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公开(公告)号：US20220194419A1
公开(公告)日：2022-06-23
申请号：US17125890
申请日：2020-12-17
Applicant: Zoox, Inc.
Inventor： Arian Houshmand , Ravi Verma Gogna , Mark Jonathon McClelland
Abstract: 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.
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公开(公告)号：US20220161822A1
公开(公告)日：2022-05-26
申请号：US17247048
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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公开(公告)号：US20230051486A1
公开(公告)日：2023-02-16
申请号：US17977770
申请日：2022-10-31
Applicant: Zoox, Inc.
Inventor： Zhenqi Huang , Janek Hudecek , Marin Kobilarov , Dhanushka Nirmevan Kularatne , Mark Jonathon McClelland
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：US20220204029A1
公开(公告)日：2022-06-30
申请号：US17138710
申请日：2020-12-30
Applicant: Zoox, Inc.
Inventor： Yuanyuan Chen , Subhasis Das , Mark Jonathon McClelland , Troy Donovan O'Neal , Zeng Wang , Dhanushka Nirmevan Kularatne
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：US12189387B2
公开(公告)日：2025-01-07
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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Patent Agency Ranking
公开(公告)号：US12164306B1
公开(公告)日：2024-12-10
申请号：US17531534
申请日：2021-11-19
Applicant: Zoox, Inc.
Inventor： Brian Michael Filarsky , Joseph Funke , Mark Jonathon McClelland , Amirhossein Tamjidi , Olivier Toupet
IPC: G05D1/00 , B60W30/095 , B60W40/06
Abstract: 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.
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公开(公告)号：US11945469B2
公开(公告)日：2024-04-02
申请号：US17247048
申请日：2020-11-25
Applicant: Zoox, Inc.
Inventor： Rasmus Fonseca , Marin Kobilarov , Mark Jonathon McClelland , Jack Riley
CPC classification number: B60W60/0015 , B60W60/0027 , G05D1/0088 , G05D1/0214 , G05D2201/0213
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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公开(公告)号：US20250109956A1
公开(公告)日：2025-04-03
申请号：US18478698
申请日：2023-09-29
Applicant: Zoox, Inc.
Inventor： Mark Jonathon McClelland , Christopher James Gibson , Swapnil Vikas Mankar , Zhichun Feng , Igor Podkhodov
Abstract: The techniques described herein relate to controlling and/or influencing the routes driven by vehicles, autonomous or otherwise, such as vehicles in a fleet of vehicles managed by a fleet management system. In some cases, the techniques described herein relate to centrally generating road weights using a remote system such as a fleet management system and providing those pre-calculated weights to vehicles to simplify onboard route planning. Rather than transmitting large amounts of raw traffic, road condition, and other data to each vehicle, the remote system pre-processes the data into condensed road weights optimized for route planning. This architecture provides various technical advantages such as reduced data transmission, decreased computational load on vehicles, and decentralized control of fleet-wide routing.
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公开(公告)号：US12252200B2
公开(公告)日：2025-03-18
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
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：US11787438B2
公开(公告)日：2023-10-17
申请号：US17125890
申请日：2020-12-17
Applicant: Zoox, Inc.
Inventor： Arian Houshmand , Ravi Verma Gogna , Mark Jonathon McClelland
CPC classification number: B60W60/0011 , B60W60/0023 , G01C21/3469 , G05D1/0038 , G05D1/0088 , B60W2556/10
Abstract: 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.
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