Abstract: Provided are methods and systems for semantic behavior filtering for prediction improvement. A method for operating an autonomous vehicle, is provided. The method includes obtaining, by at least one processor, semantic image data associated with an environment where an autonomous vehicle is operating. The method includes determining, by the at least one processor, a set of agents in the environment based on the semantic image data. The method includes determining a set of predicted actions for at least one agent of the set of agents. The method includes determining, from the set of predicted actions, a set of secondary predicted actions for the at least one primary agent using semantic data. The method includes determining, from the set of predicted actions, a set of primary predicted actions other than secondary predicted actions The method includes generating a path for the autonomous vehicle based on the set of primary predicted actions.

Abstract: Provided are methods and systems for corridor/homotopy scoring and validation. A method for operating an autonomous vehicle, is provided. The method includes obtaining sensor data associated with an environment in which an autonomous vehicle is operating and determining, by the at least one processor, a set of agents in the environment based on the sensor data. The method includes determining, by the at least one processor, a plurality of sets of navigation options for the autonomous vehicle, wherein each set of navigation options includes a first navigation option and a second navigation option, and wherein at least one set of navigation options is associated with at least one agent of the set of agents. The method includes selecting a plurality of navigation options from the plurality of sets of navigation options and generating a corridor for the autonomous vehicle based on the plurality of selected navigation options.

Abstract: Provided are methods and systems for semantic behavior filtering for prediction improvement. A method for operating an autonomous vehicle is provided. The method includes obtaining, by at least one processor, semantic image data associated with an environment in which an autonomous vehicle is operating. The method includes determining, by the at least one processor, at least one agent in the environment. The method includes determining a predicted action for the at least one agent. The method includes determining an agent predicted path for the at least one agent. The method includes determining a vehicle path of the autonomous vehicle. The method includes determining a predicted collision of the at least one agent and the autonomous vehicle. The method includes simulating actions to avoid the predicted collision. The method includes categorizing the predicted collision as a primary predicted collision based on the simulating actions.

Abstract: Provided are methods and systems for semantic behavior filtering for prediction improvement. A method for operating an autonomous vehicle is provided. The method includes obtaining, by at least one processor, semantic image data associated with an environment in which an autonomous vehicle is operating. The method includes determining, by the at least one processor a set of agents in the environment based on the semantic image data. The method includes determining, by the at least one processor, a set of secondary agents from the set of agents based on a relative location of a respective secondary agent to a respective object and a set of object semantic behavior data associated with the respective object. The method includes determining, from the set of agents, a set of primary agents other than secondary agents. The method includes generating a path for the autonomous vehicle based on the set of primary agents.

Abstract: The subject matter described in this specification is generally directed control architectures for an autonomous vehicle. In one example, a reference trajectory, a set of lateral constraints, and a set of speed constraints are received using a control circuit. The control circuit determines a set of steering commands based at least in part on the reference trajectory and the set of lateral constraints and a set of speed commands based at least in part on the set of speed constraints. The vehicle is navigated, using the control circuit, according to the set of steering commands and the set of speed commands.

Abstract: Among other things, we describe techniques for adjusting lateral clearance for a vehicle using a multi-dimensional envelope. A trajectory is generated for the vehicle. A multi-dimensional envelope is generated indicating a drivable region for the vehicle and containing the trajectory. One or more objects are identified located along or adjacent to the trajectory. At least one dimension of the generated multi-dimensional envelope is adjusted to adjust a lateral clearance between the vehicle and the identified one or more objects. A control module of the vehicle navigates the vehicle along the multi-dimensional envelope.

Abstract: Provided are methods for prediction error scenario mining for machine learning methods, which can include determining a prediction error indicative of a difference between a planned decision of an autonomous vehicle and an ideal decision of the autonomous vehicle. The prediction error is associated with an error-prone scenario for which a machine learning model of an autonomous vehicle is to make planned movements. The method includes searching a scenario database for the error-prone scenario based on the prediction error. The scenario database includes a plurality of datasets representative of data received from an autonomous vehicle sensor system in which the plurality of datasets is marked with at least one attribute of the set of attributes. The method further includes obtaining the error-prone scenario from the scenario database for inputting into the machine learning model for training the machine learning model. Systems and computer program products are also provided.

Abstract: The subject matter described in this specification is generally directed control architectures for an autonomous vehicle. In one example, a reference trajectory, a set of lateral constraints, and a set of speed constraints are received using a control circuit. The control circuit determines a set of steering commands based at least in part on the reference trajectory and the set of lateral constraints and a set of speed commands based at least in part on the set of speed constraints. The vehicle is navigated, using the control circuit, according to the set of steering commands and the set of speed commands.

Abstract: Provided are methods for prediction error scenario mining for machine learning methods, which can include determining a prediction error indicative of a difference between a planned decision of an autonomous vehicle and an ideal decision of the autonomous vehicle. The prediction error is associated with an error-prone scenario for which a machine learning model of an autonomous vehicle is to make planned movements. The method includes searching a scenario database for the error-prone scenario based on the prediction error. The scenario database includes a plurality of datasets representative of data received from an autonomous vehicle sensor system in which the plurality of datasets is marked with at least one attribute of the set of attributes. The method further includes obtaining the error-prone scenario from the scenario database for inputting into the machine learning model for training the machine learning model. Systems and computer program products are also provided.

Abstract: Provided are methods for prediction error scenario mining for machine learning methods, which can include determining a prediction error indicative of a difference between a planned decision of an autonomous vehicle and an ideal decision of the autonomous vehicle. The prediction error is associated with an error-prone scenario for which a machine learning model of an autonomous vehicle is to make planned movements. The method includes searching a scenario database for the error-prone scenario based on the prediction error. The scenario database includes a plurality of datasets representative of data received from an autonomous vehicle sensor system in which the plurality of datasets is marked with at least one attribute of the set of attributes. The method further includes obtaining the error-prone scenario from the scenario database for inputting into the machine learning model for training the machine learning model. Systems and computer program products are also provided.

Abstract: Provided are methods for vehicle scenario mining for machine learning methods, which can include determining a set of attributes associated with an untested scenario for which a machine learning model of an autonomous vehicle is to make planned movements. The method includes searching a scenario database for the untested scenario based on the set of attributes. The scenario database includes a plurality of datasets representative of data received from an autonomous vehicle sensor system in which the plurality of datasets is marked with at least one attribute of the set of attributes. The method further includes obtaining the untested scenario from the scenario database for inputting into the machine learning model for training the machine learning model. The machine learning model is configured to make the planned movements for the autonomous vehicle. Systems and computer program products are also provided.

Abstract: Provided are methods for agent prioritization, which can include determining a primary agent set and generating, based on the primary agent set, a trajectory for the autonomous vehicle. Some methods described also include determining an interaction parameter of agents in the environment. Systems and computer program products are also provided.

Abstract: Provided are methods for agent prioritization, which can include determining a primary agent set and generating, based on the primary agent set, a trajectory for the autonomous vehicle. Some methods described also include determining an interaction parameter of agents in the environment. Systems and computer program products are also provided.

Abstract: Provided are methods for agent prioritization, which can include determining a primary agent set and generating, based on the primary agent set, a trajectory for the autonomous vehicle. Some methods described also include determining an interaction parameter of agents in the environment. Systems and computer program products are also provided.

Abstract: In some aspects and/or embodiments, systems, methods, and computer program products described herein include and/or implement technology for encoding dynamic homotopy constraints in spatio-temporal grids, including a method comprising: determining a plurality of dynamic homotopy constraints associated with a scenario involving a vehicle in an environment; embedding the plurality of dynamic homotopy constraints in a plurality of spatio-temporal grids, where each spatio-temporal grid includes individual grids for each timestep of a prediction horizon, generating a plurality of trajectories based on the plurality of dynamic homotopy constraints embedded in the plurality of spatio-temporal grids; selecting a particular trajectory from among the plurality of trajectories generated; and controlling the vehicle in the environment based on the particular trajectory selected from among the plurality of trajectories.

Abstract: Provided are methods for prediction error scenario mining for machine learning methods, which can include determining a prediction error indicative of a difference between a planned decision of an autonomous vehicle and an ideal decision of the autonomous vehicle. The prediction error is associated with an error-prone scenario for which a machine learning model of an autonomous vehicle is to make planned movements. The method includes searching a scenario database for the error-prone scenario based on the prediction error. The scenario database includes a plurality of datasets representative of data received from an autonomous vehicle sensor system in which the plurality of datasets is marked with at least one attribute of the set of attributes. The method further includes obtaining the error-prone scenario from the scenario database for inputting into the machine learning model for training the machine learning model. Systems and computer program products are also provided.

Abstract: Provided are methods for vehicle scenario mining for machine learning methods, which can include determining a set of attributes associated with an untested scenario for which a machine learning model of an autonomous vehicle is to make planned movements. The method includes searching a scenario database for the untested scenario based on the set of attributes. The scenario database includes a plurality of datasets representative of data received from an autonomous vehicle sensor system in which the plurality of datasets is marked with at least one attribute of the set of attributes. The method further includes obtaining the untested scenario from the scenario database for inputting into the machine learning model for training the machine learning model. The machine learning model is configured to make the planned movements for the autonomous vehicle. Systems and computer program products are also provided.

Abstract: The subject matter described in this specification is generally directed to a system and techniques for controlling an autonomous vehicle. In one example, a proximity rule is received by a control circuit. A reference trajectory is received from the planning circuit by a control circuit, where the reference trajectory is determined by the planning circuit based on the proximity rule. The control circuit receives the proximity rule and determines a predicted trajectory based on the reference trajectory and the proximity rule. The autonomous vehicle is then navigated according to the predicted trajectory.

Abstract: Among other things, techniques are described for planning a route for an autonomous vehicle. As an example, a set of candidate constraints for a road segment to be traversed by a vehicle is obtained. A plurality of homotopies are determined, each including a different respective combination of the candidate constraints. For each homotopy, a first prediction of a motion of the vehicle is generated according to a first degree of precision, and a determination is made that the vehicle can traverse the road segment according to a subset of the homotopies. Further, a plurality of trajectories are determined according to the subset of the homotopies, including generating at least one second prediction of the motion of the vehicle according to a second degree of precision greater than the first degree of precision, and selecting one of the trajectories.

Abstract: Enclosed are embodiments for a sampling-based maneuver realizer.