Abstract: A prediction component of an autonomous vehicle may detect and use a combination of object attributes and semantic features to determine potential destinations and corresponding potential trajectories for the dynamic objects in an environment. In some examples, the prediction component may analyze sensor data using trained models to determine a number of object attributes for a dynamic object, as well as semantic features associated with the intent or purpose of the dynamic object. Using a combination of the object attributes and the semantic features, for the dynamic object and any number of potential destination(s), the prediction component may predict a likely potential destination and determine a predicted trajectory for the dynamic object. Based on predicted trajectory of the dynamic object, the autonomous vehicle may select and execute driving maneuvers to control the autonomous vehicle safely and efficiently within the environment.

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: An over actuated system capable of controlling wheel parameters, such as speed (e.g., by torque and braking), steering angles, caster angles, camber angles, and toe angles, of wheels in an associated vehicle. The system may determine the associated vehicle is in a rollover state and adjust wheel parameters to prevent vehicle rollover. Additionally, the system may determine a driving state and dynamically adjust wheel parameters to optimize driving, including, for example, cornering and parking. Such a system may also dynamically detect wheel misalignment and provide alignment and/or corrective driving solutions. Further, by utilizing degenerate solutions for driving, the system may also estimate tire-surface parameterization data for various road surfaces and make such estimates available for other vehicles via a network.

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: 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 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: An over actuated system capable of controlling wheel parameters, such as speed (e.g., by torque and braking), steering angles, caster angles, camber angles, and toe angles, of wheels in an associated vehicle. The system may determine the associated vehicle is in a rollover state and adjust wheel parameters to prevent vehicle rollover. Additionally, the system may determine a driving state and dynamically adjust wheel parameters to optimize driving, including, for example, cornering and parking. Such a system may also dynamically detect wheel misalignment and provide alignment and/or corrective driving solutions. Further, by utilizing degenerate solutions for driving, the system may also estimate tire-surface parameterization data for various road surfaces and make such estimates available for other vehicles via a network.

Abstract: Systems and techniques for determining a sideslip vector for a vehicle that may have a direction that is different from that of a heading vector for the vehicle. The sideslip vector in a current vehicle state and sideslip vectors in predicted vehicles states may be used to determine paths for a vehicle through an environment and trajectories for controlling the vehicle through the environment. The sideslip vector may be based on a vehicle position that is the center point of the wheelbase of the vehicle and may include lateral velocity, facilitating the control of four-wheel steered vehicle while maintaining the ability to control two-wheel steered vehicles.

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: Four-wheel steering of a vehicle, e.g., in which leading wheels and trailing wheels are steered independently of each other, can provide improved maneuverability and stability. A first vehicle model may be used to determine trajectories for execution by a vehicle equipped with four-wheel steering. A second vehicle model may be used to control the vehicle relative to the determined trajectories. For instance, the second vehicle model can determine leading wheels steering angles for steering leading wheels of the vehicle and trailing wheels steering angles for steering trailing wheels of the vehicle, independently of the leading wheels.

Abstract: Techniques for optimal trajectory generation for a vehicle are described herein. In some cases, the techniques described herein include determining a first primary trajectory by a first component (e.g., a planner component) of the vehicle, determining a triggering event associated with the first primary trajectory by a second component (e.g., a trajectory validation component) of the vehicle, controlling the vehicle based on an alternative trajectory (e.g., a trajectory configured to cause the vehicle to stop and/or slow down) in response to the triggering event, determining a second primary trajectory by the first component and based at least in part on a state of the vehicle along the alternative trajectory, and one or more of: (i) controlling the vehicle based on the alternative trajectory if the triggering event is still present, or (ii) controlling the vehicle based on the second primary trajectory if the triggering event is no longer present.

Abstract: Techniques are described for determining to modify a coordinate system in response to the occurrence of a condition. As non-limiting examples, such conditions comprise distance traveled, speed of the vehicle (or system), an amount of computational resources available, a time since the last change, a number of objects present, the presence (or absence) of a particular object proximate the vehicle, a distance to a proximate object, based on a particular frequency, or the like. Such modifications may improve the operation and safety of a computing system used for path planning and trajectory generation by allowing lower precision numerical representations to be used in safety-critical situations while avoiding potential computational errors that would otherwise result from using such a representation.

Abstract: Techniques for accurately predicting vehicle state errors for avoiding collisions with objects detected in an environment of a vehicle are discussed herein. A vehicle safety system can implement a model determine a representation of the vehicle usable in a scenario. The model may dynamically determine a size and/or a heading of the vehicle representation based on differences between a candidate trajectory and a current trajectory of the vehicle. The safety system can identify a potential collision between the vehicle and the object based at least in part on an overlap of the vehicle representation and an object representation.

Abstract: Four-wheel steering of a vehicle, e.g., in which leading wheels and trailing wheels are steered independently of each other, can provide improved maneuverability and stability. Steering angle constraints may be used to limit or prevent saturation of steering by only one set of wheels as well as to reduce sideslip. The steering angle constraints are vehicle independent and applicable across a fleet of different vehicles based on vehicle speed. For instance, the steering angle constraints establish angle limits that dynamically adjust based on vehicle speed to ensure vehicle velocity and acceleration remain within predefined limits as established by autonomous vehicle system design.

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: A vehicle can include a primary computing device and a secondary computing device. The primary computing device can receive a trajectory and can generate control data to control the vehicle based on a computed state. Further, the primary computing device can send the internal data to the secondary computing device configured to control the vehicle in the event of a failure of the primary computing device. The secondary computing device can receive the internal data as first internal data and determine a capability associated with the primary computing device. Using the first internal data, the secondary computing device can determine second internal data and, based on the capability (e.g., in event of a failure of the primary computing device), can control the vehicle to follow a trajectory using the second internal data. Transferring state between an active to a standby computing device can ensure algorithmic synchronization and safe operation.

Abstract: A device for attachment to a surface of a ferromagnetic object and for holding an item near the surface, including: a base with a frame and grip portion, the grip portion coupled to the frame at the rear side and having an outer surface for gripping the surface; a magnet-retaining portion pivotally connected to an end of the frame; a magnetic device connected to the magnet-retaining portion and having an outer surface displaced frontwardly from the outer surface of the grip portion; a cover pivotally connected to another end of the frame, the cover including a lifter configured to contact an end of the magnet-retaining portion and cause an end of the magnet-retaining portion to pivot about the frame when the cover is lifted and the lifter is rotated, thereby moving the first end of the magnet-retaining portion and the magnetic device in a rear-to-front direction; and an item-holding portion.

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: Trajectory generation and/or execution architecture is described. In an example, a first signal can be determined at a first frequency, wherein the first signal comprises information associated with causing the system to move to a location. Further, a second signal can be determined at a second frequency different from the first frequency and based at least in part on the first signal. A system can be controlled to move to the location, based at least in part on the second signal.