Abstract: The disclosure includes embodiments for forming an on-demand stationary vehicular micro cloud. In some embodiments, a method for a connected vehicle includes receiving event data that describes a set of events configured for triggering a creation of the on-demand stationary vehicular micro cloud. The method includes receiving sensor data that describes a roadway environment. The method includes detecting, based at least in part on the sensor data, an occurrence of at least one event included in the set of events. The method includes modifying an operation of a communication unit of the connected vehicle to initiate the creation of the on-demand stationary vehicular micro cloud to address the occurrence of the at least one event.

Abstract: A method for deploying of a backup roadside unit (RSU) vehicle to provide on-demand RSU services is described. The method may include determining, by a server, a resource specification to deploy a selected backup RSU vehicle to a requested site. The determining of the resource specification may be performed by the server in response to a scene description received from a requestor. The method also includes sending a dispatch instruction to the selected backup RSU vehicle to deploy the selected backup RSU vehicle to the requested site. In this method, a hardware/software capability of the selected backup RSU vehicle is matched to the resource specification determined by the server.

Abstract: Compensation can be made for mismatch in abnormal driving behavior detection between an ego vehicle and its human driver. Sensor data of a surrounding driving environment can be acquired. The acquired sensor data can be analyzed to detect abnormal driving behavior by a vehicle in the surrounding driving environment. Responsive to detecting abnormal driving behavior by the vehicle in the surrounding driving environment, the abnormal driving behavior can be classified as to whether it is severe. If the abnormal driving behavior is classified as not being severe, an amplified version of the abnormal driving behavior can be caused to be presented to the human driver of the ego vehicle.

Abstract: The disclosure includes embodiments for reorganizing autonomous entities to meet a resource demand of a vehicular micro cloud. In some embodiments, a method for a connected vehicle that is a member of the vehicular micro cloud includes modifying an operation of a communication unit of the connected vehicle to receive, via a Vehicle-to-Everything (V2X) network, a reorganization message that includes a reorganization instruction to fulfill the resource demand of the vehicular micro cloud. The method includes executing the reorganization instruction to incorporate one or more resources of an autonomous entity into a resource pool of the vehicular micro cloud so that the resource demand of the vehicular micro cloud is met.

Abstract: Systems include a controller programmed to instruct a sensor to detect an object in an environment of a vehicle in response to an activation of a fall-on-car prevention function, determine a first value of a parameter pertaining to the object at a first time with the sensor, determine a second value of the parameter pertaining to the object at a second time with the sensor, and automatically cause an action to occur based on a comparison of the first value of the parameter and the second value of the parameter.

Abstract: A system includes a processor and a memory in communication with the processor. The memory has instructions that, when executed by the processor, cause the processor to generate, based on an image of a roof of a multi-level parking structure, a roof map having at least one road segment and at least one parking space of the roof. The instructions further cause the processor to predict, based on the roof map, a lower level map having at least one road segment and at least one parking space of a lower level of the parking structure.

Abstract: In accordance with one embodiment of the present disclosure, method includes obtaining multi-level environment data corresponding to a plurality of driving environment levels, encoding the multi-level environment data at each level, extracting features from the multi-level environment data at each encoded level, fusing the extracted features from each encoded level with a spatial-temporal attention framework to generate a fused information embedding, and decoding the fused information embedding to predict driving environment information at one or more driving environment levels.

Abstract: Systems, methods, and other embodiments described herein relate to improving locating favorable entrances. In one embodiment, a method includes acquiring sensor data about a parking facility entrance from at least one vehicle sensor, processing the sensor data into characteristics about the parking facility entrance, storing the characteristics about the parking facility entrance in a map that includes a location of the parking facility entrance, and providing a recommendation for a parking facility based, at least in part, on the map.

Abstract: A method for detecting objects that are likely to fall from vehicle cargo includes obtaining first data of a cargo of a first vehicle captured at a first time and second data of the cargo captured by a second vehicle in an environment of the first vehicle at a second time after the first time, determining a first distance between two key points of the first data or a first shape constructed by key points of the first data and a second distance between two key points of the second data or a second shape constructed by key points of the second data, and detecting a movement of the cargo based on a comparison of the first distance and the second distance or a comparison of the first shape and the second shape.

Abstract: An information processing system includes: a user terminal carried by a user; and an information processing device being capable of communicating with the user terminal. The user terminal includes: a display, and a processor configured to: acquire product information on a product and expiration information on an expiration date of the product; and cause the display to show product inventory information on the user, which is acquired from the information processing device. The information processing device includes a processor configured to: acquire the product information and the expiration information, which are obtained by the user terminal, as product inventory information on the user, when the product has been paid for by the user, and provide the product inventory information on the user, which is acquired by the processor, to the user terminal based on a request from the user.

Abstract: Systems, methods, and vehicles for classifying driving behavior of a target vehicle are provided. The systems include a controller programmed to identify a target vehicle associated with a pseudo-id, obtain a first tag representing first driving behavior of the target vehicle during a first period of time along with the pseudo-id based on first motion data of the target vehicle, obtain a second tag representing second driving behavior of the target vehicle during a second period of time along with the pseudo-id based on second motion data of the target vehicle, the second period of time being after the first period of time, and classify driving behavior of the target vehicle based on interpretation of the first tag and the second tag.

Abstract: A method of operating a traffic management system comprises receiving data from a vehicle indicating a traffic state, estimating a future time at which the vehicle will receive driving instructions transmitted by the traffic management system, predicting a future traffic state at the estimated future time, determining the driving instructions for the vehicle based on the predicted future traffic state, and transmitting the driving instructions to the vehicle.

Abstract: A method for planning lane-change actions considering string stability in a target lane is provided. The method includes learning a car following model based on training data related to a region, obtaining information about vehicles in a target lane in the region, simulating movements of the vehicles in the target lane responsive to an ego vehicle moving into the target lane based on the learned car following model and the information about the vehicles, determining whether the vehicles in the target lane will be string stable responsive to the ego vehicle moving into the target lane based on the simulated movements of the vehicles in the target lane, and instructing the ego vehicle to change lanes from a current lane to the target lane in response to determining that the vehicles in the target lane will be string stable responsive to the ego vehicle moving into the target lane.

Abstract: A method for mitigating lane-change disturbance based on cooperative maneuvering includes obtaining traffic data from a target lane, determining whether a traffic in the target lane becomes string unstable in response to an ego vehicle moving to the target lane based on learned car-following models and the traffic data, identifying a connected vehicle in the target lane in response to determining that the traffic in the target lane becomes string unstable in response to an ego vehicle moving to the target lane, and requesting that the identified connected vehicle change lanes from the target lane to another lane to obtain an empty space between a vehicle in front of the identified connected vehicle and a vehicle behind the identified connected vehicle or follow a suggested speed profile to obtain an empty space between the identified connected vehicle and a vehicle in front of the identified connected vehicle.

Abstract: A method for optimizing a parking spot database is provided including determining a utility score of a candidate route to a candidate parking spot of a plurality of parking spots of a parking spot database based on an availability status of a non-candidate parking spot along the candidate route, and selecting the candidate route from a plurality of candidate routes based on the utility score.

Abstract: The disclosure includes embodiments for a phantom system. A method according to some embodiments is executed by an onboard vehicle computer. The method includes generating a model of a driving behavior of a target vehicle based on sensor data describing the driving behavior and a roadway environment. The method also includes determining a graphic of a phantom vehicle based on the model. The method also includes causing an electronic display of an ego vehicle to depict the graphic as an element of a graphical user interface that depicts the graphic and an image of a remote vehicle that is following the ego vehicle. For example, the method includes generating graphical data describing the graphical user interface including the graphic and the image and providing the graphical data to the electronic display to cause the electronic display to depict the graphical user interface including the graphic and the image.

Abstract: A method for generating a dataset is provided. The method includes generating, within a simulated environment, a simulated image including one or more distortions, the simulated image includes a plurality of vehicles, generating vehicle image patches and ground truth from the simulated image, performing, using a style transfer module, a style-transfer operation on the vehicle image patches, combining the vehicle image patches, on which the style-transfer operation is performed, with a background image of a real-world location, and generating a dataset based on the ground truth and the combination of the vehicle image patches and the background image.

Abstract: A method according to some embodiments includes sensing, by a sensor set of the ego vehicle, a remote vehicle to generate sensor data describing driving behavior of the remote vehicle. The method further includes classifying a type of the remote vehicle based on the sensor data. The method further includes retrieving a behavior profile for the type of the remote vehicle that identifies criteria for the type of the remote vehicle and classifications of abnormal behavior and normal behavior based on the criteria. The method further includes comparing the behavior of the remote vehicle to the behavior profile for the type of the remote vehicle. The method further includes determining that the behavior of the remote vehicle is normal based on the comparing.

Abstract: Systems, methods, and other embodiments described herein relate to improving the performance of a device in different geographic locations by using transfer learning to provide a customized learning model for the different locations. In one embodiment, a method includes receiving segments of a model from separate members in a geographic hierarchy and assembling the segments into the model. The segments include at least a first segment, a second segment, and a third segment. The method includes processing sensor data using the model to provide an output for assisting a device.

Abstract: A method for performing correlation-based parking availability estimation is provided. The method includes retrieving a plurality of parking availability correlation models, wherein each of the parking availability correlation models estimates parking availability for a target parking block based on parking availability for one or more other parking blocks; selecting a subset of the parking availability correlation models, wherein the subset comprises parking availability correlation models that estimate parking availability of the target parking block based on parking availability for one or more other parking blocks for which current parking availability is known; selecting a parking availability correlation model from the subset based on a model quality indicator associated with each parking availability correlation model; and estimating parking availability for the target parking block based on the selected parking availability correlation model.