Zoox, Inc.

Tesla, Inc.

Mark Jonathon McClelland

Marin Kobilarov

Rasmus Fonseca

Jack Riley

Neda Cvijetic

Joseph Funke

Olivier Toupet

Adrian Michael Costantino

Yunpeng Pan

Francis Havlak

Dhanushka Nirmevan Kularatne

Liam Gallagher

Robert Cofield

Zeljko Popovic

Subhasis Das

Ravi Verma Gogna

Zeng Wang

Janek Hudecek

Yuanyuan Chen

David Evan Zlotnik

Kazuhide Okamoto

Jacob Patrick Thalman

Zhenqi Huang

Jeremy Schwartz

Sriram Narayanan

Arian Houshmand

Brian Michael Filarsky

Vincent Andreas Laurense

Amirhossein Tamjidi

Troy Donovan O′Neal

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 representing sensor data and predicted behavior of various objects in an environment are described herein. For example, an autonomous vehicle can represent prediction probabilities as an uncertainty model that may be used to detect potential collisions, define a safe operational zone or drivable area, and to make operational decisions in a computationally efficient manner. The uncertainty model may represent a probability that regions within the environment are occupied using a heat map type approach in which various intensities of the heat map represent a likelihood of a corresponding physical region being occupied at a given point in time.

Object uncertainty models to assist with drivable area determinations

Techniques are described for determining to modify a coordinate system in response to the occurrence of a condition. As non-limiting examples, such conditions comprise distance traveled, speed of the vehicle (or system), an amount of computational resources available, a time since the last change, a number of objects present, the presence (or absence) of a particular object proximate the vehicle, a distance to a proximate object, based on a particular frequency, or the like. Such modifications may improve the operation and safety of a computing system used for path planning and trajectory generation by allowing lower precision numerical representations to be used in safety-critical situations while avoiding potential computational errors that would otherwise result from using such a representation.

Adaptable origin for location offsets

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

Techniques for collision avoidance using an object contour are discussed. A trajectory associated with a vehicle may be received. Sensor data can be received from a sensor associated with the vehicle. A bounding contour may be determined and associated with an object represented in the sensor data. Based on the trajectory, a simulated position of the vehicle can be determined. Additionally, a predicted position of the bounding contour can be determined. Based on the simulated position of the vehicle and the predicted position of the bounding contour, a distance between the vehicle and the object may be determined. An action can be performed based on the distance between the vehicle and the object.

Collision avoidance using an object contour

Object uncertainty models

A teleoperations system that collaboratively works with an autonomous vehicle guidance system to generate a path for controlling the autonomous vehicle may comprise generating one or more trajectories at the teleoperations system based at least in part on environment data received from the autonomous vehicle and presenting the one or more trajectories to a teleoperator (e.g., a human user, machine-learned model, or artificial intelligence component). A selection of one of the trajectories may be received at the teleoperations system and transmitted to the autonomous vehicle. The one or more trajectories may be generated at the teleoperations system and/or received from the autonomous vehicle. Regardless, the autonomous vehicle may generate a control trajectory based on the trajectory received from teleoperations, instead of merely implementing the trajectory from the teleoperations system.

Collaborative vehicle path generation

The techniques discussed herein may comprise an autonomous vehicle guidance system that generates a path for controlling an autonomous vehicle based at least in part on a static object map and/or one or more dynamic object maps. The guidance system may identify a path based at least in part on determining set of nodes and a cost map associated with the static and/or dynamic object, among other costs, pruning the set of nodes, and creating further nodes from the remaining nodes until a computational or other limit is reached. The path output by the techniques may be associated with a cheapest node of the sets of nodes that were generated.

Unstructured vehicle path planner

Various vehicle technologies for improving positioning accuracy despite various factors that affect signals from navigation satellites. Such positioning accuracy is increased via determining an offset and communicating the offset in various ways or via sharing of raw positioning data between a plurality of devices, where at least one knows its location sufficiently accurately, for use in differential algorithms.

Systems and methods for correcting the geographic location of a vehicle

Technologies for vehicle positioning

Growing community of inventors

San Francisco, CA, United States of America

Mark Jonathon McClelland