Abstract: Systems and methods described herein relate to generating a preference set for a vehicle occupant, optimizing a coordinated vehicular action based on the preference set, and generating an intent-sharing message based on the coordinated vehicular action. For example, preference sets for vehicle occupants may allow them to optimize coordinated vehicular actions with respect to their preferences regarding energy efficiency and social associations.

Abstract: A system includes a first node, a second node, and a backplane connection communicatively coupling the first node to the second node. The first node includes at least one first Bluetooth controller and stores a first affinity table storing first information for a plurality of Bluetooth devices including a preferred node for each Bluetooth device of the plurality of Bluetooth devices. The second node includes at least one second Bluetooth controller and stores a second affinity table storing second information for the plurality of Bluetooth devices including the preferred node for each Bluetooth device. Each respective node of the first node and the second node is configured to, in response to discovering a previously paired Bluetooth device of the plurality of Bluetooth devices, check the respective affinity table to determine the preferred node for the previously paired Bluetooth device; and connect the previously paired Bluetooth device to the preferred node.

Abstract: Disclosed are techniques to temporarily deactivate or activate the ACL link associated with an isochronous link between a central and a peripheral device of a BLE piconet. The central and peripheral devices may agree to enter into an ACL parked mode after an isochronous link is established between the devices. In the parked mode, the ACL link is in a dormant state without any ACL events. If the central or the peripheral device wants to activate the ACL link to transmit one or more control or data packets, the device may use a CIS subevent on the isochronous link to transmit an LL control PDU. The LL control PDU may contain ACL timing information that specifies the timing of the future ACL event. The device may specify the ACL timing information to avoid or reduce chances of collisions of the future ACL event with CIS packets on the isochronous link.

Abstract: Systems and methods for externally communicating vehicular gaming status are disclosed herein. One embodiment of a gaming-status communication system detects automatically, in a connected vehicle, activation of a gaming mode of the connected vehicle in which a gaming system of the connected vehicle supports at least one of (1) electronic game playing by one or more occupants of the connected vehicle and (2) multi-vehicle networked electronic gaming involving the connected vehicle and one or more other connected vehicles. In response to detecting the activation of the gaming mode, the system activates automatically one or more external human-machine interface (HMI) indicators on the exterior of the connected vehicle to communicate, to other road users, information pertaining to the gaming mode of the connected vehicle.

Abstract: An example operation includes one or more of analyzing a sign proximate a road that a vehicle is traveling on to determine an action to be performed by the vehicle and a level of the action to be performed, determining a query related to the action to be performed when the action to be performed is above a first threshold, responsive to the action being below a second threshold, processing the query to generate a first response, responsive to the action being at or above the second threshold, sending the query to a server, receiving a second response from the server to the query, and autonomously performing the action, by the vehicle, based on at least one of the first response or the second response.

Abstract: An example operation includes one or more of capturing sensor data of a vehicle with at least one sensor installed on the vehicle, determining a baseline resonant frequency of the vehicle in a frequency domain based on the sensor data of the vehicle, capturing additional sensor data of the vehicle with the at least one sensor installed on the vehicle, determining a subsequent resonant frequency of the vehicle in the frequency domain based on the additional sensor data of the vehicle, and alerting at least one of the vehicle and a device associated with the vehicle when the subsequent resonant frequency is different than the baseline resonant frequency.

Abstract: An example operation includes one or more of implementing an Artificial Intelligence (AI) model configured to process incentive and intent messages, forming a platoon comprising a leader vehicle and at least one follower vehicle in proximity to each other with similar position, speed, and direction, implementing on at least one of the leader vehicle or the at least one follower vehicle, an incentive model configured to receive at least one incentive message from a vehicle in the platoon, or an incentive message from the AI model, and responsive to receiving the at least one incentive message, performing at least one of: promoting the at least one follower vehicle to the leader vehicle, demoting the leader vehicle to the at least one follower vehicle, remaining by the leader vehicle in a same position in the platoon, or remaining by the at least one follower vehicle in a same position in the platoon.

Abstract: An example operation includes one or more of processing, by at least one processor on a vehicle, a query related to a sign proximate a road that the vehicle is traveling on using a first artificial intelligence (AI) model deployed on the vehicle to generate contextual information related to the sign, sending, by the at least one processor, the query and the contextual information to a second AI model deployed on a server off the vehicle, processing, by the server, the query and the contextual information using the second AI model, generating, by the server, a response related to the query and the contextual information, sending, by the server, the response to the at least one processor, and providing, by the at least one processor, the response to the vehicle.

Abstract: An example operation includes one or more of receiving sensor data from at least one sensor installed on a vehicle, generating a plurality of sequences of data points corresponding to a plurality of different attributes associated with the vehicle, respectively, based on execution of a reservoir computing model on an iteration of sensor data, determining an attribute associated with the vehicle which is a priority based on execution of an artificial intelligence (AI) model on the plurality of sequences of data points, and adjusting a state of the reservoir computing model to prioritize sensor data of the attribute when generating subsequent sequences of data points

Abstract: A system includes sensors that obtain occupant data of an occupant within the ego vehicle. The ego vehicle is part of a platoon of vehicles. The system includes one or more processors, and a memory storing instructions that, when executed by the one or more processors, cause the system to perform operations. The operations include determining, based on the occupant data, an impairment status of the occupant; and in response to determining the impairment status, selectively implementing an action to address the impairment status. The action includes communicating, using one or more coded status messages, at least a portion of the impairment status to one or more other vehicles in the platoon, and implementing a navigation action of the ego vehicle.

Abstract: Systems and methods are provided for improved/personalized operation of Adaptive Cruise Control (ACC). An example method may comprise: (1) computing an ACC parameter personalized for a driver of a vehicle, wherein the personalized ACC parameter comprises a multi-dimensional vector reflecting preferred following gaps for the driver across different human contexts (e.g., physiological and psychological states of the driver) and environmental contexts (e.g., weather conditions, times of day, types of traffic scenario, types of road); (2) during at least a portion of ACC operation of the vehicle, using the personalized ACC parameter to compute a target following gap for following a second vehicle; and (3) controlling speed of the vehicle in accordance with the target following gap.

Abstract: A system for controlling sensor fusion can include a processor and a memory. The memory can store a sensor data adjustment module, a sensor fusion module, and a communications module. The sensor data adjustment module can include instructions that, when executed by the processor, cause the processor to: (1) calculate reliability scores for sensors and (2) multiply values of data, produced by the sensors, by the reliability scores to produce a scored input value. The sensor fusion module can include instructions that, when executed by the processor, cause the processor to cause the scored input value to be processed by a reservoir computing network in a manner that realizes the sensor fusion. The communications module can include instructions that, when executed by the processor, cause one or more readout values, based on the sensor fusion, to be communicated to an automated vehicle technology system to control movement of a vehicle.

Abstract: An example operation includes one or more of capturing, by a vehicle, internal audible data from an interior of the vehicle and external sensor data from an exterior of the vehicle, identifying, by the vehicle, a type and a tone of the internal audible data, determining that a driver of the vehicle is distracted based on the type and the tone of the internal audible data, determining, by the vehicle, that a dangerous situation related to the vehicle exists based on the external sensor data, and generating an alert within the vehicle based on the driver being distracted and the dangerous situation related to the vehicle.

Abstract: An example operation includes one or more of receiving eye movement data of an occupant in a vehicle and visual data of an oncoming object on a path toward the vehicle, generating eye movement tokens from the eye movement data and visual tokens from the visual data, executing at least one artificial intelligence (AI) model on the eye movement tokens and the visual tokens to identify data of a dangerous situation related to the vehicle, generating a recommendation to avoid the dangerous situation based on the data of the dangerous situation output by the AI model, and generating an alert within the vehicle based on the recommendation.

Abstract: Systems, methods, and other embodiments described herein relate to real-time risk reasoning within a vehicle. In one embodiment, a method includes acquiring sensor data about a surrounding environment of a vehicle. The method includes, responsive to identifying a risk to the vehicle from the sensor data, merging tokens that are features extracted from the sensor data into a condensed representation of a context of the surrounding environment associated with the risk. The method includes providing a driving recommendation according to the risk.

Abstract: Systems and methods described herein relate to receiving intended position data and actual position data regarding a vehicle, generating sensed position data regarding the vehicle, determining a weighting function based on an intended position loss and a sensed position loss, and determining a weighted position of the vehicle by applying the weighting function to the intended position data and the sensed position data.

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: Systems, methods, and other embodiments described herein relate to forming and managing a platoon or convoy of unmanned aerial vehicles (UAVs). In one embodiment, a method includes receiving, from a plurality of UAVs. The intent messages indicate an air corridor and planned path for a respective UAV. The method also includes grouping, based on the air corridor and planned path in multiple intent messages, a set of UAVs into a platoon and generating a coordinated flight path and coordinated flight parameters for the platoon. The method also includes flying the set of UAVs based on the coordinated flight path and the coordinated flight parameters.

Abstract: A system and method for online learning adaptive cruise control (ACC) for driving a vehicle includes one or more non-transitory memories storing an ACC model pool comprising one or more ACC models, and one or more processors. The one or more processors operably causes the system to generate, using a trained meta algorithm and based on historical user inputs and user preferences of a user, one or more personalized ACC models associated with the user, and include the one or more personalized ACC models in the ACC model pool, determine whether the user triggers a real-time ACC disengagement, in response to determining that the user triggers the real-time ACC disengagement, select, based on real-time user inputs, a predicted ACC model from the ACC model pool including the one or more personalized ACC models, and engage the vehicle with the predicted ACC model.

Abstract: Systems and methods are provided for authenticating vehicle communications. The system can identify, by a roadway device, a vehicle traveling on a roadway and authenticate a proxy device associated with the vehicle. Sensor data can be transmitted to the vehicle based on the authentication of the proxy device, wherein the vehicle is operated to navigate the roadway based on the sensor data.