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: 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: 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: Techniques for controlling a vehicle on and off a route structure in an environment are discussed herein. A vehicle computing system controls the vehicle along a route based on a route-based reference system. The vehicle computing system may determine to operate off the route, such as to operate in reverse, park, etc. The vehicle computing system may modify vehicle operations to an inertial-based reference system to navigate to a location off the route. The vehicle computing system may determine a vehicle trajectory to the location off the route based on a reference trajectory between a location on the route and the location off the route and a corridor associated therewith. The vehicle computing system may transition between the route-based reference system and the inertial-based reference system, based on a determination to operate on or off the route.

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: 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: Techniques for controlling a vehicle on and off a route structure in an environment are discussed herein. A vehicle computing system controls the vehicle along a route based on a route-based reference system. The vehicle computing system may determine to operate off the route, such as to operate in reverse, park, etc. The vehicle computing system may modify vehicle operations to an inertial-based reference system to navigate to a location off the route. The vehicle computing system may determine a vehicle trajectory to the location off the route based on a reference trajectory between a location on the route and the location off the route and a corridor associated therewith. The vehicle computing system may transition between the route-based reference system and the inertial-based reference system, based on a determination to operate on or off the route.

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 and methods for identifying parking zones. For instance, a vehicle may identify a parking zone located near a destination location for the vehicle. The vehicle may then generate one or more lines representing the parking zone. Additionally, the vehicle may generate polygons representing objects located proximate to the parking zone. The vehicle may then determine whether the one or more lines intersect with one or more of the polygons. If the vehicle determines that the one or more lines intersect with one or more of the polygons, then the vehicle may identify one or more first portions of the parking zone that are occupied by one or more objects and as such, unavailable. Using the one or more first portions, the vehicle may identify one or more second portions of the parking zone that are not occupied by objects and as such, available.

Abstract: Model-based control of dynamical systems typically requires accurate domain-specific knowledge and specifications system components. Generally, steering actuator dynamics can be difficult to model due to, for example, an integrated power steering control module, proprietary black box controls, etc. Further, it is difficult to capture the complex interplay of non-linear interactions, such as power steering, tire forces, etc. with sufficient accuracy. To overcome this limitation, a recurring neural network can be employed to model the steering dynamics of an autonomous vehicle. The resulting model can be used to generate feedforward steering commands for embedded control. Such a neural network model can be automatically generated with less domain-specific knowledge, can predict steering dynamics more accurately, and perform comparably to a high-fidelity first principle model when used for controlling the steering system of a self-driving vehicle.

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: 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: Model-based control of dynamical systems typically requires accurate domain-specific knowledge and specifications system components. Generally, steering actuator dynamics can be difficult to model due to, for example, an integrated power steering control module, proprietary black box controls, etc. Further, it is difficult to capture the complex interplay of non-linear interactions, such as power steering, tire forces, etc. with sufficient accuracy. To overcome this limitation, a recurring neural network can be employed to model the steering dynamics of an autonomous vehicle. The resulting model can be used to generate feedforward steering commands for embedded control. Such a neural network model can be automatically generated with less domain-specific knowledge, can predict steering dynamics more accurately, and perform comparably to a high-fidelity first principle model when used for controlling the steering system of a self-driving vehicle.

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: 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: 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 vehicle having a control system to utilize a movable suspension to increase sensor coverage. The control system can detect an object of interest that is partially, or completely, outside the field of view of one or more sensors on the vehicle. The system can then use the movable suspension to raise one portion of the vehicle and/or lower another portion of the vehicle to bring the object of interest at least partially into the field of view of the sensor, increasing the effective field of view of the sensor. When an object of interest is determined to be significant (e.g., a traffic or street sign), the system can attempt to bring the object of interest into view of the sensor by tilting the vehicle. The system can use different tilt rates and/or tilt angles depending on whether the vehicle is occupied or not.

Abstract: Systems, methods, and apparatuses described herein are directed to vehicle self-diagnostics. For example, a vehicle can include sensors monitoring vehicle components, for perceiving objects and obstacles in an environment, and for navigating the vehicle to a destination. Data from these and other sensors can be leveraged to determine a behavior associated with the vehicle. Based at least in part on determining the behavior, a vehicle can determine a fault and query one or more information sources associated with the vehicle to diagnose the fault. Based on diagnosing the fault, the vehicle can determine instructions for redressing the fault. The vehicle can diagnose the fault in near-real time, that is, while driving or otherwise in the field.

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.

Abstract: Vehicle ride can be improved using perception based suspension control on a vehicle. A computing system on the vehicle may receive a map of a road surface. The computing system may identify a trajectory of the vehicle relative to the road surface, and determine if a deformation exists in a track of one of the vehicle tires. The deformation may include a depression and/or a raised portion from the road surface. The computing system may calculate an adjustment to make to one or more suspension system components to negate or minimize the effects of the vehicle traveling over the deformation or roughness of the road, and may send an instruction to adjust the suspension components accordingly. Additionally, or alternatively, the computing system may determine that the deformation may be avoidable, in whole or in part, and may cause the vehicle to maneuver around the deformation.