Zoox, Inc.

Adrian Michael Costantino

Subhasis Das

Francesco Papi

Marin Kobilarov

Mark Jonathon McClelland

Yunpeng Pan

Michael Carsten Bosse

Rasmus Fonseca

Liam Gallagher

A vehicle computing system may implement techniques to determine whether two objects in an environment are related as an articulated object. The techniques may include applying heuristics and algorithms to object representations (e.g., bounding boxes) to determine whether two objects are related as a single object with two portions that articulate relative to each other. The techniques may include predicting future states of the articulated object in the environment. One or more model(s) may be used to determine presence of the articulated object and/or predict motion of the articulated object in the future. Based on the presence and/or motion of the articulated object, the vehicle computing system may control operation of the vehicle.

Articulated object determination

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

Predicting articulated object states

The techniques discussed herein include modifying a Kalman filter to additionally include a loss component that dampens the effect measurements with large errors (or measurements indicating states that are rather different than the predicted state) have on the Kalman filter and, in particular, the updated uncertainty and/or updated prediction. In some examples, the techniques include scaling a Kalman gain based at least in part on a loss function that is based on the innovation determined by the Kalman filter. The techniques additionally or alternatively include a reformulation of a Kalman filter that ensures that the uncertainties determined by the Kalman filter remain symmetric and positive definite.

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Foster City, CA, United States of America

Adrian Michael Costantino