Abstract: Tracking perception and/or planning component decisions may comprise generating a data structure in association with an output determined by a component. This data structure, along with one or more data structures generated in association with other outputs generated by the same or different components of the vehicle, may be used to determine a trace that identifies component(s) that determined outputs that affected a particular component's generation of an output. This trace and/or factors identified by the data structure may be used to detect a problem with a component; identify a component or output therefrom that may be causing an error; determine a portion of a component that has been activated and a frequency associated therewith; and/or visualize operation of the vehicle associated with activation of particular components, among additional or alternate uses discussed herein.

Abstract: Techniques for generating a driving surface cost landscape for determining costs for vehicle positions in an environment are described herein. A planning component within a vehicle may determine a non-preferred surface associated with a type of non-preferred area of an environment along a route of a vehicle, determine a preferred surface associated with a preferred area of the environment, determine an adjusted non-preferred surface by removing an overlapping area of the non-preferred surface that overlaps the preferred surface and determine a cost associated with a vehicle position based at least on the preferred surface and the adjusted non-preferred surface. The planning component may then determine a control trajectory for the autonomous vehicle based at least in part on the cost associated with the vehicle position.

Abstract: A vehicle computing system may implement techniques to control a vehicle to avoid collisions between the vehicle and an object in an environment in which a vehicle path and an object path merge. The techniques may include determining an initial merge location associated with the vehicle path and the object path and a final merge location. The final merge location may represent a location at which the vehicle is proximate to and ahead of the object. The vehicle computing system may determine whether the vehicle may proceed to the final merge location and merge with the object without the occurrence of a collision. The vehicle computing system may determine to maintain a vehicle trajectory or modify the vehicle to trajectory to yield to the object based on a determination of whether the vehicle may proceed without the occurrence of the collision.

Abstract: Drive envelope determination is described. In an example, a vehicle can capture sensor data while traversing an environment and can provide the sensor data to computing system(s). The sensor data can indicate agent(s) in the environment and the computing system(s) can determine, based on the sensor data, a planned path through the environment relative to the agent(s). The computing system(s) can also determine lateral distance(s) to the agent(s) from the planned path. In an example, the computing system(s) can determine modified distance(s) based at least in part on the lateral distance(s) and information about the agents. The computing system can determine a drive envelope based on the modified distance(s) and can determine a trajectory in the drive envelope.

Abstract: A vehicle computing system may identify an obstruction along a route of travel and may connect to a service computing device for guidance. The service computing device may include a guidance system configured to receive waypoint and/or orientation input from an operator. The operator may evaluate the scenario and determine one or more waypoints and/or associated orientations for the vehicle to navigate the scenario. In some examples, the guidance system may validate the waypoint(s) and/or associated orientation(s). The service computing device may send the waypoint(s) and/or associated orientation(s) to the vehicle computing system. The vehicle computing system may validate the waypoint(s) and/or associated orientation(s) and, based on the validation, control the vehicle according to the input. Based on a determination that the vehicle has navigated the scenario, the guidance system may release vehicle guidance back to the vehicle computing system.

Abstract: A vehicle computing system may identify an obstruction along a route of travel and may connect to a service computing device for guidance. The service computing device may include a guidance system configured to receive waypoint and/or orientation input from an operator. The operator may evaluate the scenario and determine one or more waypoints and/or associated orientations for the vehicle to navigate the scenario. In some examples, the guidance system may validate the waypoint(s) and/or associated orientation(s). The service computing device may send the waypoint(s) and/or associated orientation(s) to the vehicle computing system. The vehicle computing system may validate the waypoint(s) and/or associated orientation(s) and, based on the validation, control the vehicle according to the input. Based on a determination that the vehicle has navigated the scenario, the guidance system may release vehicle guidance back to the vehicle computing system.

Abstract: Techniques for generating simulations for evaluating a performance of a controller of an autonomous vehicle are described. A computing system may evaluate the performance of the controller to navigate the simulation and respond to actions of one or more objects (e.g., other vehicles, bicyclists, pedestrians, etc.) in a simulation. Actions of the objects in the simulation may be controlled by the computing system (e.g., by an artificial intelligence) and/or one or more users inputting object controls, such as via a user interface. The computing system may calculate performance metrics associated with the actions performed by the vehicle in the simulation as directed by the autonomous controller. The computing system may utilize the performance metrics to verify parameters of the autonomous controller (e.g., validate the autonomous controller) and/or to train the autonomous controller utilizing machine learning techniques to bias toward preferred actions.

Abstract: Techniques for determining a vehicle action and controlling a vehicle to perform the vehicle action for navigating the vehicle in an environment can include determining a vehicle action, such as a lane change action, for a vehicle to perform in an environment. The vehicle can detect, based at least in part on sensor data, an object associated with a target lane associated with the lane change action sensor data. In some instances, the vehicle may determine attribute data associated with the object and input the attribute data to a machine-learned model that can output a yield score. Based on such a yield score, the vehicle may determine whether it is safe to perform the lane change action.

Abstract: A vehicle computing system may identify a scenario in an environment that violates an operating constraint. The vehicle computing system may request remote guidance from a guidance system of a service computing device. The vehicle computing system may receive input from the guidance system including one or more waypoints and/or associated orientations for the vehicle to navigate through the scenario. The vehicle computing system may be configured to validate the input. A validation may include processing the input to determine whether the waypoint(s) and/or orientation(s) associated therewith may cause the vehicle to violate a safety protocol. Based on a determination that the input will not cause the vehicle to violate the safety protocol, the vehicle computing system may control the vehicle according to the input, such as by causing a drive system to operate the vehicle to each waypoint at the associated orientation.

Abstract: Drive envelope determination is described. In an example, a vehicle can capture sensor data while traversing an environment and can provide the sensor data to computing system(s). The sensor data can indicate agent(s) in the environment and the computing system(s) can determine, based on the sensor data, a planned path through the environment relative to the agent(s). The computing system(s) can also determine lateral distance(s) to the agent(s) from the planned path. In an example, the computing system(s) can determine modified distance(s) based at least in part on the lateral distance(s) and information about the agents. The computing system can determine a drive envelope based on the modified distance(s) and can determine a trajectory in the drive envelope.

Abstract: Drive envelope determination is described. In an example, a vehicle can capture sensor data while traversing an environment and can provide the sensor data to computing system(s). The sensor data can indicate agent(s) in the environment and the computing system(s) can determine, based on the sensor data, a planned path through the environment relative to the agent(s). The computing system(s) can also determine lateral distance(s) to the agent(s) from the planned path. In an example, the computing system(s) can determine modified distance(s) based at least in part on the lateral distance(s) and information about the agents. The computing system can determine a drive envelope based on the modified distance(s) and can determine a trajectory in the drive envelope.

Abstract: Techniques for determining a vehicle action and controlling a vehicle to perform the vehicle action for navigating the vehicle in an environment can include determining a vehicle action, such as a lane change action, for a vehicle to perform in an environment. The vehicle can detect, based at least in part on sensor data, an object associated with a target lane associated with the lane change action sensor data. In some instances, the vehicle may determine attribute data associated with the object and input the attribute data to a machine-learned model that can output a yield score. Based on such a yield score, the vehicle may determine whether it is safe to perform the lane change action.

Abstract: A vehicle computing system may identify an obstruction along a route of travel and may connect to a service computing device for guidance. The service computing device may include a guidance system configured to receive waypoint and/or orientation input from an operator. The operator may evaluate the scenario and determine one or more waypoints and/or associated orientations for the vehicle to navigate the scenario. In some examples, the guidance system may validate the waypoint(s) and/or associated orientation(s). The service computing device may send the waypoint(s) and/or associated orientation(s) to the vehicle computing system. The vehicle computing system may validate the waypoint(s) and/or associated orientation(s) and, based on the validation, control the vehicle according to the input. Based on a determination that the vehicle has navigated the scenario, the guidance system may release vehicle guidance back to the vehicle computing system.

Abstract: A vehicle computing system may identify a scenario in an environment that violates an operating constraint. The vehicle computing system may request remote guidance from a guidance system of a service computing device. The vehicle computing system may receive input from the guidance system including one or more waypoints and/or associated orientations for the vehicle to navigate through the scenario. The vehicle computing system may be configured to validate the input. A validation may include processing the input to determine whether the waypoint(s) and/or orientation(s) associated therewith may cause the vehicle to violate a safety protocol. Based on a determination that the input will not cause the vehicle to violate the safety protocol, the vehicle computing system may control the vehicle according to the input, such as by causing a drive system to operate the vehicle to each waypoint at the associated orientation.

Abstract: Drive envelope determination is described. In an example, a vehicle can capture sensor data while traversing an environment and can provide the sensor data to computing system(s). The sensor data can indicate agent(s) in the environment and the computing system(s) can determine, based on the sensor data, a planned path through the environment relative to the agent(s). The computing system(s) can also determine lateral distance(s) to the agent(s) from the planned path. In an example, the computing system(s) can determine modified distance(s) based at least in part on the lateral distance(s) and information about the agents. The computing system can determine a drive envelope based on the modified distance(s) and can determine a trajectory in the drive envelope.

Abstract: Drive envelope determination is described. In an example, a vehicle can capture sensor data while traversing an environment and can provide the sensor data to computing system(s). The sensor data can indicate agent(s) in the environment and the computing system(s) can determine, based on the sensor data, a planned path through the environment relative to the agent(s). The computing system(s) can also determine lateral distance(s) to the agent(s) from the planned path. In an example, the computing system(s) can determine modified distance(s) based at least in part on the lateral distance(s) and information about the agents. The computing system can determine a drive envelope based on the modified distance(s) and can determine a trajectory in the drive envelope.

Abstract: Drive envelope determination is described. In an example, a vehicle can capture sensor data while traversing an environment and can provide the sensor data to computing system(s). The sensor data can indicate agent(s) in the environment and the computing system(s) can determine, based on the sensor data, a planned path through the environment relative to the agent(s). The computing system(s) can also determine lateral distance(s) to the agent(s) from the planned path. In an example, the computing system(s) can determine modified distance(s) based at least in part on the lateral distance(s) and information about the agents. The computing system can determine a drive envelope based on the modified distance(s) and can determine a trajectory in the drive envelope.