Abstract: Vehicle forgotten item detection using depth aided image background removal includes determining a first image of an interior of a vehicle for a start of a time-period and a second image for the interior of the vehicle for an end of the time-period. The first image and the second image include a depth metric. One or more elements present in both the first image and the second image are removed from the second image. One or more remaining elements are determined to exist within the second image after removal, which are categorized. An occupant of the vehicle is selectively notified based on the categorization.

Abstract: A visibility grid is determined for at least one external object within an operational environment of an autonomous vehicle. The visibility grid represents areas visible and areas obstructed for the external object. When the autonomous vehicle is positioned within an occluded region relative to the visibility grid of the external object is identified. Driving parameters of the autonomous vehicle are altered in response to being in the occluded region to avoid collision with the external object.

Abstract: System and method for detecting objects outside of a vehicle using object-detection data that is shared between vehicles engaged in searching for the same object. A small language model performs query understanding and object-label extraction and a large-language model, in conjunction with an object detector, detects the object from image data captured by cameras or other sensors of the vehicle. Object-detection data is sent to a remote server or to other vehicles, which respond with their own object-detection data for the same object. The various object-detection data is integrated and provided to a user, for example, by a graphical display.

Abstract: An audio system for a host vehicle includes a plurality of sensors, a infotainment system, and an electronic controller. The plurality of sensors are configured to detect information regarding objects in an area surrounding the host vehicle. The infotainment system includes a first speaker and a second speaker. The electronic controller is configured to play a note through the infotainment system based on the information detected by the plurality of sensors.

Abstract: A method includes collecting data from one or more systems of a vehicle, wherein the vehicle comprises a battery. The method also includes obtaining a navigation map that represents a vehicle transportation network in a computer-interpretable form and determining a route for the vehicle. The method also includes determining a target speed for travel of the vehicle along the route based on the navigation map and determining a state of the vehicle, wherein the state of the vehicle comprises the target speed. The method also includes determining, using a decision-making model, whether to use a first a powertrain control mode or a second powertrain control mode based on the state of the vehicle, and setting the vehicle to use one of the first powertrain control mode or the second powertrain control mode.

Abstract: A vehicle traversing a vehicle transportation network may use a scenario-specific operational control evaluation model instance. The vehicle may receive operational environment data that includes data associated with an object external to the vehicle. The vehicle may determine, based at least in part on the data associated with the object, a final belief associated with an undesired outcome. The vehicle may construct a backward Monte Carlo tree search model based on the final belief and may determine, based on the backward Monte Carlo tree search model, a set of initial beliefs corresponding to a likelihood of leading to the final belief within a timestep threshold. A policy may be trained using the set of initial beliefs. Based on the trained policy, a set of candidate vehicle control actions may be determined, and a vehicle control action may be selected from the set of candidate vehicle control actions.

Abstract: A method of delivering data to a vehicle including transmitting a request for the data from a vehicle to a database. The data stored in the database is tagged with location information. A location of the vehicle is determined. The data in the database tagged with location information within a predetermined distance of the determined location of the vehicle is determined. The data is delivered to the vehicle based on the determined location of the vehicle. The delivered data is displayed on a display disposed in the vehicle.

Abstract: A method includes obtaining a user profile that includes first parameter values associated with a first powertrain control mode and lacks second parameter values associated with a second powertrain control mode. The method also includes identifying a behavior group based on similarity of the user profile to third-party profiles in the behavior group, and updating the user profile based on the third-party profiles to include the second parameter values. The method also includes controlling a vehicle powertrain based on the user profile.

Abstract: Dynamic display of estimated electric vehicle charge times based on predicted battery preconditioning and absent destination route input is described. A method includes identifying one or more charger station locations based on a location of the EV vehicle and a defined distance, determining one or more routes for the one or more charger station locations absent a destination input, determining, for each of the one or more routes, values for a predicted battery state based on vehicle data, determining, for each of the one or more routes, an estimated charge time based on the predicted battery state, an ambient temperature, and charger station information, and providing the one or more charger station locations, the one or more routes, and associated estimated charging times for display to the user.

Abstract: Autonomous vehicle operational management may include an autonomous vehicle traversing a vehicle transportation network, which may include operating a scenario-specific operational control evaluation module instance, which is an instance of a scenario-specific operational control evaluation module instantiated for an occurrence of a distinct vehicle operational scenario by allocating computing resources to, and populating, the scenario-specific operational control evaluation module instance with operational environment information corresponding to the occurrence of the distinct vehicle operational scenario.

Abstract: A system for vehicle decision-making using sequential information probing generates a first world model for a feature of a vehicle operational scenario. The first world model is a copy of a second world model that represents the vehicle operational scenario using incomplete state information for the feature. The first world model represents the vehicle operational scenario using complete state information for the feature. A first expected reward is determined for a sequence of actions of an artificial intelligence (AI) agent using the first world model. A second expected reward is determined for the sequence of actions using the second world model. An expected loss in decision-making performance associated with the incomplete state information for the feature is calculated based on the first and second expected rewards. The expected loss is used to inform a decision-making process for controlling an autonomous vehicle to traverse a portion of a vehicle transportation network.

Abstract: A vehicle includes an on-hoard satellite navigation device, a telematics control unit, a non-transitory computer readable medium, and a processor. The on-board satellite navigation device is in communication with a global positioning system unit to acquire real-time information regarding conditions near the vehicle's vicinity. The telematics control unit is in wireless communications to a cloud services or a vehicle network to upload and receive crowdsourced information regarding conditions near the vehicle's vicinity. The non-transitory computer readable medium stores predetermined information regarding conditions near the vehicle vicinity. The processor is programmed to output one or more complexity values related to the vehicle's vicinity during vehicle travel based on one or more of the real-time information, the crowdsourced information and the predetermined information.

Abstract: A method of displaying road information in a host vehicle includes receiving with the host vehicle the road information acquired by another vehicle. The road information includes information about a portion of a road ahead of a current portion of the road upon which the host vehicle is traveling. An information presentation time is determined at which the road information is configured to be presented in the host vehicle. The road information is presented in the host vehicle at the determined information presentation time.

Abstract: System and method for dynamically refining custom classes using zero-shot image classifiers. The system uses a CLIP model to generate text embeddings of object descriptions and image embeddings of captured images, and determines similarity scores between the text and image embeddings. When a similarity score exceeds a threshold, the system notifies a user that a captured image includes the object, and the system stores the relevant text prompt and captured image as a custom class.

Abstract: A video of a driver of a vehicle is obtained. Based on subset of frames of the video, a series of body poses are identified. The series of body poses are identified by detecting landmark points associated with respective body parts of the driver. The landmark points correspond to coordinates of locations of pixels that represent at least one or more joints of the respective body parts of the driver in the subset of frames. A driver behavior is identified based on the series of the body poses. An assistive vehicle control action for the vehicle is output based on the driver behavior.

Abstract: Vehicle decision-making is analyzed and can be used to modify a decision-making process. For subsets of features comprising a vehicle operational scenario, a first value is generated that quantifies behavior of an artificial intelligence (AI) agent as the AI agent performs a sequence of actions within a first world model based on a complete set of observations for the subset of features. A first world model is a copy of a second world model for sequential decision making. A second value is generated that quantifies behavior of an AI agent as the AI agent performs a sequence of actions in the second world model based on an incomplete set of observations for the subset of features. A difference between the first and second values determines the impact of individual features on the AI agent within the second world model. A decision-making process of the AI agent can be updated.

Abstract: A vehicle traversing a vehicle transportation network may use a scenario-specific operational control evaluation model instance. The vehicle may receive operational environment data that includes data associated with an object external to the vehicle. The vehicle may determine, based at least in part on the data associated with the object, a final belief associated with an undesired outcome. The vehicle may construct a backward Monte Carlo tree search model based on the final belief and may determine, based on the backward Monte Carlo tree search model, a set of initial beliefs corresponding to a likelihood of leading to the final belief within a timestep threshold. A policy may be trained using the set of initial beliefs. Based on the trained policy, a set of candidate vehicle control actions may be determined, and a vehicle control action may be selected from the set of candidate vehicle control actions.

Abstract: System and method for responding to queries about objects in a cabin of a vehicle. The system detects a trigger that causes an in-cabin camera to capture video of the cabin, and the system generates a history of captions for at least selected frames of the video by a large multi-modal model (LMM). The system converts a spoken query received by a microphone to a text-based prompt and generates, by the LMM, a response to the prompt based on the history of captions. The response is converted to speech that is output to a speaker.

Abstract: A method for resolving an exception situation in autonomous driving includes receiving telemetry data from an autonomous vehicle (AV); identifying, using the telemetry data, features and feature values; identifying, using the features and the feature values, a solution to the exception situation; determining a confidence level of the solution; in response to the confidence level exceeding a threshold, transmitting the solution to the AV; and in response to the confidence level not exceeding the threshold, forwarding the solution to a mobility manager, obtaining, from the mobility manager, a validated solution, and transmitting the validated solution to the AV.

Abstract: Systems and methods are provide an incentive for a driver to improve power systems, safety systems, and autonomous driving systems of a vehicle. A method includes determining a learning goal for the vehicle. The method includes generating a request based on the learning goal. The method includes calculating a reward value. The method includes displaying a task on a user interface of the vehicle. The task may be based on the request, the reward value, or both. The method includes obtaining sensor data. The sensor data may be obtained based on an initiation of the task. The method includes determining progress of the task. The method includes transmitting a notification based on a determination that the task is completed.