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: 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: 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: A vehicle includes a vehicle pedal, a vehicle engine and an electronic control unit. The vehicle engine generates a torque output of the vehicle in accordance with an operation of the vehicle pedal. The electronic control unit controls the vehicle pedal between a normal state, an increased resistance state and a decreased resistance state. The vehicle pedal is less sensitive to driver pressure in the increased resistance state. The vehicle pedal is more sensitive to driver pressure in the decreased resistance state.

Abstract: A vehicle includes a user input interface, an electronic display device and a processor. The user input interface is configured to allow a user to input one or more burden conditions of the user. The electronic display device is positioned in an interior compartment of the vehicle. The processor is programmed to control the electronic display device to display one or more route selections, the one or more route selections being based on the burden conditions.

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.

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 vehicle includes an on-board satellite navigation device, a telematics control unit, a non-transitory computer readable medium, an electronic display device, 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 at least one of a cloud services and 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 control the electronic display device to display notification data regarding the vehicle vicinity based on one or more of the real-time information, the crowdsourced information and the predetermined information.

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: 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: 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: 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.