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公开(公告)号：US20220073096A1
公开(公告)日：2022-03-10
申请号：US17479777
申请日：2021-09-20
Applicant: Zoox, Inc.
Inventor： Abishek Krishna Akella , Janek Hudecek , Marin Kobilarov , Marc Wimmershoff
IPC: B60W60/00
Abstract: Command determination for controlling a vehicle, such as an autonomous vehicle, is described. In an example, individual requests for controlling the vehicle relative to each of multiple objects or conditions in an environment are received (substantially simultaneously) and based on the request type and/or additional information associated with a request, command controllers can determine control commands (e.g., different accelerations, steering angles, steering rates, and the like) associated with each of the one or more requests. The command controllers may have different controller gains (which may be based on functions of distance, distance ratios, time to estimated collisions, etc.) for determining the controls and a control command may be determined based on the all such determined controls.
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公开(公告)号：US20210271901A1
公开(公告)日：2021-09-02
申请号：US17325562
申请日：2021-05-20
Applicant: Zoox, Inc.
Abstract: Techniques for determining predictions on a top-down representation of an environment based on vehicle action(s) are discussed herein. Sensors of a first vehicle (such as an autonomous vehicle) can capture sensor data of an environment, which may include object(s) separate from the first vehicle (e.g., a vehicle or a pedestrian). A multi-channel image representing a top-down view of the object(s) and the environment can be generated based on the sensor data, map data, and/or action data. Environmental data (object extents, velocities, lane positions, crosswalks, etc.) can be encoded in the image. Action data can represent a target lane, trajectory, etc. of the first vehicle. Multiple images can be generated representing the environment over time and input into a prediction system configured to output prediction probabilities associated with possible locations of the object(s) in the future, which may be based on the actions of the autonomous vehicle.
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公开(公告)号：US20210271251A1
公开(公告)日：2021-09-02
申请号：US16805118
申请日：2020-02-28
Applicant: Zoox, Inc.
Inventor： Janek Hudecek , Marin Kobilarov , Jack Riley
Abstract: Techniques for compensating for errors in position of a vehicle are discussed herein. In some cases, a discrepancy may exist between a measured state of the vehicle and a desired state as determined by a system of the vehicle. Techniques and methods for a planning architecture of an autonomous vehicle that is able to provide maintain a smooth trajectory as the vehicle follows a planned path or route. In some cases, a planning architecture of the autonomous vehicle may compensate for differences between an estimated state and a planned path without the use of a separate system. In this example process, the planning architecture may include a mission planning system, a decision system, and a tracking system that together output a trajectory for a drive system.
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公开(公告)号：US10937320B2
公开(公告)日：2021-03-02
申请号：US16832940
申请日：2020-03-27
Applicant: Zoox, Inc.
Inventor： Timothy Caldwell , Dan Xie , William Anthony Silva , Abishek Krishna Akella , Jefferson Bradfield Packer , Rick Zhang , Marin Kobilarov
Abstract: Drive envelope determination is described. In an example, a vehicle can capture sensor data while traversing an environment and can provide the sensor data to computing system(s). The sensor data can indicate agent(s) in the environment and the computing system(s) can determine, based on the sensor data, a planned path through the environment relative to the agent(s). The computing system(s) can also determine lateral distance(s) to the agent(s) from the planned path. In an example, the computing system(s) can determine modified distance(s) based at least in part on the lateral distance(s) and information about the agents. The computing system can determine a drive envelope based on the modified distance(s) and can determine a trajectory in the drive envelope.
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公开(公告)号：US10691127B2
公开(公告)日：2020-06-23
申请号：US16193801
申请日：2018-11-16
Applicant: Zoox, Inc.
Inventor： Marin Kobilarov , Timothy Caldwell , Vasumathi Raman , Christopher Paxton , Joona Markus Petteri Kiiski , Jacob Lee Askeland , Robert Edward Somers
Abstract: Techniques for determining a trajectory for an autonomous vehicle are described herein. In general, determining a route can include utilizing a search algorithm such as Monte Carlo Tree Search (MCTS) to search for possible trajectories, while using temporal logic formulas, such as Linear Temporal Logic (LTL), to validate or reject the possible trajectories. Trajectories can be selected based on various costs and constraints optimized for performance. Determining a trajectory can include determining a current state of the autonomous vehicle, which can include determining static and dynamic symbols in an environment. A context of an environment can be populated with the symbols, features, predicates, and LTL formula. Rabin automata can be based on the LTL formula, and the automata can be used to evaluate various candidate trajectories. Nodes of the MCTS can be generated and actions can be explored based on machine learning implemented as, for example, a deep neural network.
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Patent Agency Ranking
公开(公告)号：US20190101919A1
公开(公告)日：2019-04-04
申请号：US16193801
申请日：2018-11-16
Applicant: Zoox, Inc.
Inventor： Marin Kobilarov , Timothy Caldwell , Vasumathi Raman , Christopher Paxton , Joona Markus Petteri Kiiski , Jacob Lee Askeland , Robert Edward Somers
Abstract: Techniques for determining a trajectory for an autonomous vehicle are described herein. In general, determining a route can include utilizing a search algorithm such as Monte Carlo Tree Search (MCTS) to search for possible trajectories, while using temporal logic formulas, such as Linear Temporal Logic (LTL), to validate or reject the possible trajectories. Trajectories can be selected based on various costs and constraints optimized for performance. Determining a trajectory can include determining a current state of the autonomous vehicle, which can include determining static and dynamic symbols in an environment. A context of an environment can be populated with the symbols, features, predicates, and LTL formula. Rabin automata can be based on the LTL formula, and the automata can be used to evaluate various candidate trajectories. Nodes of the MCTS can be generated and actions can be explored based on machine learning implemented as, for example, a deep neural network.
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公开(公告)号：US20190011910A1
公开(公告)日：2019-01-10
申请号：US15644310
申请日：2017-07-07
Applicant: Zoox, Inc.
Inventor： Amanda Lee Kelly Lockwood , Ravi Gogna , Gary Linscott , Timothy Caldwell , Marin Kobilarov , Paul Orecchio , Dan Xie , Ashutosh Gajanan Rege , Jesse Sol Levinson
IPC: G05D1/00 , G05D1/02 , G08G1/16 , B60W30/095
Abstract: A method for autonomously operating a driverless vehicle along a path between a first geographic location and a destination may include receiving communication signals from the driverless vehicle. The communication signals may include sensor data from the driverless vehicle and data indicating occurrence of an event associated with the path. The communication signals may also include data indicating that a confidence level associated with the path is less than a threshold confidence level due to the event. The method may also include determining, via a teleoperations system, a level of guidance to provide the driverless vehicle based on data associated with the communication signals, and transmitting teleoperations signals to the driverless vehicle. The teleoperations signals may include guidance to operate the driverless vehicle according to the determined level of guidance, so that a vehicle controller maneuvers the driverless vehicle to avoid, travel around, or pass through the event.
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公开(公告)号：US20180251126A1
公开(公告)日：2018-09-06
申请号：US15632208
申请日：2017-06-23
Applicant: Zoox, Inc.
Inventor： Gary Linscott , Robert Edward Somers , Joona Markus Petteri Kiiski , Marin Kobilarov , Timothy Caldwell , Jacob Lee Askeland , Ashutosh Gajanan Rege , Joseph Funke
CPC classification number: G05D1/0088 , B60W30/09 , G01C21/3407 , G05D1/0055 , G05D1/0212 , G05D1/0223 , G05D1/0272 , G05D1/0274 , G05D2201/0213 , G06N3/04 , G06N3/08
Abstract: Techniques for generating and executing trajectories to guide autonomous vehicles are described. In an example, a first computer system associated with an autonomous vehicle can generate, at a first operational frequency, a route to guide the autonomous vehicle from a current location to a target location. The first computer system can further determine, at a second operational frequency, an instruction for guiding the autonomous vehicle along the route and can generate, at a third operational frequency, a trajectory based at least partly on the instruction and real-time processed sensor data. A second computer system that is associated with the autonomous vehicle and is in communication with the first computer system can execute, at a fourth operational frequency, the trajectory to cause the autonomous vehicle to travel along the route. The separation of the first computer system and the second computer system can provide enhanced safety, redundancy, and optimization.
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公开(公告)号：US12269462B1
公开(公告)日：2025-04-08
申请号：US16820378
申请日：2020-03-16
Applicant: Zoox, Inc.
Inventor： Gowtham Garimella , Marin Kobilarov , Kai Zhenyu Wang
Abstract: Techniques relating to determining regions based on intents of objects are described. In an example, a computing device onboard a first vehicle can receive sensor data associated with an environment of the first vehicle. The computing device can determine, based on the sensor data, a region associated with a second vehicle proximate the first vehicle that is to be occupied by the second vehicle while the vehicle performs a maneuver. Further, the computing device can determine an instruction for controlling the first vehicle based at least in part on the region.
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公开(公告)号：US20240400103A1
公开(公告)日：2024-12-05
申请号：US18204339
申请日：2023-05-31
Applicant: Zoox, Inc.
Inventor： Matthew Van Heukelom , Chonhyon Park , Olivier Amaury Toupet , Marin Kobilarov
IPC: B60W60/00
Abstract: Techniques for predicting and avoiding collisions with objects detected in an environment of a vehicle are discussed herein. A vehicle computing device can implement a model that receives a set of potential reference trajectories for a vehicle to follow at a future time. The model can determine a tracking trajectory for the vehicle to follow while changing between a first reference trajectory and a second reference trajectory. The model may be implemented in connection with a parallel processing unit to determine points defining the tracking trajectory that represent spatial and temporal differences. The tracking trajectory can be used by the vehicle computing device for predicting vehicle actions by the vehicle computing device to control the vehicle.
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