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公开(公告)号：US11485384B2
公开(公告)日：2022-11-01
申请号：US16872284
申请日：2020-05-11
Applicant: Zoox, Inc.
Inventor： Zhenqi Huang , Janek Hudecek , Dhanushka Nirmevan Kularatne , Mark Jonathon McClelland , Marin Kobilarov
Abstract: 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.
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公开(公告)号：US20210347382A1
公开(公告)日：2021-11-11
申请号：US16872284
申请日：2020-05-11
Applicant: Zoox, Inc.
Inventor： Zhenqi Huang , Janek Hudecek , Dhanushka Nirmevan Kularatne , Mark Jonathon McClelland , Marin Kobilarov
Abstract: 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.
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公开(公告)号：US20250042429A1
公开(公告)日：2025-02-06
申请号：US18228426
申请日：2023-07-31
Applicant: Zoox, Inc.
Inventor： Akash Arora , Andrew Baker , Timothy Caldwell , Eunsuk Chong , Rasmus Fonseca , Ravi Gogna , Jeffrey Loris Irion , Dhanushka Nirmevan Kularatne , Yangwei Liu , Joseph Lorenzetti , Mark Jonathon McClelland , Jack Riley , Rick Zhang
IPC: B60W60/00
Abstract: Techniques for generating a driving surface cost landscape for determining costs for vehicle positions in an environment are described herein. A planning component within a vehicle may determine a non-preferred surface associated with a type of non-preferred area of an environment along a route of a vehicle, determine a preferred surface associated with a preferred area of the environment, determine an adjusted non-preferred surface by removing an overlapping area of the non-preferred surface that overlaps the preferred surface and determine a cost associated with a vehicle position based at least on the preferred surface and the adjusted non-preferred surface. The planning component may then determine a control trajectory for the autonomous vehicle based at least in part on the cost associated with the vehicle position.
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Patent Agency Ranking
公开(公告)号：US12077181B1
公开(公告)日：2024-09-03
申请号：US17491483
申请日：2021-09-30
Applicant: Zoox, Inc.
Inventor： Adrian Michael Costantino , Marin Kobilarov , Mark Jonathon McClelland , Yunpeng Pan
IPC: B60W60/00
CPC classification number: B60W60/0011 , B60W2552/30 , B60W2720/10 , B60W2720/106 , B60W2720/12 , B60W2720/125 , B60W2720/24
Abstract: 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.
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公开(公告)号：US12060060B1
公开(公告)日：2024-08-13
申请号：US17463464
申请日：2021-08-31
Applicant: Zoox, Inc.
Inventor： Adrian Michael Costantino , Rasmus Fonseca , Liam Gallagher , Marin Kobilarov , Mark Jonathon McClelland , Yunpeng Pan
IPC: B60W30/095 , G06F16/28
CPC classification number: B60W30/0956 , G06F16/285 , B60W2554/4041 , B60W2554/4042 , B60W2554/4043 , B60W2554/4044 , B60W2556/10
Abstract: 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.
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公开(公告)号：US12030522B2
公开(公告)日：2024-07-09
申请号：US17138710
申请日：2020-12-30
Applicant: Zoox, Inc.
Inventor： Yuanyuan Chen , Subhasis Das , Dhanushka Nirmevan Kularatne , Mark Jonathon McClelland , Troy Donovan O′Neal , Zeng Wang
CPC classification number: B60W60/0011 , B60W60/00274 , G01S17/931 , G06T7/74 , G06T15/06 , G06V10/44 , G06V20/58 , B60W2420/408 , B60W2554/80 , G06T2207/10028 , G06T2207/30261 , G06T2210/12
Abstract: 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.
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公开(公告)号：US20240109585A1
公开(公告)日：2024-04-04
申请号：US17957756
申请日：2022-09-30
Applicant: Zoox, Inc.
Inventor： Joseph Funke , Liam Gallagher , Marin Kobilarov , Vincent Andreas Laurense , Mark Jonathon McClelland , Sriram Narayanan , Kazuhide Okamoto , Jack Riley , Jeremy Schwartz , Jacob Patrick Thalman , Olivier Amaury Toupet , David Evan Zlotnik
IPC: B62D7/15
CPC classification number: B62D7/159
Abstract: 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.
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公开(公告)号：US20220163966A1
公开(公告)日：2022-05-26
申请号：US17247047
申请日：2020-11-25
Applicant: Zoox, Inc.
Inventor： Rasmus Fonseca , Marin Kobilarov , Mark Jonathon McClelland , Jack Riley
Abstract: 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.
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