Abstract: This application relates to techniques for determining whether to engage an autonomous controller of a vehicle based on previously recorded data. A computing system may receive, from a vehicle computing system, data representative of a vehicle being operated in an environment, such as by an autonomous controller. The computing system may generate a simulation associated with the vehicle operation and configured to test an updated autonomous controller. The computing system may determine one or more first time periods associated with the vehicle operations that satisfy one or more conditions associated with engaging an autonomous controller and one or more second time periods associated with the vehicle operations that fail to satisfy the one or more conditions. The computing system may enable an engagement of the autonomous controller during the one or more first time periods and disable the engagement during the one or more second time periods.

Abstract: Techniques for compensating for errors in position of a vehicle are discussed herein. In some cases, a discrepancy may exist between a measured state of the vehicle and a desired state as determined by a system of the vehicle. Techniques and methods for a planning architecture of an autonomous vehicle that is able to provide maintain a smooth trajectory as the vehicle follows a planned path or route. In some cases, a planning architecture of the autonomous vehicle may compensate for differences between an estimated state and a planned path without the use of a separate system. In this example process, the planning architecture may include a mission planning system, a decision system, and a tracking system that together output a trajectory for a drive system.

Abstract: Techniques for generating trajectories and drivable areas for navigating a vehicle in an environment are discussed herein. The techniques can include receiving a trajectory representing an initial trajectory for a vehicle, such as an autonomous vehicle, to traverse the environment in a drivable area. A location can be determined along the trajectory. A cost associated with the location can determined and can be evaluated with respect to a cost threshold. Further, the techniques can include determining, based at least in part on the cost meeting or exceeding the cost threshold, an action associated with the location, and controlling the autonomous vehicle to traverse the environment based at least in part on the action.

Abstract: Acceleration determination for controlling a vehicle, such as an autonomous vehicle, is described. In an example, objects in an environment of the vehicle are identified and a probability that each object will impact travel of the vehicle is determined. Individual accelerations for responding to each object may also be determined. Weighting factors for each of the accelerations may also be determined based on the probabilities. A control acceleration may be determined based on the weighting factors and the accelerations.

Abstract: This application relates to techniques for dynamically determining whether to engage an autonomous controller of a vehicle. A computing system may receive a request to engage the autonomous controller (e.g., autonomous mode) of the vehicle. In some examples, the request may be received from a simulation computing system configured to test an updated autonomous controller in a simulation. Based on a determination that conditions associated with engaging autonomy are satisfied, the computing system engages the autonomous controller. Based on a determination that conditions associated with engaging autonomy are not satisfied, the computing system disables the engagement of the autonomous controller such that the vehicle is controlled according to an initial operational mode (e.g., manual mode, semi-autonomous mode, previous version of the autonomous controller, etc.).

Abstract: A vehicle can include various sensors to detect objects in an environment. In some cases, the object may be within a planned path of travel of the vehicle. In these cases, leaving the planned path may be dangerous to the passengers so the vehicle may, based on dimensions of the object, dimensions of the vehicle, and semantic information of the object, determine operational parameters associate with passing the object while maintaining a position within the planned path, if possible.

Abstract: The techniques discussed herein may comprise an autonomous vehicle guidance system that generates a path for controlling an autonomous vehicle based at least in part on a static object map and/or one or more dynamic object maps. The guidance system may identify a path based at least in part on determining set of nodes and a cost map associated with the static and/or dynamic object, among other costs, pruning the set of nodes, and creating further nodes from the remaining nodes until a computational or other limit is reached. The path output by the techniques may be associated with a cheapest node of the sets of nodes that were generated.

Abstract: A vehicle can include various sensors to detect objects in an environment. In some cases, the object may be within a planned path of travel of the vehicle. In these cases, leaving the planned path may be dangerous to the passengers so the vehicle may, based on dimensions of the object, dimensions of the vehicle, and semantic information of the object, determine operational parameters associate with passing the object while maintaining a position within the planned path, if possible.

Abstract: Techniques for determining vehicle trajectories to operate a vehicle according to a planned path are described herein. In an example, a vehicle computing system may determine a location of the vehicle at a first time. Based on the location, the vehicle computing system may determine an estimated location of the vehicle at a second time, the estimated location of the vehicle including a lateral coordinate and a longitudinal coordinate. The vehicle computing system may determine the longitudinal coordinate based on a vehicle trajectory associated with the first time (e.g., previously determined trajectory) and the lateral coordinate based on the planned path. The vehicle computing system may determine a second vehicle trajectory based in part on the estimated location and the first trajectory, and may control the vehicle according to the second vehicle trajectory.

Abstract: The techniques discussed herein may comprise an autonomous vehicle guidance system that generates a path for controlling an autonomous vehicle based at least in part on a static object map and/or one or more dynamic object maps. The guidance system may identify a path based at least in part on determining set of nodes and a cost map associated with the static and/or dynamic object, among other costs, pruning the set of nodes, and creating further nodes from the remaining nodes until a computational or other limit is reached. The path output by the techniques may be associated with a cheapest node of the sets of nodes that were generated.

Abstract: The present disclosure is directed to performing one or more validity checks on potential trajectories for a device, such as an autonomous vehicle, to navigate. In some examples, a potential trajectory may be validated based on whether it is consistent with a current trajectory the vehicle is navigating such that the potential and current trajectories are not too different, whether the vehicle can feasibly or kinematically navigate to the potential trajectory from a current state, whether the potential trajectory was punctual or received within a time period of a prior trajectory, and/or whether the potential trajectory passes a staleness check, such that it was created within a certain time period. In some examples, determining whether a potential trajectory is feasibly may include updating a set of feasibility limits based on one or more operational characteristics of statuses of subsystems of the vehicle.

Abstract: Trajectory determination for controlling a vehicle, such as an autonomous vehicle, is described. In an example, a vehicle system includes multiple planning systems for calculating trajectories. A first system may calculate first trajectories at a first frequency and the second system may calculate second trajectories at a second frequency and based on the first trajectories. The first and/or second trajectories may be initialized at states of the vehicle corresponding to a projection onto a previous-in-time respective first or second trajectory. The second trajectories may be control trajectories along which the vehicle is controlled.

Abstract: This application relates to techniques for determining whether to engage an autonomous controller of a vehicle based on previously recorded data. A computing system may receive, from a vehicle computing system, data representative of a vehicle being operated in an environment, such as by an autonomous controller. The computing system may generate a simulation associated with the vehicle operation and configured to test an updated autonomous controller. The computing system may determine one or more first time periods associated with the vehicle operations that satisfy one or more conditions associated with engaging an autonomous controller and one or more second time periods associated with the vehicle operations that fail to satisfy the one or more conditions. The computing system may enable an engagement of the autonomous controller during the one or more first time periods and disable the engagement during the one or more second time periods.

Abstract: This application relates to techniques for dynamically determining whether to engage an autonomous controller of a vehicle. A computing system may receive a request to engage the autonomous controller (e.g., autonomous mode) of the vehicle. In some examples, the request may be received from a simulation computing system configured to test an updated autonomous controller in a simulation. Based on a determination that conditions associated with engaging autonomy are satisfied, the computing system engages the autonomous controller. Based on a determination that conditions associated with engaging autonomy are not satisfied, the computing system disables the engagement of the autonomous controller such that the vehicle is controlled according to an initial operational mode (e.g., manual mode, semi-autonomous mode, previous version of the autonomous controller, etc.).

Abstract: Command determination for controlling a vehicle, such as an autonomous vehicle, is described. In an example, individual requests for controlling the vehicle relative to each of multiple objects or conditions in an environment are received (substantially simultaneously) and based on the request type and/or additional information associated with a request, command controllers can determine control commands (e.g., different accelerations, steering angles, steering rates, and the like) associated with each of the one or more requests. The command controllers may have different controller gains (which may be based on functions of distance, distance ratios, time to estimated collisions, etc.) for determining the controls and a control command may be determined based on the all such determined controls.

Abstract: Techniques for generating trajectories and drivable areas for navigating a vehicle in an environment are discussed herein. The techniques can include receiving a reference trajectory representing an initial trajectory for a vehicle, such as an autonomous vehicle, to traverse the environment. Portions of the reference trajectory can be identified as corresponding to actions to navigate around a double-parked vehicle or to change lanes, for example. In some cases, a portion of the reference trajectory can be identified based on a proximity to an object in the environment. A weight can be associated with the portions of the reference trajectory, and the techniques can include evaluating a reference cost function at points of the reference trajectory based on the associated weights to generate a target trajectory. Further, the techniques can include controlling the autonomous vehicle to traverse the environment based at least in part on the target trajectory.

Abstract: Techniques for determining to modify a trajectory based on an object are discussed herein. A vehicle can determine a drivable area of an environment, capture sensor data representing an object in the environment, and perform a spot check to determine whether or not to modify a trajectory. Such a spot check may include processing to incorporate an actual or predicted extent of the object into the drivable area to modify the drivable area. A distance between a reference trajectory and the object can be determined at discrete points along the reference trajectory, and based on a cost, distance, or intersection associated with the trajectory and the modified area, the vehicle can modify its trajectory. One trajectory modification includes following, which may include varying a longitudinal control of the vehicle, for example, to maintain a relative distance and velocity between the vehicle and the object.

Abstract: Techniques for generating trajectories and drivable areas for navigating a vehicle in an environment are discussed herein. The techniques can include receiving a trajectory representing an initial trajectory for a vehicle, such as an autonomous vehicle, to traverse the environment in a drivable area. A location can be determined along the trajectory. A cost associated with the location can determined and can be evaluated with respect to a cost threshold. Further, the techniques can include determining, based at least in part on the cost meeting or exceeding the cost threshold, an action associated with the location, and controlling the autonomous vehicle to traverse the environment based at least in part on the action.

Abstract: The techniques discussed herein may comprise an autonomous vehicle guidance system that generates a path for controlling an autonomous vehicle based at least in part on a static object map and/or one or more dynamic object maps. The guidance system may identify a path based at least in part on determining set of nodes and a cost map associated with the static and/or dynamic object, among other costs, pruning the set of nodes, and creating further nodes from the remaining nodes until a computational or other limit is reached. The path output by the techniques may be associated with a cheapest node of the sets of nodes that were generated.

Abstract: Command determination for controlling a vehicle, such as an autonomous vehicle, is described. In an example, individual requests for controlling the vehicle relative to each of multiple objects or conditions in an environment are received (substantially simultaneously) and based on the request type and/or additional information associated with a request, command controllers can determine control commands (e.g., different accelerations, steering angles, steering rates, and the like) associated with each of the one or more requests. The command controllers may have different controller gains (which may be based on functions of distance, distance ratios, time to estimated collisions, etc.) for determining the controls and a control command may be determined based on the all such determined controls.