Zoox, Inc.

Jacob Patrick Thalman

Joseph Funke

Kazuhide Okamoto

Steven Cheng Qian

Sriram Narayanan

David Evan Zlotnik

Olivier Toupet

Liam Gallagher

Vincent Andreas Laurense

Marin Kobilarov

Ravi Verma Gogna

Mark Jonathon McClelland

Jeremy Schwartz

Zachary Stuart Churukian

Rasoul Kabirzadeh

Yunpeng Pan

Jack Riley

Genie Kim

Caili Li

Ruikun Yu

Joshua Dean Egbert

Amirhossein Tamjidi

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.

Autonomous vehicle trajectory generation and optimization

Techniques are described herein for determining simulated vehicle positional errors and correlating the positional errors with vehicle features. Such techniques may include receiving log data comprising trajectories and position data for a real vehicle, and executing a log-based simulated vehicle based on the log data. A simulated vehicle may be controlled to follow a simulation trajectory in a simulated environment based on the trajectory of the real vehicle. A simulation system may determine a difference between the positions of the simulated vehicle and corresponding positions of the real vehicle in the real environment. The techniques may further include determining a vehicle state features correlated to the lateral and/or longitudinal position errors of the simulated vehicle, and determining, based on the vehicle state features, position error distributions and/or models that can be used to control subsequent driving simulations.

Simulated vehicle position errors in driving simulations

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.

Determining vehicle position using sideslip vector

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.

Autonomous driving object detection and avoidance

Systems and techniques for determining acceleration controls to use in a trajectory for use in controlling a vehicle are described. An acceleration determination system may receive a trajectory from a trajectory determination system and determine, based on various vehicle, acceleration, and spatial trajectory parameters, the appropriate controls to configure in a longitudinal profile of the trajectory and the suitable spatial implementation points for implementing the controls within acceleration rate change constraints. An operational trajectory determination system may determine a trajectory for use in operating the vehicle using one or more candidate trajectories and/or associated data, including one or more acceleration controls determined by the acceleration determination system.

Systems and methods for spatially controlled acceleration and deceleration

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.

Remote assistance for vehicles

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.

Autonomous vehicle trajectory generation using velocity-based steering limits

Techniques for diagnosing and disambiguating a bias in one or more axels of a bidirectional four-wheel drive vehicle. In some examples, the diagnostics system may diagnose and disambiguate the bias in the two axles by collecting data related to the movement of the vehicle while the vehicle traverses a zero curvature path in a forward direction. The diagnostics system may then determine a bias in an axle acting as the rear axle based on the collected data. The bias in the axle acting as the front axle may then be determined based on the collected data and the bias in the rear axle.

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Menlo Park, CA, United States of America

Jacob Patrick Thalman