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公开(公告)号：US20210192748A1
公开(公告)日：2021-06-24
申请号：US16719780
申请日：2019-12-18
Applicant: Zoox, Inc.
Abstract: Techniques for determining predictions on a top-down representation of an environment based on object movement are discussed herein. Sensors of a first vehicle (such as an autonomous vehicle) may capture sensor data of an environment, which may include object(s) separate from the first vehicle (e.g., a vehicle, a pedestrian, a bicycle). A multi-channel image representing a top-down view of the object(s) and the environment may be generated based in part on the sensor data. Environmental data (object extents, velocities, lane positions, crosswalks, etc.) may also be encoded in the image. Multiple images may be generated representing the environment over time and input into a prediction system configured to output a trajectory template (e.g., general intent for future movement) and a predicted trajectory (e.g., more accurate predicted movement) associated with each object. The prediction system may include a machine learned model configured to output the trajectory template(s) and the predicted trajector(ies).
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公开(公告)号：US10671076B1
公开(公告)日：2020-06-02
申请号：US15833715
申请日：2017-12-06
Applicant: Zoox, Inc.
Inventor： Marin Kobilarov , Timothy Caldwell , Vasumathi Raman , Christopher Paxton
Abstract: Techniques for generating trajectories for autonomous vehicles and for predicting trajectories for third-party objects using temporal logic and tree search are described herein. Perception data about an environment can be captured to determine static objects and dynamic objects. For a particular dynamic object, which can represent a third-party vehicle, predictive trajectories can be generated to represent possible trajectories based on available options and rules of the road. Operations can include determining probabilities that a third-party vehicle will execute a predictive trajectory and updating the probabilities over time as motion data is captured. Predictive trajectories can be provided to the autonomous vehicle and commands for the autonomous vehicle can be based on the predictive trajectories. Further, determining a trajectory can include utilizing a Monte Carlo Tree Search (MCTS) to search for possible trajectories, while using Linear Temporal Logic (LTL) formulas to validate or reject the possible trajectories.
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公开(公告)号：US10386836B2
公开(公告)日：2019-08-20
申请号：US15644267
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
Abstract: A method for operating a driverless vehicle may include receiving, at the driverless vehicle, sensor signals related to operation of the driverless vehicle, and road network data from a road network data store. The method may also include determining a driving corridor within which the driverless vehicle travels according to a trajectory, and causing the driverless vehicle to traverse a road network autonomously according to a path from a first geographic location to a second geographic location. The method may also include determining that an event associated with the path has occurred, and sending communication signals to a teleoperations system including a request for guidance and one or more of sensor data and the road network data. The method may include receiving, at the driverless vehicle, teleoperations signals from the teleoperations system, such that the vehicle controller determines a revised trajectory based at least in part on the teleoperations signals.
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公开(公告)号：US12179792B2
公开(公告)日：2024-12-31
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：US12162513B2
公开(公告)日：2024-12-10
申请号：US17670357
申请日：2022-02-11
Applicant: Zoox, Inc.
Inventor： Marin Kobilarov
Abstract: Generating a lane reference from a roadway shape and/or generating a trajectory for controlling an autonomous vehicle may include determining a predicted state of the lane reference and/or a candidate trajectory by an integrator. The disclosed integrator is implemented as a numerical integrator in predominantly closed-form that is able to avoid singularities while maintaining no approximation error. The disclosed integrator is also more robust to poor initial estimations, high curvature roadways, and zero-velocity conditions.
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Patent Agency Ranking
公开(公告)号：US12065171B2
公开(公告)日：2024-08-20
申请号：US17535357
申请日：2021-11-24
Applicant: Zoox, Inc.
Inventor： Gowtham Garimella , Marin Kobilarov , Andres Guillermo Morales Morales , Ethan Miller Pronovost , Kai Zhenyu Wang , Xiaosi Zeng
CPC classification number: B60W60/0027 , G05D1/0274 , G06N3/08 , G06N5/04 , B60W2552/53 , B60W2554/20 , B60W2554/402 , B60W2554/4041 , B60W2554/4042 , B60W2554/4043 , B60W2554/80 , B60W2555/20 , B60W2555/60
Abstract: Techniques for determining unified futures of objects in an environment are discussed herein. Techniques may include determining a first feature associated with an object in an environment and a second feature associated with the environment and based on a position of the object in the environment, updating a graph neural network (GNN) to encode the first feature and second feature into a graph node representing the object and encode relative positions of additional objects in the environment into one or more edges attached to the node. The GNN may be decoded to determine a predicted position of the object at a subsequent timestep. Further, a predicted trajectory of the object may be determined using predicted positions of the object at various timesteps.
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公开(公告)号：US20240092357A1
公开(公告)日：2024-03-21
申请号：US17900258
申请日：2022-08-31
Applicant: Zoox, Inc.
Inventor： Marin Kobilarov , Chonhyon Park
IPC: B60W30/095
CPC classification number: B60W30/0956 , B60W2555/60
Abstract: Techniques are discussed herein for determining optimal driving trajectories for autonomous vehicles in complex multi-agent driving environments. A baseline trajectory may be perturbed and parameterized into a vector of vehicle states associated with different segments (or portions) of the trajectory. Such a vector may be modified to ensure the resultant perturbed trajectory is kino-dynamically feasible. The vectorized perturbed trajectory may be input, including a representation of the current driving environment and additional agents, into a prediction model trained to output a predicted future driving scene. The predicted future driving scene, including predicted future states for the vehicle and predicted trajectories for the additional agents in the environment, may be evaluated to determine costs associated with each perturbed trajectory. Based on the determined costs, the optimization algorithm may determine subsequent perturbations and/or the optimal trajectory for controlling the vehicle in the driving environment.
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公开(公告)号：US11932282B2
公开(公告)日：2024-03-19
申请号：US17394334
申请日：2021-08-04
Applicant: Zoox, Inc.
Inventor： Timothy Caldwell , Rasmus Fonseca , Arian Houshmand , Xianan Huang , Marin Kobilarov , Lichao Ma , Chonhyon Park , Cheng Peng , Matthew Van Heukelom
CPC classification number: B60W60/0027 , G05B13/0265 , B60W2554/402 , B60W2554/4045
Abstract: Trajectory generation for controlling motion or other behavior of an autonomous vehicle may include alternately determining a candidate action and predicting a future state based on that candidate action. The technique may include determining a cost associated with the candidate action that may include an estimation of a transition cost from a current or former state to a next state of the vehicle. This cost estimate may be a lower bound cost or an upper bound cost and the tree search may alternately apply the lower bound cost or upper bound cost exclusively or according to a ratio or changing ratio. The prediction of the future state may be based at least in part on a machine-learned model's classification of a dynamic object as being a reactive object or a passive object, which may change how the dynamic object is modeled for the prediction.
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公开(公告)号：US11875678B2
公开(公告)日：2024-01-16
申请号：US16517506
申请日：2019-07-19
Applicant: Zoox, Inc.
Inventor： Zhenqi Huang , Marin Kobilarov
IPC: G08G1/0968 , B60W30/10 , G05D1/02 , G08G1/01
CPC classification number: G08G1/096805 , B60W30/10 , G05D1/021 , G08G1/0125 , B60W2556/50
Abstract: An autonomous vehicle guidance system that generates a path for controlling an autonomous vehicle based at least in part on a data structure generated based at least in part on sensor data that may indicate occupied space in an environment surrounding an autonomous vehicle. The guidance system may receive a grid and generate a grid associated with the grid and the data structure. The guidance system may additionally or alternatively sub-sample the grid (latterly and/or longitudinally) dynamically based at least in part on characteristics determined from the data structure. The guidance system may identify a path based at least in part on a set of precomputed motion primitives, costs associated therewith, and/or a heuristic cost plot that indicates a cheapest cost to move from one pose to another.
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公开(公告)号：US11851054B2
公开(公告)日：2023-12-26
申请号：US17351642
申请日：2021-06-18
Applicant: Zoox, Inc.
Inventor： Marin Kobilarov , Lichao Ma , Chonhyon Park , Matthew Van Heukelom
IPC: B60W30/095 , B60W60/00 , B60W30/18
CPC classification number: B60W30/0956 , B60W30/18159 , B60W60/00274 , B60W2552/05 , B60W2554/4044 , B60W2555/60
Abstract: Techniques for accurately predicting and avoiding collisions with objects detected in an environment of a vehicle are discussed herein. A vehicle computing device can implement a model to output data indicating costs for potential intersection points between the object and the vehicle in the future. The model may employ a control policy and a time-step integrator to determine whether an object may intersect with the vehicle, in which case the techniques may include predicting vehicle actions by the vehicle computing device to control the vehicle.
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