Abstract: Decision-making for a vehicle uses a data determining interface between perception and decision-making. The interface receives, from at least two data sources, operational environment data representing objects external to the vehicle while the vehicle is traversing a vehicle transportation network. The operational environment data is modified to determine output data for data types needed to determine a candidate vehicle control action responsive to the distinct vehicle operation scenario identified using the operational environment data. The solution is the same candidate vehicle control action for the same conditions regardless of the data types available from the data sources. That is, even where different data sources are available, consistent output is provided to a decision-making system. Consistent decision-making results whether the vehicle has a high-definition map, a standard-definition map, or no map as a data source, for example.

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: Activating an engine of a HEV to charge a battery includes obtaining a first GPS trace from a first trip of the HEV along a first route, where the first GPS trace includes first trace metadata; obtaining a second GPS trace from a second trip, where the second GPS trace includes second trace metadata; adding, to a navigation map, an aggregation of the first trace metadata and the second trace metadata for edges of the navigation map; using the navigation map to obtain an activation action of the engine, where the activation action is selected from a set that includes a first activation action of turning the engine on and a second activation action of turning the engine off; and activating the engine according to the activation action, where activating the engine using the first activation action causes the engine to turn on to charge the battery of the HEV.

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: A group of state-action history entries may be determined from state-action history entries stored in a database. A state-action history entry may represent an experienced operational scenario. A state-action history entry may be associated with a feature. The group of state-action history entries may be determined based on a similarity of the feature. A parameter may be generated based on the group of state-action history entries. The parameter may represent a probability associated with experienced operational scenarios that are similar to one another. A model may be generated based on the parameter. The model may be configured for use in an operational scenario that is similar to the experienced operational scenarios when traversing a portion of the vehicle transportation network.

Abstract: An autonomous vehicle traverses a vehicle transportation network using a multi-objective policy based on a model for specific scenarios. The multi-objective policy includes a topographical map that shows a relationship between at least two objectives. The autonomous vehicle receives a candidate vehicle control action associated with each of the at least two objectives. The autonomous vehicle selects a vehicle control action based on a buffer value that is associated with the at least two objectives. The autonomous vehicle traverses a portion of the vehicle transportation network in accordance with the selected vehicle control action.

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: 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: An apparatus for post-processing of a decision-making model of an autonomous vehicle receives a decision-making model including a plurality of states. The model is processed using multivariate data that comprises values for at least three observations of a vehicle operational scenario. A slice of the model decision space is generated by fixing values of all except two observations, and modifying the values of the two observations to obtain multiple alternative solutions for the model. The alternative solutions and the modified values form the slice. Each alternative solution is associated with a respective first value of a first observation and a respective second value of a second observation. The apparatus also generates a solution to a modified decision-making model that is the model modified by, for at least one state and at least one of the two observations, modifying a probabilistic transition matrix, a probabilistic observation matrix, or both.

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: An occlusion is identified in a vehicle transportation network. A visibility grid is identified on a second side of the occlusion for a vehicle that is on a first side of the occlusion. The visibility grid is identified with respect to a region of interest that is at least a predefined distance above ground. The visibility grid is used to identify first portions of roads sensed by a sensor positioned on the vehicle and second portions of the roads that are not sensed by the sensor. A driving behavior of the vehicle is altered based on the visibility grid.

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: Route planning for a hybrid electric vehicle (HEV) includes obtaining respective engine activation actions for at least some road segments of a route between an origin and a destination by optimizing for at least one of a noise level or energy consumption of an engine of the HEV that is used to charge a battery of the HEV. The HEV is then controlled to follow the at least some of the road segments of the route and to activate the engine according to the respective engine activation actions. Controlling the HEV to follow the at least some of the road segments includes masking at least one of the respective engine activation actions for a current road segment by increasing a volume of an entertainment system of the HEV.

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: A method for planning an activation action for an engine of a vehicle is disclosed. The method includes planning, according to a model, an activation action of an engine of a vehicle, and activating the engine according to the activation action. The model includes a state space comprising a current charge level of the battery and whether the engine is currently on or off. The activation action is selected from a set comprising a first action to turn on the engine to charge the battery and a second action to turn off the engine.

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: Route planning for a hybrid electric vehicle (HEV) includes obtaining a route between an origin and a destination, where the route is optimized for at least one of a noise level or energy consumption of an engine of the HEV that is used to charge a battery of the HEV, and where the route comprises respective engine activation actions for at least some segments of the route; and controlling the HEV to follow the segments of the route and to activate the engine according to the respective engine activation actions.

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.