Zoox, Inc.

David Martins Belo

Johannes Andreas Huennekens

Johannes Edren

Kshitij Agarwal

Michael Isaac Mizrahi

Ella Strack

Joseph Funke

Collin MacGregor

Jefferson Bradfield Packer

Andrew Scott Crego

Sy Kelly Olson

Thomas Joseph Burrow

Matthew Nathaniel Dickerson

Tushar Shishpal Rawat

Dhrumil Parikh

John Douglas Logan

Elaine Hoyen Kwan

Bala Sai Varma Gottumukkala

Techniques for fault injection testing are described herein. The techniques may include receiving, at a computing device, an indication to simulate a fault associated with a component of a vehicle. The computing device may be coupled to the component via a control line that includes a relay or switch component that, when activated, causes the component to simulate the fault. The computing device may also receive data indicative of a vehicle response, such as a measured trajectory of the vehicle in response to the fault, whether a backup system of the vehicle performed correctly in response to the fault, whether the vehicle responded to the fault within a threshold period of time, and the like. The computing device may determine a difference between the vehicle response and an intended response of the vehicle relative to a threshold difference.

Autonomous vehicle fault injection testing

Techniques are discussed herein for detecting and measuring faults within vehicle drive systems. In various implementations, actuator feedback monitors and/or vehicle performance monitors are used individually or in combination to determine faults within acceleration systems, braking systems, and/or steering systems of a vehicle. Different monitors may use different algorithms, models, and input data from the vehicle during operation, to compare a predicted drive system output to a corresponding measured output. Additionally, monitors may include associated sets of disable conditions used to distinguish drive system faults from other driving conditions. The outputs from actuator feedback monitors, vehicle performance monitors, and disable conditions may be analyzed to detect drive system faults and control the vehicle to address the faults and control the safe operation of the vehicle.

Model-based vehicle drive system monitors

A system may schedule a vehicle for accommodating preferences of occupants within the vehicle. In some instances, an identity of the occupant may be determined and using the identity, the preference of the occupant may be determined. The vehicle may be schedule for the occupant in instances where the vehicle is able to accommodate the preference. The vehicle may be adjusted or setting(s) of the vehicle may be adjusted based on the preference. The vehicle may travel along a route according to the preferences.

Vehicle scheduling based on occupant preferences

A location of an occupant within a vehicle and/or an activity engaged in by the occupant may be determined. Based on the location and/or the activity, a point of interest associated with the occupant and/or the vehicle may be determined. One or more systems of the vehicle, such as the steering and/or suspension, may be controlled to minimize acceleration associated with the point of interest, thereby increasing a comfort of the occupant. In instances where the vehicle includes more than one occupant, the vehicle may be adjusted to accommodate the multiple occupants.

Dynamic vehicle suspension and steering adjustment

A trajectory for a vehicle can be generated using a lateral offset bias. The vehicle, such as an autonomous vehicle (AV), may be directed to follow reference trajectory for through an environment. The AV may determine a segment associated with the reference trajectory based on curvatures of the reference trajectory, determine a lateral offset bias associated with the segment based at least in part on, for example, one or more of a speed or acceleration of the vehicle, and determine a candidate trajectory for the autonomous vehicle based at least in part on the lateral offset bias. The candidate trajectory may then be used to control the autonomous vehicle.

Trajectory generation using lateral offset biasing

Systems and methods are described for 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.

Feasibility validation for vehicle trajectory selection

A surface characterization system includes an active suspension a system with a wheel controller to control a first and second wheel of a vehicle where the active suspension causes a difference in loading between the first and second wheel. The wheel controller may cause the first wheel to slow and receive a signal indicative of a change of state of the vehicle. The wheel controller may cause the second wheel to oppose the change of state caused by the first wheel. The surface characterization system may estimate tire-surface parameterization data associated with the first tire and a surface upon which the vehicle is located.

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San Francisco, CA, United States of America

David Martins Belo