Abstract: The present disclosure is generally directed to media systems configured to receive and play live media content. In particular, methods and systems are provided for a multi-screen content playback experience for time shifted live stream content. Systems and methods are provided herein for generating for display a catch-up video (e.g., a segment of a live stream stored as URLs on a local device during the time the live stream was interrupted) in a picture-in-picture (PIP) window, and generating the PIP catch-up window and the live window for simultaneous display.

Abstract: The disclosure generally includes systems and methods of generating a preferred personalized adaptive cruise control (P-ACC) mode of operation. The method includes capturing vehicle data using one or more internal and external vehicle sensors, and adjusting one or more P-ACC mode of operation parameters, based on captured vehicle data, before operation of the P-ACC mode of operation. Vehicle data includes internal vehicle data comprising a type of passenger and number of passengers in the vehicle.

Abstract: A method for managing moving agents is provided. The method comprises identifying goods agents, moving agents, and a plurality of requests within a predetermined area, generating a nodal graph including the moving agents, requests, and goods as vertices and edges defining relations between two vertices, obtaining actions for the moving agents by inputting the nodal graph to a reinforcement-learning based graphical neural network model stored in the moving agents, the reinforcement-learning based graphical neural network outputs the action for the moving agent in response to receiving the nodal graph, and instructing the moving agents to operate based on the actions to satisfy at least one of the plurality of requests.

Abstract: Methods, vehicles, and systems described herein generate alerts based on a generated macro state level indicative of a traffic hazard risk in view of a set of parameters which may lead driver to make various sub-conscious decisions. The set of parameters can include at least one of a vehicle parameter, driver state parameter, or external parameter.

Abstract: Described herein is a release coating comprising a blend of: (a) a first polymer, wherein the first polymer is a silicone-containing (meth)acrylic polymer; and (b) a second polymer, wherein the second polymer comprises the polymerization reaction product of: (i) a first monomer having an alkyl group with 12 to 24 carbon atoms, a linking group containing a nitrogen or ester group, and a free-radically polymerizable (meth)acryl group; and (ii) a second free-radically polymerizable monomer having less than 12 carbon atoms. Such release coatings can be coated onto a substrate and used as a release coating for pressure sensitive adhesives.

Abstract: Systems, methods, and other embodiments described herein relate to generating a notification about a risk of a target vehicle toppling over due to wind. In one embodiment, a method includes determining a wind force of the wind at a location of the target vehicle and determining one or more characteristics of the target vehicle. The method includes determining whether there is a risk of the target vehicle toppling over based on the wind force of the wind and the one or more characteristics of the target vehicle. The method includes generating a notification about the risk.

Abstract: Bid values submitted for various keywords can take into account the recovery propensity between paid search and organic search. When submitting a bid to a search engine provider for a keyword, an entity may get a certain level of performance in return. If not submitting a bid, however, the entity will likely still get some level of performance, although likely less than for paid search. In order to optimize for a parameter such as impressions, purchases, or profit, the recovery propensity can be taken into account in order to adjust the bid price, taking into account the relative performance of paid and organic search and then optimizing for the determined goal. Organic search data in some embodiments can be obtained through testing or modeling, or a combination thereof.

Abstract: Techniques for detecting erroneous LIDAR data are disclosed herein. One embodiment receives LIDAR point-cloud data pertaining to a robot's environment; receives image data and generates segmented optical-flow data therefrom; fuses, in a 2D grid, a plurality of objects including LIDAR points and optical-flow pixels; executes a hash function that generates, for the plurality of objects, a 1D hash table and an associated index; performs one or more queries using the 1D hash table and the associated index to measure the extent of spatial correspondence between the LIDAR points and the optical-flow pixels; identifies the LIDAR point-cloud data as erroneous, when the extent of spatial correspondence fails to satisfy one or more predetermined criteria; and identifies the LIDAR point-cloud data as valid and controls operation of the robot based, at least in part, on the LIDAR point-cloud data, when the extent of spatial correspondence satisfies the one or more predetermined criteria.

Abstract: In some implementations, a method for providing a decentralized parking fulfillment service may include executing, by a first processor of a connected computing device, a first artificial intelligence network. In addition, the decentralized parking fulfillment service may include executing, by a second processor of a connected vehicle, a second artificial intelligence network. The decentralized parking fulfillment service may include providing the decentralized parking fulfillment service including solving, by the first artificial intelligence network and the second artificial intelligence network, a function that determines a parking space for the connected vehicle. In some implementations, a decentralized parking fulfillment system for providing the decentralized parking fulfillment system includes a parking lot agent stored in a first non-transitory memory of the connected computing device and a vehicle parking agent stored in a second non-transitory memory of the connected vehicle.

Abstract: A method includes receiving features associated with an object to be tracked, receiving an estimated location of the object, determining a region of interest with respect to a first connected vehicle that includes the estimated location of the object, transmitting the features and the region of interest to the first connected vehicle, receiving object data associated with the object from the first connected vehicle, the object data comprising a location of the object, and updating an object track associated with the object based on the object data, the object track comprising the location of the object at a plurality of time steps.

Abstract: Systems and methods for protecting a vehicle at an intersection are disclosed herein. One embodiment detects that the vehicle is stopped at the intersection at one of a first position and a second position; detects that a driver of the vehicle is pressing on an accelerator pedal of the vehicle; delays acceleration of the vehicle automatically by a first predetermined period, when the vehicle has been detected at the first position; and delays acceleration of the vehicle automatically by a second predetermined period, when the vehicle has been detected at the second position. Delaying acceleration of the vehicle automatically prevents the vehicle from being struck by a cross-traffic vehicle that has proceeded through the intersection against a first traffic signal in a red-light state.

Abstract: Systems and methods are provided for effectuating and using a mobility digital twin (MDT) framework including a digital space (where digital twins reside) and a physical space (where physical objects/processes reside). The MDT framework is realized in a cloud-based system/on a cloud platform. Digital twins may represent not only vehicular entities, but human and traffic entities as data/models representative of these different physical objects/processes may be applicable to more than just a directly-related entity. Additionally, the MDT framework is able to leverage data associated with different time horizons (e.g., real-time data as well as historical data).

Abstract: The disclosure includes embodiments for a medic system to respond to a medical condition of a vehicle occupant. A method according to some embodiments is executed by a processor. The method also includes determining, by the processor, that a driver of an ego vehicle is experiencing a debilitating medical condition. The method also includes overriding a protocol to decrease an autonomy level of the ego vehicle responsive to inattentiveness of the driver to a driving interface of the ego vehicle so that the driver can be inattentive to the driving interface and the autonomy level is not decreased. The method also includes modifying an operation of an autonomous driving system of the ego vehicle to increase the autonomy level of the ego vehicle to decrease a driving responsibility of the driver responsive to the debilitating medical condition.

Abstract: Method, systems and apparatuses provides for technology that provides a decentralized network. The technology includes a managing node that generates a list of a plurality of compute nodes that are within a tier. The technology further includes a first compute node providing compute resources for other nodes to utilize. The first compute node conducts a determination that the first compute node is within the tier based at least in part on the compute resources, and sends a notification to the managing node to add the first compute node to the list based on the determination. The technology also includes a client node conducting an identification of the tier based on a compute capacity that is predicted to be utilized to execute one or more tasks associated with the client node. The client node identifies the managing node based on the identification and requests the list from the managing node.

Abstract: Systems and methods for personalizing adaptive cruise control in a vehicle are disclosed herein. One embodiment collects vehicle-following-behavior data associated with a particular driver; trains a Gaussian Process (GP) Regression model using the collected vehicle-following-behavior data to produce a set of adaptive-cruise-control (ACC) parameters pertaining to the particular driver, the set of ACC parameters modeling learned vehicle-following behavior of the particular driver; generates an acceleration command for the vehicle based, at least in part, on the set of ACC parameters; applies a predictive safety filter to the acceleration command to produce a certified acceleration command that has been vetted for safety; and controls acceleration of the vehicle automatically in accordance with the certified acceleration command to regulate a following distance between a lead vehicle and the vehicle in accordance with the learned vehicle-following behavior of the particular driver.

Abstract: An example operation includes one or more of determining, by a vehicle, a charging location, directing, by the vehicle, another vehicle occupying the charging location to maneuver proximate the charging location, and receiving, by the vehicle, charge when the another vehicle maneuvers proximate the charging location.

Abstract: A learning-based lane change prediction algorithm, and systems and methods for implementing the algorithm, are disclosed. The prediction algorithm evaluates the driving behaviors of a target human driver and predicts lane change maneuvers based on those personalized driving behaviors. The algorithm may include an online lane change decision prediction phase and an offline prediction training and cost function recovery phase. During the offline training phase, a machine learning model may be trained based on historical vehicle states. During the online validation phase, driving data may be collected and fed to the trained model to predict a driver's lane change maneuver, identify potential vehicle trajectories, and determine a most probable vehicle trajectory based on a driver's cost function recovered during the offline phase.

Abstract: Driver classification systems and methods are disclosed herein. The driver classification method includes collecting first vehicle driving data from a first vehicle, processing the first vehicle driving data using a driver classification learning model including a machine learning algorithm at one of an edge server and the first vehicle to assign a driver classification to the first vehicle, updating the driver classification learning model based on additional driver classification learning models received from a plurality of additional vehicles, sending the driver classification to an insurance provider, receiving an insurance rate for the first vehicle from the insurance provider based on the driver classification of the first vehicle, and providing the insurance rate to the first vehicle.

Abstract: A system including sensors and a controller for cooperative escape zone detection for a group of vehicles are provided. The sensors obtain driving condition information indicating driving environments and vehicle conditions for the group of vehicles. For each vehicle, the controller determines, based on the driving environment of the vehicle, one or more distances associated with the vehicle that are between the vehicle and one or more obstacles that surround the vehicle and determines an escape zone status for the vehicle based on the one or more distances and the driving environment and the vehicle condition of the vehicle. When the escape zone status of one in the group of vehicles fails to satisfy a pre-defined condition, the controller sends one or more control signals to one or more vehicles in the group of vehicles to create an additional escape zone for the one in the group of vehicles.

Abstract: A method for providing graph data is described. A request for graph data based on a data graph is received, the data graph having i) nodes representing entities associated with an enterprise organization, and ii) edges between nodes representing relationships among the entities. A search embedding corresponding to the request is generated. Embeddings from a set of embeddings that are adjacent to the search embedding are identified, wherein the set of embeddings represent the data graph. Graph data corresponding to the identified embeddings is provided in response to the request.