Zoox, Inc.

Yunpeng Pan

Marin Kobilarov

Mark Jonathon McClelland

Adrian Michael Costantino

Joseph Funke

Linjun Zhang

Timothy Caldwell

Steven Cheng Qian

Kazuhide Okamoto

Jacob Patrick Thalman

Rasmus Fonseca

Sriram Narayanan

Liam Gallagher

Xianan Huang

Ke Sun

Joseph Lorenzetti

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

Techniques for determining a vehicle trajectory that causes a vehicle to navigate in an environment relative to one or more objects are described herein. For example, the techniques may include a computing device determining a decision tree having nodes to represent different object intents and/or nodes to represent vehicle actions at a future time. A tree search algorithm can search the decision tree to evaluate potential interactions between the vehicle and the one or more objects over a time period, and output a vehicle trajectory for the vehicle. The vehicle trajectory can be sent to a vehicle computing device for consideration during vehicle planning, which may include simulation.

Trajectory prediction based on a decision tree

Controlling motion of an autonomous vehicle may comprise determining a state space representation of the environment associated with the autonomous vehicle based at least in part on sensor data. The autonomous vehicle may parameterize the state space according to arc length and lateral distance from a route reference. The autonomous vehicle may determine a state in the state space upon which to base trajectory generation (e.g., via a tree search, via a state space sampling technique based on cost) and may determine a set of control instructions (i.e., a trajectory) that would bring the autonomous vehicle to the arc length and lateral distance specified by the state. Determining the state for generating the trajectory may be based on a heuristic for determining approximately where the trajectory would bring the vehicle, since the arc length parameterized state space doesn't include an indication of location.

Vehicle control using context-sensitive trajectory generation

The techniques discussed herein may comprise an autonomous vehicle guidance system that generates a group of candidate trajectories based at least in part on sensor data. This group of candidate trajectories is clustered into two or more clusters and one or more representative trajectories may be determined for each cluster. The representative trajectory may be one of the trajectories in the cluster or a separately determined trajectory that represents the trajectories in a cluster, such as a mean or median trajectory. The representative trajectory may be used to predict how a dynamic object would react to the representative trajectory. This prediction may be used to determine potentially different costs associated with the different trajectories of the cluster with which the representative trajectory is associated. These costs may include costs to induce following behavior by the autonomous vehicle and/or cost(s) associated with conditionally available roadway portions.

Vehicle control using trajectory clustering and reactive prediction

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Newark, CA, United States of America

Yunpeng Pan