomous vehicle are provided. The vehicle computing system can input sensor data to a machine-learned system including one or more machine-learned models. The computing system can obtain, as an output of the machine-learned model(s), motion prediction(s) associated with object(s) detected by the system. The system can convert a shape of the object(s) into a probability of occupancy by convolving an occupied area of the object(s) with a continuous uncertainty associated with the object(s). The system can determine a probability of future occupancy of a plurality of locations in the environment at future times based at least in part on the motion prediction(s) and the probability of occupancy of the object(s). The system can provide the motion prediction(s) and the probability of future occupancy of the plurality of locations to a motion planning system of the autonomous vehicle.