Abstract: Centralized shared scenario-specific operational control management includes receiving, at a centralized shared scenario-specific operational control management device, shared scenario-specific operational control management input data, from an autonomous vehicle, validating the shared scenario-specific operational control management input data, identifying a current distinct vehicle operational scenario based on the shared scenario-specific operational control management input data, generating shared scenario-specific operational control management output data based on the current distinct vehicle operational scenario, and transmitting the shared scenario-specific operational control management output data.

Abstract: Autonomous vehicle operational management including blocking monitoring may include traversing, by an autonomous vehicle, a vehicle transportation network. Traversing the vehicle transportation network may include operating a blocking monitor instance, which may include identifying operational environment information including information corresponding to a first external object within a defined distance of the autonomous vehicle, determining a first area of the vehicle transportation network based on a current geospatial location of the autonomous vehicle in the vehicle transportation network and an identified route for the autonomous vehicle, and determining a probability of availability for the first area based on the operational environment information. Traversing the vehicle transportation network may include traversing a portion of the vehicle transportation network based on the probability of availability.

Abstract: A first method includes identifying an occlusion in the vehicle transportation network; identifying, for a first world object that is on a first side of the occlusion, a visibility grid on a second side of the occlusion; and altering a driving behavior of the first vehicle based on the visibility grid. The visibility grid is used in determining whether other world objects exist on the second side of the occlusion. A second includes identifying a first trajectory of a first world object in the vehicle transportation network; identifying a visibility grid of the first world object; identifying, using the visibility grid, a second world object that is invisible to the first world object; and, in response to determining that the first world object is predicted to collide with the second world object, alerting at least one of the first world object or the second world object.

Abstract: Autonomous vehicle operation with explicit occlusion reasoning may include traversing, by a vehicle, a vehicle trans-network. Traversing the vehicle transportation network can include receiving, from a sensor of the vehicle, sensor data for a portion of a vehicle operational environment, determining, using the sensor data, a visibility grid comprising coordinates forming an unobserved region within a defined distance from the vehicle, computing a probability of a presence of an external object within the unobserved region by comparing the visibility grid to a map (e.g., a high-definition map), and traversing a portion of the vehicle transportation network using the probability. An apparatus and a vehicle are also described.

Abstract: Traversing, by an autonomous vehicle, a vehicle transportation network, may include operating a scenario-specific operational control evaluation module instance, wherein the scenario-specific operational control evaluation module instance includes an instance of a scenario-specific operational control evaluation model of a vehicle operational scenario wherein the vehicle operational scenario is a merge vehicle operational scenario or a pass-obstruction vehicle operational scenario, receiving a candidate vehicle control action from the scenario-specific operational control evaluation module instance, and traversing a portion of the vehicle transportation network in accordance with the candidate vehicle control action.

Abstract: A processor is configured to execute instructions stored in a memory to determine, in response to identifying vehicle operational scenarios of a scene, an action for controlling the AV, where the action is from a selected decision component that determined the action based on level of certainty associated with a state factor; generate an explanation as to why the action was selected, such that the explanation includes respective descriptors of the action, the selected decision component, and the state factor; and display the explanation in a graphical view that includes a first graphical indicator of a world object of the selected decision component, a second graphical indicator describing the state factor, and a third graphical indicator describing the action.

Abstract: An apparatus for traversing a vehicle transportation network by an autonomous vehicle (AV) includes a human-autonomy teaming manager. The manager includes a first processor configured to initiate a dialogue associated with a predefined dynamic task sequence that is responsive to a condition experienced by the AV while traversing from a starting location to an ending location within the vehicle transportation network. The sequence is one of a plurality of predefined dynamic task sequences that uses multiple agents to resolve the condition. The manager receives, from an interface accessible to a human agent, an input responsive to the dialogue. The input confirms the sequence as a selected predefined dynamic task sequence or selects an other of the plurality of predefined dynamic task sequences as the selected predefined dynamic task sequence. The manager delegates tasks of the selected predefined dynamic task sequence to resolve the condition.

Abstract: A processor is configured to execute instructions stored in a memory to identify distinct vehicle operational scenarios; instantiate decision components, where each of the decision components is an instance of a respective decision problem, and where the each of the decision components maintains a respective state describing the respective vehicle operational scenario; receive respective candidate vehicle control actions from the decision components; select an action from the respective candidate vehicle control actions, where the action is from a selected decision component of the decision components, and where the action is used to control the AV to traverse a portion of the vehicle transportation network; and generate an explanation as to why the action was selected, where the explanation includes respective descriptors of the action, the selected decision component, and a state factor of the respective state of the selected decision component.

Abstract: A vehicle traversing a vehicle transportation network may use a scenario-specific operational control evaluation model instance. A multi-objective policy for the model is received, wherein the policy includes at least a first objective, a second objective, and a priority of the first objective relative to the second objective. A representation of the policy (e.g., the first objective, the second objective, and the priority) is generated using a user interface. Based on feedback to the user interface, a change to the multi-objective policy for the scenario-specific operational control evaluation model is received. The change is to the first objective, the second objective, the priority, of some combination thereof. Then, for determining a vehicle control action for traversing the vehicle transportation network, an updated multi-objective policy for the scenario-specific operational control evaluation model is generated to include the change to the policy.

Abstract: Traversing a vehicle transportation network includes operating a scenario-specific operational control evaluation module instance. The scenario-specific operational control evaluation module instance includes an instance of a scenario-specific operational control evaluation model of a distinct vehicle operational scenario. Operating the scenario-specific operational control evaluation module instance includes identifying a multi-objective policy for the scenario-specific operational control evaluation model. The multi-objective policy may include a relationship between at least two objectives. Traversing the vehicle transportation network includes receiving a candidate vehicle control action associated with each of the at least two objectives. Traversing the vehicle transportation network includes selecting a vehicle control action based on a buffer value.

Abstract: Methods and vehicles may be configured to gain experience in the form of state-action and/or action-observation histories for an operational scenario as the vehicle traverses a vehicle transportation network. The histories may be incorporated into a model in the form of learning to improve the model over time. The learning may be used to improve integration with human behavior. Driver feedback may be used in the learning examples to improve future performance and to integrate with human behavior. The learning may be used to create customized scenario solutions. The learning may be used to transfer a learned solution and apply the learned solution to a similar scenario.

Abstract: A method for use in traversing a vehicle transportation network by an autonomous vehicle (AV) includes traversing, by the AV, the vehicle transportation network. Traversing the vehicle transportation network includes identifying a distinct vehicle operational scenario; instantiating a first decision component instance; receiving a first set of candidate vehicle control actions from the first decision component instance; selecting an action; and controlling the AV to traverse a portion of the vehicle transportation network based on the action. The first decision component instance is an instance of a first decision component modeling the distinct vehicle operational scenario.

Abstract: Traversing a vehicle transportation network includes operating a scenario-specific operational control evaluation module instance. The scenario-specific operational control evaluation module instance includes an instance of a scenario-specific operational control evaluation model of a distinct vehicle operational scenario. Operating the scenario-specific operational control evaluation module instance includes identifying a multi-objective policy for the scenario-specific operational control evaluation model. The multi-objective policy may include a relationship between at least two objectives. Traversing the vehicle transportation network includes receiving a candidate vehicle control action associated with each of the at least two objectives. Traversing the vehicle transportation network includes selecting a vehicle control action based on a buffer value.

Abstract: Exception handing, such as of obstruction situations, by an autonomous vehicle (AV) is disclosed. A method includes identifying an exception situation; identifying a risk associated with autonomously resolving the exception situation; and in response to the risk exceeding a risk threshold, initiating a request for assistance from a tele-operator, and halting for the tele-operator to respond to the request; and receiving a response from the tele-operator.

Abstract: A first method includes detecting, based on sensor data, an environment state; selecting an action based on the environment state; determining an autonomy level associated with the environment state and the action; and performing the action according to the autonomy level. The autonomy level can be selected based at least on an autonomy model and a feedback model. A second method includes calculating, by solving an extended Stochastic Shortest Path (SSP) problem, a policy for solving a task. The policy can map environment states and autonomy levels to actions and autonomy levels. Calculating the policy can include generating plans that operate across multiple levels of autonomy.

Abstract: Traversing, by an autonomous vehicle, a vehicle transportation network, may include identifying a distinct vehicle operational scenario, wherein traversing the vehicle transportation network includes traversing a portion of the vehicle transportation network that includes the distinct vehicle operational scenario, communicating shared scenario-specific operational control management data associated with the distinct vehicle operational scenario with an external shared scenario-specific operational control management system, operating a scenario-specific operational control evaluation module instance including an instance of a scenario-specific operational control evaluation model of the distinct vehicle operational scenario, and wherein operating the scenario-specific operational control evaluation module instance includes identifying a policy for the scenario-specific operational control evaluation model, receiving a candidate vehicle control action from the policy for the scenario-specific operational contr

Abstract: An automated vehicle system includes an information system, data storage, an electronic controller and a notification device. The information system is configured to receive information related to an environment of the automated vehicle. The data storage has a predetermined socially acceptable behavior parameter stored therein. The electronic controller is configured to compare the information to the predetermined socially acceptable behavior parameter and determine vehicle behavior based on the information. The notification device is configured to present a notification of a course of action based on the determined vehicle behavior.

Abstract: Autonomous vehicle operational management with visual saliency perception control may include operating a perception unit and an autonomous vehicle operational management controller. Operating the perception unit may include generating external object information based on image data received from image capture units of the vehicle and saliency information received from the autonomous vehicle operational management controller.

Abstract: Centralized shared scenario-specific operational control management includes receiving, at a centralized shared scenario-specific operational control management device, shared scenario-specific operational control management input data, from an autonomous vehicle, validating the shared scenario-specific operational control management input data, identifying a current distinct vehicle operational scenario based on the shared scenario-specific operational control management input data, generating shared scenario-specific operational control management output data based on the current distinct vehicle operational scenario, and transmitting the shared scenario-specific operational control management output data.

Abstract: Resolving an exception situation in autonomous driving includes receiving an assistance request to resolve the exception situation from an autonomous vehicle (AV); identifying a solution to the exception situation; forwarding the solution to a tele-operator; receiving a request for playback data from the tele-operator; receiving, from the AV, the playback data; and obtaining, from the tele-operator, a validated solution based on the tele-operator using the playback data. The playback data includes snapshots ni of data related to autonomous driving stored at the AV at respective consecutive times ti, for i=1, . . . , N.