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公开(公告)号：US20240367688A1
公开(公告)日：2024-11-07
申请号：US18658674
申请日：2024-05-08
Applicant: Aurora Operations, Inc.
Inventor： Alexander Yuhao Cui , Abbas Sadat , Sergio Casas , Renjie Liao , Raquel Urtasun
Abstract: Systems and methods are disclosed for motion forecasting and planning for autonomous vehicles. For example, a plurality of future traffic scenarios are determined by modeling a joint distribution of actor trajectories for a plurality of actors, as opposed to an approach that models actors individually. As another example, a diversity objective is evaluated that rewards sampling of the future traffic scenarios that require distinct reactions from the autonomous vehicle. An estimated probability for the plurality of future traffic scenarios can be determined and used to generate a contingency plan for motion of the autonomous vehicle. The contingency plan can include at least one initial short-term trajectory intended for immediate action of the AV and a plurality of subsequent long-term trajectories associated with the plurality of future traffic scenarios.
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Patent Agency Ranking
公开(公告)号：US12205004B2
公开(公告)日：2025-01-21
申请号：US18495434
申请日：2023-10-26
Applicant: Aurora Operations, Inc.
Inventor： Sergio Casas , Cole Christian Gulino , Shun Da Suo , Raquel Urtasun
Abstract: The present disclosure provides systems and methods for training probabilistic object motion prediction models using non-differentiable representations of prior knowledge. As one example, object motion prediction models can be used by autonomous vehicles to probabilistically predict the future location(s) of observed objects (e.g., other vehicles, bicyclists, pedestrians, etc.). For example, such models can output a probability distribution that provides a distribution of probabilities for the future location(s) of each object at one or more future times. Aspects of the present disclosure enable these models to be trained using non-differentiable prior knowledge about motion of objects within the autonomous vehicle's environment such as, for example, prior knowledge about lane or road geometry or topology and/or traffic information such as current traffic control states (e.g., traffic light status).
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3.发明授权公开(公告)号：US12214801B2
公开(公告)日：2025-02-04
申请号：US17528549
申请日：2021-11-17
Applicant: Aurora Operations, Inc.
Inventor： Jingkang Wang , Ava Alison Pun , Xuanyuan Tu , Mengye Ren , Abbas Sadat , Sergio Casas , Sivabalan Manivasagam , Raquel Urtasun
Abstract: Techniques for generating testing data for an autonomous vehicle (AV) are described herein. A system can obtain sensor data descriptive of a traffic scenario. The traffic scenario can include a subject vehicle and actors in an environment. Additionally, the system can generate a perturbed trajectory for a first actor in the environment based on perturbation values. Moreover, the system can generate simulated sensor data. The simulated sensor data can include data descriptive of the perturbed trajectory for the first actor in the environment. Furthermore, the system can provide the simulated sensor data as input to an AV control system. The AV control system can be configured to process the simulated sensor data to generate an updated trajectory for the subject vehicle in the environment. Subsequently, the system can evaluate an adversarial loss function based on the updated trajectory for the subject vehicle to generate an adversarial loss value.
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公开(公告)号：US20240391504A1
公开(公告)日：2024-11-28
申请号：US18676029
申请日：2024-05-28
Applicant: Aurora Operations, Inc.
Inventor： Shun Da Suo , Sebastián David Regalado Lozano , Sergio Casas , Raquel Urtasun
IPC: B60W60/00 , G06F18/214 , G06N3/045 , G06V20/58
Abstract: Systems and methods for generating synthetic testing data for autonomous vehicles are provided. A computing system can obtain map data descriptive of an environment and object data descriptive of a plurality of objects within the environment. The computing system can generate context data including deep or latent features extracted from the map and object data by one or more machine-learned models. The computing system can process the context data with a machine-learned model to generate synthetic motion prediction for the plurality of objects. The synthetic motion predictions for the objects can include one or more synthesized states for the objects at future times. The computing system can provide, as an output, synthetic testing data that includes the plurality of synthetic motion predictions for the objects. The synthetic testing data can be used to test an autonomous vehicle control system in a simulation.
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公开(公告)号：US20240391097A1
公开(公告)日：2024-11-28
申请号：US18670288
申请日：2024-05-21
Applicant: Aurora Operations, Inc.
Inventor： Sergio Casas , Davi Eugenio Nascimento Frossard , Shun Da Suo , Xuanyuan Tu , Raquel Urtasun
Abstract: Systems and methods for streaming sensor packets in real-time are provided. An example method includes obtaining a sensor data packet representing a first portion of a three-hundred and sixty degree view of a surrounding environment of a robotic platform. The method includes generating, using machine-learned model(s), a local feature map based at least in part on the sensor data packet. The local feature map is indicative of local feature(s) associated with the first portion of the three-hundred and sixty degree view. The method includes updating, based at least in part on the local feature map, a spatial map to include the local feature(s). The spatial map includes previously extracted local features associated with a previous sensor data packet representing a different portion of the three-hundred and sixty degree view than the first portion. The method includes determining an object within the surrounding environment based on the updated spatial map.
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公开(公告)号：US20250117709A1
公开(公告)日：2025-04-10
申请号：US18982461
申请日：2024-12-16
Applicant: Aurora Operations, Inc.
Inventor： Sergio Casas , Cole Christian Gulino , Shun Da Suo , Raquel Urtasun
Abstract: The present disclosure provides systems and methods for training probabilistic object motion prediction models using non-differentiable representations of prior knowledge. As one example, object motion prediction models can be used by autonomous vehicles to probabilistically predict the future location(s) of observed objects (e.g., other vehicles, bicyclists, pedestrians, etc.). For example, such models can output a probability distribution that provides a distribution of probabilities for the future location(s) of each object at one or more future times. Aspects of the present disclosure enable these models to be trained using non-differentiable prior knowledge about motion of objects within the autonomous vehicle's environment such as, for example, prior knowledge about lane or road geometry or topology and/or traffic information such as current traffic control states (e.g., traffic light status).
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