Zoox, Inc.

Kazuhide Okamoto

Joseph Funke

Steven Cheng Qian

Jacob Patrick Thalman

Sriram Narayanan

Olivier Toupet

Liam Gallagher

Marin Kobilarov

Mark Jonathon McClelland

David Evan Zlotnik

Jeremy Schwartz

Jack Riley

Yunpeng Pan

Vincent Andreas Laurense

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

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

Techniques for determining environmental data of an environment associated with a vehicle are discussed herein. The vehicle can use the data to determine a vehicle configuration data, a trajectory for the vehicle to traverse, a route to a destination, or orientation for ingress/egress. The vehicle can determine control data based on the vehicle configuration data, the trajectory, or the route. The vehicle configuration data may be associated with a setting of a suspension component, a speed, or other component. The trajectory may be biased based on a source of the wind. The route may be selected from a first candidate route and a second candidate route using a first wind score associated with the first candidate route and a second score associated with the second candidate route. The vehicle can use a sensor, such as a wind sensor, to capture or otherwise determine the wind data.

Trajectory modification based on wind compensation

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

Vehicle control based on wind compensation

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.

Vehicle control to join route

Techniques for determining a condition of a component of a vehicle and/or adjusting a configuration of a vehicle based on a condition of a component of the vehicle are discussed herein. The vehicle can receive vehicle component data and use the vehicle component data to determine a condition of one or more vehicle components. The vehicle can use a sensor, such as an image sensor, to capture or otherwise determine the vehicle component data. The condition of the component(s) of the vehicle may be determined using a machine learned model trained to determine condition of the component from image data. The vehicle can determine vehicle configuration data based on the condition. The vehicle configuration data may include an instruction to navigate to a maintenance facility, to remove the vehicle from service, constraining operation of the vehicle, etc.

Vehicle component monitoring

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.

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Mountain View, CA, United States of America

Kazuhide Okamoto