Abstract: Autonomous vehicle operation with explicit occlusion reasoning may include traversing, by a vehicle, a vehicle transporation 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: A vehicle receives sensor data from at least one of its sensors as it approaches an intersection and determines whether a traffic flow control device for the intersection is detected. When detected, a detected type, a detected state, or both of the traffic flow control device is determined. Using a type of the intersection, at least one of an existing type or an existing state of the traffic flow control device is determined, where the traffic flow control device is undetected or the detected type, the detected state, or both are determined with a detection confidence less than a defined level of detection confidence. The traffic flow control device is tagged with a label including its location and existing type, the existing state, or both within at least one control system for the vehicle. The vehicle is operated within vehicle transportation network using a control system that incorporates the label.

Abstract: A first distinct vehicle operational scenario is identified for an autonomous vehicle (AV). A first set of candidate vehicle control actions are received from a model that provides a first solution to the first distinct vehicle operational scenario. An action is selected from the first set of candidate vehicle control actions. The AV is controlled based on the action. The first solution is obtained offline in a first idealized situation that is decoupled from a current context of the AV.

Abstract: Systems and methods for optimizing traffic flow through an intersection are disclosed. In an embodiment, the method includes receiving positional data indicating a current location of a first vehicle intending to pass through the intersection, receiving directional data indicating an intended direction of the first vehicle through the intersection from the current location, determining, based on the positional data and the directional data, whether an intended path of the first vehicle through the intersection interferes with an alternative path through the intersection, and adjusting a traffic signal at the intersection to decrease an amount of time to pass through the intersection via the alternative path.

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: 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: 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: 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: Systems and methods for adjusting vehicle components based on audible commands are disclosed herein. In an embodiment, the system includes a plurality of adjustable vehicle components, an audio device, and a controller. The audio device is configured to receive an audible command and generate corresponding command data. The controller is programmed to generate at least one confidence score based on the command data and (i) cause a first response to be output from the audio device after determining that the at least one confidence score does not meet a first threshold, (ii) cause a second response to be output from the audio device after determining that the at least one confidence score does not meet a second threshold, and (iii) cause an adjustment of at least one adjustable vehicle component after determining that the at least one confidence score meets the second threshold.

Abstract: Real-time decision-making for a vehicle using belief state determination is described. Operational environment data is received while the vehicle is traversing a vehicle transportation network, where the data includes data associated with an external object. An operational environment monitor establishes an observation that relates the object to a distinct vehicle operation scenario. A belief state model of the monitor computes a belief state for the observation directly from the operational environment data. The monitor provides the computed belief state to a decision component implementing a policy that maps a respective belief state for the object within the distinct vehicle operation scenario to a respective candidate vehicle control action. A candidate vehicle control action is received from the policy of the decision component, and a vehicle control action is selected for traversing the vehicle transportation from any available candidate vehicle control actions.

Abstract: Vehicle guidance with systemic optimization may include traversing, by a current vehicle, a vehicle transportation network, by obtaining, by the current vehicle, systemic-utility vehicle guidance data for a current portion of the vehicle transportation network and traversing, by the current vehicle, the current portion of the vehicle transportation network in accordance with the systemic-utility vehicle guidance data. Obtaining the systemic-utility vehicle guidance data may include obtaining vehicle operational data for a region of a vehicle transportation network, wherein the vehicle operational data includes current operational data for a plurality of vehicles operating in the region, operating a systemic-utility vehicle guidance model for the region, obtaining systemic-utility vehicle guidance data for the region from the systemic-utility vehicle guidance model in response to the vehicle operational data, and outputting the systemic-utility vehicle guidance data to the current vehicle.

Abstract: Autonomous vehicle operational management may include traversing, by an autonomous vehicle, a vehicle transportation network. Traversing the vehicle transportation network may include operating a scenario-specific operational control evaluation module instance, wherein the scenario-specific operational control evaluation module instance is an instance of a scenario-specific operational control evaluation module, wherein the scenario-specific operational control evaluation module implements a partially observable Markov decision process. Traversing the vehicle transportation network may include receiving a candidate vehicle control action from the scenario-specific operational control evaluation module instance, and traversing a portion of the vehicle transportation network based on the candidate vehicle control action.

Abstract: Providing explanations in route planning includes determining a route based on at least two objectives received from a user, where a second objective of the at least two objectives is constrained to within a slack value of a first objective of the at least two objectives; receiving, from the user, a request for an explanation as to an action along the route; and providing the explanation to the user. The explanation describes an extent of violating the slack value.

Abstract: Lane-level route planning includes obtaining lane-level information of a road, where the road includes a first lane and a second lane and the lane-level information includes first lane information related to the first lane and second lane information related to the second lane; converting the lane-level information to probabilities for a state transition function; receiving a destination; and obtaining a policy as a solution to a model that uses the state transition function.

Abstract: Route planning includes receiving a destination, obtaining a lane-level route to the destination using a map, and controlling an autonomous vehicle (AV) to traverse the lane-level route. The lane-level route includes a transition from a first segment of a first lane of a road to a second segment of a second lane of the road.

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.

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 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: 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: A vehicle receives sensor data from at least one of its sensors as it approaches an intersection and determines whether a traffic flow control device for the intersection is detected. When detected, a detected type, a detected state, or both of the traffic flow control device is determined. Using a type of the intersection, at least one of an existing type or an existing state of the traffic flow control device is determined, where the traffic flow control device is undetected or the detected type, the detected state, or both are determined with a detection confidence less than a defined level of detection confidence. The traffic flow control device is tagged with a label including its location and existing type, the existing state, or both within at least one control system for the vehicle. The vehicle is operated within vehicle transportation network using a control system that incorporates the label.