Patents by Inventor Mark Jonathon McClelland
Mark Jonathon McClelland has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20240109585Abstract: 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.Type: ApplicationFiled: September 30, 2022Publication date: April 4, 2024Inventors: 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
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Patent number: 11945469Abstract: 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.Type: GrantFiled: November 25, 2020Date of Patent: April 2, 2024Assignee: Zoox, Inc.Inventors: Rasmus Fonseca, Marin Kobilarov, Mark Jonathon McClelland, Jack Riley
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Patent number: 11787438Abstract: 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.Type: GrantFiled: December 17, 2020Date of Patent: October 17, 2023Assignee: Zoox, Inc.Inventors: Arian Houshmand, Ravi Verma Gogna, Mark Jonathon McClelland
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Publication number: 20230051486Abstract: 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.Type: ApplicationFiled: October 31, 2022Publication date: February 16, 2023Inventors: Zhenqi Huang, Janek Hudecek, Marin Kobilarov, Dhanushka Nirmevan Kularatne, Mark Jonathon McClelland
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Patent number: 11485384Abstract: 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.Type: GrantFiled: May 11, 2020Date of Patent: November 1, 2022Assignee: Zoox, Inc.Inventors: Zhenqi Huang, Janek Hudecek, Dhanushka Nirmevan Kularatne, Mark Jonathon McClelland, Marin Kobilarov
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Publication number: 20220204029Abstract: 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.Type: ApplicationFiled: December 30, 2020Publication date: June 30, 2022Inventors: Yuanyuan Chen, Subhasis Das, Mark Jonathon McClelland, Troy Donovan O'Neal, Zeng Wang, Dhanushka Nirmevan Kularatne
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Publication number: 20220194419Abstract: 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.Type: ApplicationFiled: December 17, 2020Publication date: June 23, 2022Inventors: Arian Houshmand, Ravi Verma Gogna, Mark Jonathon McClelland
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Publication number: 20220161822Abstract: 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.Type: ApplicationFiled: November 25, 2020Publication date: May 26, 2022Inventors: Rasmus Fonseca, Marin Kobilarov, Mark Jonathon McClelland, Jack Riley
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Publication number: 20220163966Abstract: 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.Type: ApplicationFiled: November 25, 2020Publication date: May 26, 2022Inventors: Rasmus Fonseca, Marin Kobilarov, Mark Jonathon McClelland, Jack Riley
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Publication number: 20210347382Abstract: 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.Type: ApplicationFiled: May 11, 2020Publication date: November 11, 2021Inventors: Zhenqi Huang, Janek Hudecek, Dhanushka Nirmevan Kularatne, Mark Jonathon McClelland, Marin Kobilarov