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公开(公告)号：US11899454B2
公开(公告)日：2024-02-13
申请号：US16695613
申请日：2019-11-26
CPC分类号： G05D1/0088 , B60W60/0011
摘要： 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.
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公开(公告)号：US11714971B2
公开(公告)日：2023-08-01
申请号：US16778890
申请日：2020-01-31
IPC分类号： G06F40/186 , G06F40/56 , G05D1/02
CPC分类号： G05D1/0221 , G05D1/0219 , G06F40/186 , G06F40/56
摘要： 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.
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公开(公告)号：US20220382279A1
公开(公告)日：2022-12-01
申请号：US17333585
申请日：2021-05-28
摘要： 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.
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公开(公告)号：US20220315000A1
公开(公告)日：2022-10-06
申请号：US17218392
申请日：2021-03-31
摘要： 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.
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公开(公告)号：US20210157314A1
公开(公告)日：2021-05-27
申请号：US16695613
申请日：2019-11-26
IPC分类号： G05D1/00
摘要： 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. Traversing the vehicle transportation network includes traversing a portion of the vehicle transportation network in accordance with the selected vehicle control action.
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公开(公告)号：US20200346666A1
公开(公告)日：2020-11-05
申请号：US16757936
申请日：2017-10-31
摘要： 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.
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公开(公告)号：US20200283014A1
公开(公告)日：2020-09-10
申请号：US16652789
申请日：2017-10-30
摘要： Systems and methods for autonomous vehicle control are disclosed herein. According to some implementations, a method includes a scenario-specific operation control evaluation module (SSOCEM) based on a route of the vehicle. The SSOCEM includes a preferred model and one or more fallback models that respectively determine candidate vehicle control actions. The method includes instantiating a SSOCEM instance based on the SSOCEM. The SSOCEM determines a candidate vehicle control action by determining an approximate amount of time needed to determine a solution to the preferred model and determining an approximate amount of time until the upcoming scenario is reached. When the approximate amount of time needed to determine the solution is less than the approximate amount of time to reach the upcoming scenario, the candidate vehicle control action is determined based on the preferred model; otherwise, the candidate vehicle control action is determined based on a fallback model.
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公开(公告)号：US20200159213A1
公开(公告)日：2020-05-21
申请号：US16192356
申请日：2018-11-15
IPC分类号： G05D1/00 , B60W50/029
摘要： Introspective autonomous vehicle operational management includes operating an introspective autonomous vehicle operational management controller including a policy for a model of an introspective autonomous vehicle operational management domain. Operating the controller includes, in response to a determination that a current belief state of the policy indicates an exceptional condition, identifying an exception handler for controlling the autonomous vehicle. Operating the controller includes, in response to a determination that the current belief state indicates an unexceptional condition, identifying a primary handler as the active handler. Operating the controller includes controlling the autonomous vehicle to traverse a current portion of the vehicle transportation network in accordance with the active handler, receiving an indicator output by the active handler, generating an updated belief state based on the indicator, and controlling the autonomous vehicle to traverse a subsequent portion of the vehicle transportation network based on the updated belief state.
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公开(公告)号：US11921506B2
公开(公告)日：2024-03-05
申请号：US17333585
申请日：2021-05-28
CPC分类号： G05D1/0088 , B60W30/18159 , B60W60/0027 , B60W2554/4045
摘要： 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.
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公开(公告)号：US20230382433A1
公开(公告)日：2023-11-30
申请号：US17828815
申请日：2022-05-31
CPC分类号： B60W60/0053 , B60W50/00 , G06N7/005 , B60W2050/0028
摘要： 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.
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