-
公开(公告)号：US20240157978A1
公开(公告)日：2024-05-16
申请号：US18506638
申请日：2023-11-10
发明人： Kelvin WONG , Simon SUO , Raquel URTASUN
CPC分类号： B60W60/00274 , G06F16/29
摘要： A method includes obtaining, from sensor data, map data of a geographic region and multiple trajectories of multiple agents located in the geographic region. The agents and the map data have a corresponding physical location in the geographic region. The method further includes determining, for an agent, an agent route from a trajectory that corresponds to the agent, generating, by an encoder model, an interaction encoding that encodes the trajectories and the map data, and generating, from the interaction encoding, an agent attribute encoding of the agent and the agent route. The method further includes processing the agent attribute encoding to generate positional information for the agent, and updating the trajectory of the agent using the positional information to obtain an updated trajectory.
-
公开(公告)号：US20240302530A1
公开(公告)日：2024-09-12
申请号：US18598958
申请日：2024-03-07
发明人： Enxu LI , Sergio CASAS ROMERO , Raquel URTASUN
IPC分类号： G01S17/894 , G01S7/4865 , G01S17/931 , G06T7/11
CPC分类号： G01S17/894 , G01S7/4865 , G01S17/931 , G06T7/11 , G06T2207/10028 , G06T2207/20084 , G06T2207/30252
摘要： LiDAR based memory segmentation includes obtaining a LiDAR point cloud that includes LiDAR points from a LiDAR sensor, voxelizing the LiDAR points to obtain LiDAR voxels, and encoding the LiDAR voxels to obtain encoded voxels. A LiDAR voxel memory is revised using the encoded voxels to obtain revised LiDAR voxel memory, decoding the revised LiDAR voxel memory to obtain decoded LiDAR voxel memory features. The LiDAR points are segmented using the decoded LiDAR voxel memory features to generate a segmented LiDAR point cloud.
-
公开(公告)号：US20240161436A1
公开(公告)日：2024-05-16
申请号：US18506668
申请日：2023-11-10
发明人： Yuwen XIONG , Wei-Chiu MA , Jingkang WANG , Raquel URTASUN
CPC分类号： G06T19/20 , G06T5/002 , G06T5/20 , G06T9/00 , G06T2207/10028 , G06T2207/20081 , G06T2207/30181 , G06T2210/56 , G06T2219/2016
摘要： Compact LiDAR representation includes performing operations that include generating a three-dimensional (3D) LiDAR image from LiDAR input data, encoding, by an encoder model, the 3D LiDAR image to a continuous embedding in continuous space, and performing, using a code map, a vector quantization of the continuous embedding to generate a discrete embedding. The operations further include decoding, by the decoder model, the discrete embedding to generate modified LiDAR data, and outputting the modified LiDAR data.
-
公开(公告)号：US20240303501A1
公开(公告)日：2024-09-12
申请号：US18598975
申请日：2024-03-07
发明人： Chris ZHANG , James TU , Lunjun ZHANG , Kelvin WONG , Simon SUO , Raquel URTASUN
IPC分类号： G06N3/092
CPC分类号： G06N3/092
摘要： Imitation and reinforcement learning for multi-agent simulation includes performing operations. The operations include obtaining a first real-world scenario of agents moving according to first trajectories and simulating the first real-world scenario in a virtual world to generate first simulated states. The simulating includes processing, by an agent model, the first simulated states for the agents to obtain second trajectories. For each of at least a subset of the agents, a difference between a first corresponding trajectory of the agent and a second corresponding trajectory of the agent is calculated and determining an imitation loss is determined based on the difference. The operations further include evaluating the second trajectories according to a reward function to generate a reinforcement learning loss, calculating a total loss as a combination of the imitation loss and the reinforcement learning loss, and updating the agent model using the total loss.
-
公开(公告)号：US20240300527A1
公开(公告)日：2024-09-12
申请号：US18598970
申请日：2024-03-07
发明人： Jack LU , Kelvin WONG , Chris ZHANG , Simon SUO , Raquel URTASUN
CPC分类号： B60W60/001 , B60W50/00 , G06N5/022 , B60W2556/25
摘要： Diffusion for realistic scene generation includes obtaining a current set of agent state vectors and a map data of a geographic region, and iteratively, through multiple diffusion timesteps, updating the current set of agent state vectors. Iteratively updating includes processing, by a noise prediction model, the current set of agent state vectors, a current diffusion timestep of the plurality of diffusion timesteps, and the map data to obtain a noise prediction value, generating a mean using the noise prediction value, generating a distribution function according to the mean, sampling a revised set of agent state vectors from the distribution function, and replacing the current set of agent state vectors with the revised set of agent state vectors. The current set of agent state vectors are outputted.
-
公开(公告)号：US20240159871A1
公开(公告)日：2024-05-16
申请号：US18506682
申请日：2023-11-10
发明人： Lunjun ZHANG , Yuwen XIONG , Sergio CASAS ROMERO , Mengye REN , Raquel URTASUN , Angi Joyce YANG
CPC分类号： G01S7/4802 , G01S17/89
摘要： Unsupervised object detection from lidar point clouds includes forecasting a set of new positions of a set of objects in a geographic region based on a first set of object tracks to obtain a set of forecasted object positions, and obtaining a new LiDAR point cloud of the geographic region. A detector model processes the new LiDAR point cloud to obtain a new set of bounding boxes around the set of objects detected in the new LiDAR point cloud. Object detection further includes matching the new set of bounding boxes to the set of forecasted object positions to generate a set of matches, updating the first set of object tracks with the new set of bounding boxes according to the set of matches to obtain an updated set of object tracks, and filtering, after updating, the updated set of object tracks to remove object tracks failing to satisfy a track length threshold, to generate a training set of object tracks. The object detection further includes selecting at least a subset of the new set of bounding boxes that are in the training set of object tracks, and retraining the detector model using the at least the subset of the new set of bounding boxes.
-
公开(公告)号：US20240303400A1
公开(公告)日：2024-09-12
申请号：US18598789
申请日：2024-03-07
发明人： James TU , Simon SUO , Raquel URTASUN
CPC分类号： G06F30/27 , G06F11/3688
摘要： A method includes generating a first sample including first raw parameter values of a first modifiable parameters by a probabilistic model and a kernel and executing a first test of a virtual driver of an autonomous system according to the first sample to generate a first evaluation result of multiple evaluation results. The method further includes updating the probabilistic model according to the first evaluation result and training the kernel using the first evaluation result. The method additionally includes generating a second sample including second raw parameter values of the parameters by the probabilistic model and the kernel and executing a second test of a virtual driver of an autonomous system according to the second sample to generate a second evaluation result of the evaluation results. The method further includes presenting the evaluation results.
-
公开(公告)号：US20240300526A1
公开(公告)日：2024-09-12
申请号：US18598961
申请日：2024-03-07
发明人： Sourav BISWAS , Sergio CASAS ROMERO , Quinlan SKYORA , Ben Taylor Caldwell AGRO , Abbas SADAT , Raquel URTASUN
CPC分类号： B60W60/001 , G01C21/30 , B60W2556/40
摘要： Motion planning with implicit occupancy for autonomous systems includes obtaining a set of trajectories through a geographic region for an autonomous system, and generating, for each trajectory in the set of trajectories, a set of points of interest in the geographic region to obtains sets of points of interest. Motion planning further includes quantizing the sets of points of interest to obtain a set of query points in the geographic region and querying the implicit decoder model with the set of query points to obtain point attributes for the set of query points. Motion planning further includes processing, for each trajectory of a least a subset of trajectories, the point attributes corresponding to the set of points of interest to obtain a trajectory cost for the trajectory. From the set of trajectories, a selected trajectory is selected according to trajectory cost.
-
公开(公告)号：US20240104335A1
公开(公告)日：2024-03-28
申请号：US18368488
申请日：2023-09-14
发明人： Alexander CUI , Sergio CASAS , Raquel URTASUN
摘要： Motion forecasting for autonomous systems includes obtaining map data of a geographic region and historical trajectories of agents located in the geographic region. The map data includes map elements. The agents and the map elements have a corresponding physical locations in the geographic region. Motion forecasting further includes building, from the historical trajectories and the map data, a heterogeneous graph for the agents and the map elements. The heterogeneous graph defines the corresponding physical locations of the agents and the map elements relative to each other of the agents and the map elements. Motion forecasting further includes modelling, by a graph neural network, agent actions of an agent of the agents using the heterogeneous graph to generate an agent goal location, and operating an autonomous system based on the agent goal location.
-
-
-
-
-
-
-
-
搜索结果
9 条记录 (耗时 0.018 秒)
