Abstract: Techniques for determining parking spaces and/or parking trajectories for a vehicle based on multistage filtering are discussed herein. In some examples, a vehicle may navigate to a destination within an environment. The vehicle may receive a set of parking locations and perform filtering operations to determine a subset of the parking locations. For example, the vehicle may use a list of parking characteristics (e.g., conditions) to filter the set of parking spaces. Based on determining the subset of parking spaces, the vehicle may generate a set of candidate trajectories to the subset of parking spaces. The vehicle may determine a subset of the candidate trajectories based on filtering the set of candidate trajectories according to various constraints. Further, the vehicle may determine a cost for the subset of candidate trajectories. Based on the costs, the vehicle may determine a candidate trajectory for the vehicle to follow.

Abstract: Techniques for determining a parking trajectory for a vehicle are discussed herein. A parking management component may determine or receive a three-dimensional grid (“grid”), discretized based at least in part on a heading offset, a lateral offset, and/or a longitudinal offset between a first and second pose. A cell of the grid may include a cost associated indicating a minimum difference to the second pose when moving from the first as may be limited based on kinematic and/or dynamic constraints. When driving, a vehicle may determine a relative state of the vehicle to a desired location, use the relative state to access a cost from the grid, and determine whether to follow a trajectory based on the cost.

Abstract: This disclosure describes techniques for autonomous vehicles to determine driving paths and associated trajectories through unstructured or off-route driving environments. When an object is detected within a driving environment, a vehicle may determine a cost-based side association for the object. Various costs may be used in different examples, including costs based on a cost plot and/or motion primitives that may vary for terminal and non-terminal desired destinations (or ending vehicle states). Using tree searches to determine estimated candidate costs, the autonomous vehicle may compare right-side and left-side driving path costs around the object to determine a side association for the object. Based on the side association, the autonomous vehicle may determine an updated planning corridor and/or a trajectory to control the vehicle from a current state to a desired ending vehicle state.

Abstract: A passenger may be rather vulnerable to safety risks during pickup and/or drop-off of a passenger by a vehicle. To mitigate or eliminate such risk, the vehicle may determine an endpoint for a vehicle route to pickup or drop-off a passenger at a location. The vehicle may determine an estimated path between the endpoint and the location and may determine a safety confidence score by a machine-learned model for the estimated path and/or may predict a trajectory of a detected object to ascertain whether the estimated path is safe. The vehicle may execute any of a number of different mitigation actions to reduce or eliminate a safety risk if one is detected.

Abstract: Techniques for determining a parking trajectory for an autonomous vehicle. In some examples, a vehicle may navigate to a destination within an environment. The vehicle may receive parking locations proximate the destination, and determine a cost for navigating to each parking location, or multiple locations within a parking space. Each of multiple trajectories may include desired state(s) that represent the desired state information of the vehicle at a specific locations along the trajectory. The vehicle may determine a cost for the trajectory by comparing the predicted pose of the vehicle at the parking state to the target pose of the parking location. The vehicle may follow the trajectory based on the cost.

Abstract: Techniques for determining parking spaces and/or parking trajectories for a vehicle based on multistage filtering are discussed herein. In some examples, a vehicle may navigate to a destination within an environment. The vehicle may receive a set of parking locations and perform filtering operations to determine a subset of the parking locations. For example, the vehicle may use a list of parking characteristics (e.g., conditions) to filter the set of parking spaces. Based on determining the subset of parking spaces, the vehicle may generate a set of candidate trajectories to the subset of parking spaces. The vehicle may determine a subset of the candidate trajectories based on filtering the set of candidate trajectories according to various constraints. Further, the vehicle may determine a cost for the subset of candidate trajectories. Based on the costs, the vehicle may determine a candidate trajectory for the vehicle to follow.

Abstract: This disclosure describes techniques for autonomous vehicles to determine driving paths and associated trajectories through unstructured or off-route driving environments. When an object is detected within a driving environment, a vehicle may determine a cost-based side association for the object. Various costs may be used in different examples, including costs based on a cost plot and/or motion primitives that may vary for terminal and non-terminal desired destinations (or ending vehicle states). Using tree searches to determine estimated candidate costs, the autonomous vehicle may compare right-side and left-side driving path costs around the object to determine a side association for the object. Based on the side association, the autonomous vehicle may determine an updated planning corridor and/or a trajectory to control the vehicle from a current state to a desired ending vehicle state.

Abstract: Techniques for controlling a vehicle to select parking spaces are discussed herein. The vehicle may receive a request to travel to a destination and obtain information about a first set and a second set of parking spaces within a threshold distance of the destination. The vehicle may determine a first likelihood of availability associated with the first set of parking spaces and a second likelihood of availability associated with the second set of parking spaces. The vehicle may determine a first cost associated with a first route to the first set of parking spaces and a second cost associated with a second route to the second set of parking spaces by the cost function. Upon determining that the first cost is equal to or less than the second cost, the vehicle may navigate based on the first route and park within one of the first set of parking spaces.

Abstract: Techniques for providing remote assistance to a vehicle are discussed. The techniques include receiving, from a vehicle, an indication of an event and displaying, on a display and to a user, a portion of an environment including the vehicle. The techniques further determine a valid region in the portion of the environment associated with a location at which the vehicle is capable of navigating. The techniques also display, on the display a footprint of the vehicle, where the footprint is associated with a position and orientation. The techniques further include transmitting the position and orientation of the footprint to the vehicle, which causes the vehicle to traverse in the environment to the position and orientation.

Abstract: Techniques for determining a location for a vehicle to join a route structure are discussed herein. A vehicle computing system may operate the vehicle off the route structure according to an inertial-based reference frame. The vehicle computing system may determine a lateral distance of the vehicle to a route of the route structure and an angular difference between a heading of the vehicle and a direction of travel associated with the route. The vehicle computing system may determine a location to rejoin the route based on the lateral distance and the angular difference. In some examples, the vehicle computing system may determine the location based on a sigmoid function. The vehicle computing system may determine a vehicle trajectory to the location and may control the vehicle to the route based on the vehicle trajectory and the inertial-based reference frame.

Abstract: Techniques for determining a parking trajectory for an autonomous vehicle. In some examples, a vehicle may navigate to a destination within an environment. The vehicle may receive parking locations proximate the destination, and determine a cost for navigating to each parking location, or multiple locations within a parking space. Each of multiple trajectories may include desired state(s) that represent the desired state information of the vehicle at a specific locations along the trajectory. The vehicle may determine a cost for the trajectory by comparing the predicted pose of the vehicle at the parking state to the target pose of the parking location. The vehicle may follow the trajectory based on the cost.

Abstract: Techniques are discussed for generating and optimizing trajectories for controlling autonomous vehicles in performing on-route and off-route actions within a driving environment. A planning component of an autonomous vehicle can receive or generate time-discretized (or temporal) trajectories for the autonomous vehicle to traverse an environment. Trajectories can be optimized, for example, based on the lateral and longitudinal dynamics of the vehicle, using loss functions and/or costs. In some examples, the temporal optimization of a trajectory may include resampling a previous trajectory based on the differences in the time sequences of the temporal trajectories, to ensure temporal consistency of trajectories across planning cycles. Constraints also may be applied during temporal optimization in some examples, to control or restrict driving maneuvers that are not supported by the autonomous vehicle.

Abstract: Techniques are discussed herein for generating trajectories for controlling motion and/or other behaviors of vehicles in driving environments. In particular, techniques are described herein for using a tree search to determine a trajectory for a vehicle to join a driving route from an initial vehicle state off of the driving route structure. A vehicle computing system may determine various candidate trajectories, including trajectories based on an off-route inertial reference frame, additional trajectories based on the route structure, perturbed trajectories, etc. The set of candidate trajectories may be optimized and/or filtered based on objects in the environment, and the corresponding candidate actions may be used to generate a search tree between the off-route vehicle state and an on-route target state.

Abstract: Methods, systems, and devices are described for concurrently performing handoff-related measurements for neighbor cells using multiple input multiple output (MIMO) antenna resources. In one example, a mobile device is in communication with a serving cell. Handoff-related measurements of first wireless signals from a first neighbor cell are performed. The first wireless signals are received at first MIMO antenna resources of a device. Handoff-related measurements of second wireless signals from a second neighbor cell are performed, as well. The second wireless signals are received at second MIMO antenna resources concurrently with the first wireless signals received at the first MIMO antenna resources. The first handoff-related measurements and the second handoff-related measurements may be performed during a scan interval. A type of handoff-related measurement to perform may be determined based on a determined length of the scan interval.

Abstract: Techniques are described herein for generating trajectories for autonomous vehicles using velocity-based steering limits. A planning component of an autonomous vehicle can receive steering limits determined based on safety requirements and/or kinematic models of the vehicle. Discontinuous and discrete steering limit values may be converted into a continuous steering limit function for use during on-vehicle trajectory generation and/or optimization operations. When the vehicle is traversing a driving environment, the planning component may use steering limit functions to determine a set of situation-specific steering limits associated with the particular vehicle state and/or driving conditions. The planning component may execute loss functions, including steering angle and/or steering rate costs, to determine a vehicle trajectory based on the steering limits applicable to the current vehicle state.

Abstract: A crossflow air deflector part for directing airflow includes a front central spine, a first arcuate wall extending from the spine to a first back lateral edge of the airflow deflector, and a second arcuate wall extending from the spine to a second back lateral edge of the airflow deflector opposing the first back lateral edge. Such an airflow deflector can be implemented into a storage server, positioned between a laterally adjacent pair of data storage device (DSD) chambers and a pair of vertically stacked fans, such that the crossflow air deflector functions to direct airflow from one of the lateral DSD chambers into the lower fan and to direct airflow from the other lateral DSD chamber into the upper fan. Independent airflow control for each DSD chamber and each corresponding DSD is thereby provided.

Abstract: Techniques are described herein for generating trajectories for autonomous vehicles using velocity-based steering limits. A planning component of an autonomous vehicle can receive steering limits determined based on safety requirements and/or kinematic models of the vehicle. Discontinuous and discrete steering limit values may be converted into a continuous steering limit function for use during on-vehicle trajectory generation and/or optimization operations. When the vehicle is traversing a driving environment, the planning component may use steering limit functions to determine a set of situation-specific steering limits associated with the particular vehicle state and/or driving conditions. The planning component may execute loss functions, including steering angle and/or steering rate costs, to determine a vehicle trajectory based on the steering limits applicable to the current vehicle state.

Abstract: A passenger may be rather vulnerable to safety risks during pickup and/or drop-off of a passenger by a vehicle. To mitigate or eliminate such risk, the vehicle may determine an endpoint for a vehicle route to pickup or drop-off a passenger at a location. The vehicle may determine an estimated path between the endpoint and the location and may determine a safety confidence score by a machine-learned model for the estimated path and/or may predict a trajectory of a detected object to ascertain whether the estimated path is safe. The vehicle may execute any of a number of different mitigation actions to reduce or eliminate a safety risk if one is detected.

Abstract: Techniques for requesting remote assistance from a vehicle are discussed. The techniques include receiving, from a remote computing device, coordinates of a footprint in which the vehicle is capable of stopping. The techniques further include receiving, from the remote computing device, a target orientation associated with the footprint. The techniques may determine a path to the footprint based to achieve target orientation associated with the footprint.

Abstract: Techniques for providing remote assistance to a vehicle are discussed. The techniques include receiving, from a vehicle, an indication of an event and displaying, on a display and to a user, a portion of an environment including the vehicle. The techniques further determine a valid region in the portion of the environment associated with a location at which the vehicle is capable of navigating. The techniques also display, on the display a footprint of the vehicle, where the footprint is associated with a position and orientation. The techniques further include transmitting the position and orientation of the footprint to the vehicle, which causes the vehicle to traverse in the environment to the position and orientation.