Abstract: Connected vehicles and methods described herein provide for message sender identification. Connected vehicles and methods disclosed herein can include generating a message based hyper-graph based on the positions of connected elements of a road traffic network as communicated to the connected vehicle by the connected elements. Connected vehicles and methods disclosed herein can include generating, a perception based hyper-graph based on the perceived positions of at least a subset of the elements of the road traffic network as determined by a sensor of the vehicle. Connected vehicles and methods disclosed herein can include matching a node of the message-based hyper-graph to a corresponding node of the perception-based hyper-graph to determine the sender of a received message.

Abstract: A method for data sharing between a transmitter vehicle and a receiver vehicle includes obtaining sensor data, encoding the sensor data into structured data having a set of features and a set of channels, generating a channel attention map based on inter-channel relationships of the set of features, generating weights corresponding to channels of the channel attention map, selecting one or more channels among the set of channels based on the weights corresponding to the set of channels, and transmitting the selected channels to the receiver vehicle.

Abstract: Systems and methods for cooperatively managing mixed traffic at an intersection are disclosed herein. One embodiment determines, at an autonomous sensor-rich vehicle, that one or more other vehicles are following in the same lane, the one or more other vehicles including at least one legacy vehicle; communicates with the one or more other vehicles to form a platoon; receives Signal Phase and Timing (SpaT) information from a roadside unit associated with an intersection toward which the platoon is traveling; calculates at the autonomous sensor-rich vehicle, based at least in part on the SPaT information and location information for the platoon, a speed profile and a trajectory for the autonomous sensor-rich vehicle that minimize a delay of the platoon in traversing the intersection while accounting for fuel consumption; and executes the speed profile and the trajectory to control indirectly the one or more other vehicles while the platoon traverses the intersection.

Abstract: A hybrid deterministic override to cloud based probabilistic advanced driver assistance systems. Under default driving conditions, an ego vehicle is controlled by a probabilistic controller in a cloud. An overall gap between the ego vehicle and a leading vehicle is divided into an emergency collision gap and a driver specified gap. The vehicle sensors monitor the overall gap. When the gap between the ego vehicle and the leading vehicle is less than or equal to the emergency collision gap, a deterministic controller of the ego vehicle overrides the cloud based probabilistic controller to control the braking and acceleration of the ego vehicle.

Abstract: A method is provided including receiving a planned route of a vehicle and a request to download content from a cloud server, the planned route traveling through an area covered by a plurality of edge servers, determining a state comprising possible connections between the vehicle and each of the plurality of edge servers at a plurality of time steps during the planned route, inputting the state to a trained model, the model being trained to output an action comprising a partition of the content across the plurality of edge servers that minimizes latency of transmission of the content from the cloud server to the vehicle via the plurality of edge servers, based on the state, and partitioning the content across the plurality of edge servers based on the action out by the trained mode.

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: A server includes a controller programmed to obtain information about a first perception model installed in a vehicle. The controller is further programmed to determine a value of updating the first perception model. The controller is further programmed to determine whether the first perception model needs to be updated to a second perception model based on the value of updating the first perception model. The controller is further programmed to transmit the second perception model to the vehicle in response to determining that the first perception model needs to be updated to the second perception model.

Abstract: Based on a model and an identity of an operator of a vehicle, a setting for a cruise control to maintain a distance between the vehicle and a preceding vehicle can be determined. The model can be based on historical distances maintained by the operator. The model can also be based on information about an environment in which the vehicle is operating or information about a state of the operator. The identity can be received from an interface configured to receive information associated with the identity, a cloud computing platform, or a biometric system configured to determine the identity. The cruise control can be caused to operate according to the setting. A rate of energy consumption by the vehicle when the vehicle is controlled by the cruise control can be less than the rate of energy consumption when the vehicle lacks being controlled by the cruise control.

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: 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: System, methods, and other embodiments described herein relate to a control system to improve estimating motion for an automated vehicle related to cooperative driving. In one embodiment, a method includes monitoring, by an ego vehicle, a communication link for motion data of a target vehicle, used in a model to determine motion, for motion planning by the ego vehicle. The method also includes estimating trajectory of the target vehicle according to a motion estimate by the model when criteria for the communication link are unsatisfied. The method also includes adjusting the motion estimate using a modified model, adapted for speed of the target vehicle, when the criteria for the communication link are satisfied. The method also includes controlling the ego vehicle according to the trajectory and the motion estimate.

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: System, methods, and other embodiments described herein relate to isolating a compartment. In one embodiment, a method includes isolating airflow to an occupant by actuation of a partition in response to a signal indicating a risk and the partition utilizes an inflatable bladder to seal an area of a compartment in a vehicle. The method also includes communicating a precaution associated with the actuation to occupants.

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: 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: 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: 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.