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公开(公告)号：US12087063B2
公开(公告)日：2024-09-10
申请号：US17731433
申请日：2022-04-28
Applicant: Toyota Research Institute, Inc.
Inventor： Kun-Hsin Chen , Kuan-Hui Lee , Chao Fang , Charles Christopher Ochoa
CPC classification number: G06V20/584 , B60W60/0027 , G06V10/255 , G06V20/588 , B60W2420/403
Abstract: System, methods, and other embodiments described herein relate to detection of traffic lights corresponding to a driving lane from views captured by multiple cameras. In one embodiment, a method includes estimating, by a first model using images from multiple cameras, positions and state confidences of traffic lights corresponding to a driving lane of a vehicle. The method also includes aggregating, by a second model, the state confidences and a multi-view stereo composition from geometric representations associated with the positions of the traffic lights. The method also includes assigning, by the second model according to the aggregating, a relevancy score computed for a candidate traffic light of the traffic lights to the driving lane. The method also includes executing a task by the vehicle according to the relevancy score.
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公开(公告)号：US20230351773A1
公开(公告)日：2023-11-02
申请号：US17731433
申请日：2022-04-28
Applicant: Toyota Research Institute, Inc.
Inventor： Kun-Hsin Chen , Kuan-Hui Lee , Chao Fang , Charles Christopher Ochoa
CPC classification number: G06V20/584 , G06V20/588 , G06V10/255 , B60W60/0027 , B60W2420/42
Abstract: System, methods, and other embodiments described herein relate to detection of traffic lights corresponding to a driving lane from views captured by multiple cameras. In one embodiment, a method includes estimating, by a first model using images from multiple cameras, positions and state confidences of traffic lights corresponding to a driving lane of a vehicle. The method also includes aggregating, by a second model, the state confidences and a multi-view stereo composition from geometric representations associated with the positions of the traffic lights. The method also includes assigning, by the second model according to the aggregating, a relevancy score computed for a candidate traffic light of the traffic lights to the driving lane. The method also includes executing a task by the vehicle according to the relevancy score.
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公开(公告)号：US20230334873A1
公开(公告)日：2023-10-19
申请号：US17721868
申请日：2022-04-15
Applicant: Toyota Research Institute, Inc.
Inventor： Kun-Hsin Chen , Kuan-Hui Lee , Chao Fang , Charles Christopher Ochoa
CPC classification number: G06V20/584 , G06T7/70 , B60W30/143 , G06T2207/30252 , B60W2555/60 , B60W2554/4045 , B60W2554/4029 , B60W2720/10 , B60W2420/42
Abstract: System, methods, and other embodiments described herein relate to accurately distinguishing a traffic light from other illuminated objects in the traffic scene and detecting states using hierarchical modeling. In one embodiment, a method includes detecting, using a machine learning (ML) model, two-dimensional (2D) coordinates of illuminated objects identified from a monocular image of a traffic scene for control adaptation by a control model. The method also includes assigning, using the ML model, computed probabilities to the illuminated objects for categories within a hierarchical ontology of environmental lights associated with the traffic scene, wherein one of the probabilities indicates existence of a traffic light instead of a brake light in the traffic scene. The method also includes executing a task by the control model for a vehicle according to the 2D coordinates and the computed probabilities.
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公开(公告)号：US11753012B2
公开(公告)日：2023-09-12
申请号：US17039156
申请日：2020-09-30
Applicant: Toyota Research Institute, Inc.
Inventor： Hai Jin , Shunsho Kaku , Yutaka Taruoka , Kun-Hsin Chen , Peiyan Gong , Ryan W. Wolcott
CPC classification number: B60W30/18159 , G06V20/584 , H04W4/44 , B60W2556/55
Abstract: Systems and methods for controlling the operation of an autonomous vehicle are disclosed herein. One embodiment performs traffic light detection at an intersection using a sensor-based traffic light detector to produce a sensor-based detection output, the sensor-based detection output having an associated first confidence level; performs traffic light detection at the intersection using a vehicle-to-infrastructure-based (V2I-based) traffic light detector to produce a V2I-based detection output, the V2I-based detection output having an associated second confidence level; performs one of (1) selecting as a final traffic-light-detection output whichever of the sensor-based detection output and the V2I-based detection output has a higher associated confidence level and (2) generating the final traffic-light-detection output by fusing the sensor-based detection output and the V2I-based detection output using a first learning-based classifier; and controls the operation of the autonomous vehicle based, at least in part, on the final traffic-light-detection output.
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公开(公告)号：US12080161B2
公开(公告)日：2024-09-03
申请号：US17732376
申请日：2022-04-28
Applicant: TOYOTA RESEARCH INSTITUTE, INC.
Inventor： Kuan-Hui Lee , Charles Christopher Ochoa , Arjun Bhargava , Chao Fang , Kun-Hsin Chen
IPC: G08G1/01
CPC classification number: G08G1/0125 , G08G1/0141
Abstract: A method for vehicle prediction, planning, and control is described. The method includes separately encoding traffic state information at an intersection into corresponding traffic state latent spaces. The method also includes aggregating the corresponding traffic state latent spaces to form a generalized traffic geometry latent space. The method further includes interpreting the generalized traffic geometry latent space to form a traffic flow map including current and future vehicle trajectories. The method also includes decoding the generalized traffic geometry latent space to predict a vehicle behavior according to the traffic flow map based on the current and future vehicle trajectories.
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Patent Agency Ranking
公开(公告)号：US12014549B2
公开(公告)日：2024-06-18
申请号：US17192443
申请日：2021-03-04
Applicant: Toyota Research Institute, Inc.
Inventor： Jia-En Pan , Kuan-Hui Lee , Chao Fang , Kun-Hsin Chen , Arjun Bhargava , Sudeep Pillai
CPC classification number: G06V20/56 , B60R11/04 , G06T3/0093 , G06T7/337 , B60R2300/303 , G06T2207/20084 , G06T2207/20221 , G06T2207/30252
Abstract: A vehicle light classification system captures a sequence of images of a scene that includes a front/rear view of a vehicle with front/rear-side lights, determines semantic keypoints, in the images and associated with the front/rear-side lights, based on inputting the images into a first neural network, obtains multiple difference images that are each a difference between successive images from among the sequence of images, the successive images being aligned based on their respective semantic keypoints, and determines a classification of the front/rear-side lights based at least in part on the difference images by inputting the difference images into a second neural network.
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公开(公告)号：US11810367B2
公开(公告)日：2023-11-07
申请号：US17361424
申请日：2021-06-29
Applicant: Toyota Research Institute, Inc.
Inventor： Chao Fang , Kuan-Hui Lee , Logan Michael Ellis , Jia-En Pan , Kun-Hsin Chen , Sudeep Pillai , Daniele Molinari , Constantin Franziskus Dominik Hubmann , T. Wolfram Burgard
CPC classification number: G06V20/584 , G01S17/89 , G06F18/21 , G06F18/24323 , G06N3/044 , G06T7/246 , G06T7/521 , G06T2207/10028 , G06T2207/30252 , G06V2201/08
Abstract: Described herein are systems and methods for determining if a vehicle is parked. In one example, a system includes a processor, a sensor system, and a memory. Both the sensor system and the memory are in communication with the processor. The memory includes a parking determination module having instructions that, when executed by the processor, cause the processor to determine, using a random forest model, when the vehicle is parked based on vehicle estimated features, vehicle learned features, and vehicle taillight features of the vehicle that are based on sensor data from the sensor system.
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公开(公告)号：US20230351244A1
公开(公告)日：2023-11-02
申请号：US17733476
申请日：2022-04-29
Applicant: Toyota Research Institute, Inc.
Inventor： Chao Fang , Charles Christopher Ochoa , Kuan-Hui Lee , Kun-Hsin Chen , Visak Kumar
CPC classification number: G06N20/00 , G07C5/0841
Abstract: System, methods, and other embodiments described herein relate to a manner of generating and relating frames that improves the retrieval of sensor and agent data for processing by different vehicle tasks. In one embodiment, a method includes acquiring sensor data by a vehicle. The method also includes generating a frame including the sensor data and agent perceptions determined from the sensor data at a timestamp, the agent perceptions including multi-dimensional data that describes features for surrounding vehicles of the vehicle. The method also includes relating the frame to other frames of the vehicle by track, the other frames having processed data from various times and the track having a predetermined window of scene information associated with an agent. The method also includes training a learning model using the agent perceptions accessed from the track.
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9.发明公开公开(公告)号：US20230343109A1
公开(公告)日：2023-10-26
申请号：US17726939
申请日：2022-04-22
Applicant: Toyota Research Institute, Inc.
Inventor： Kun-Hsin Chen , Kuan-Hui Lee , Chao Fang , Charles Christopher Ochoa
CPC classification number: G06V20/584 , G06V20/588 , G06V10/56
Abstract: System, methods, and other embodiments described herein relate to improving the detection of traffic lights associated with a driving lane using a camera instead of map data. In one embodiment, a method includes estimating, from an image using a first model, depth and orientation information of traffic lights relative to a driving lane of a vehicle. The method also includes computing, using a second model, relevancy scores for the traffic lights according to geometric inferences between the depth and the orientation information. The method also includes assigning, using the second model, a primary relevancy score for a light of the traffic lights associated with the driving lane according to the depth and the orientation information. The method also includes executing a control task by the vehicle according to the primary relevancy score and a state confidence, computed by the first model, for the light.
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公开(公告)号：US12148223B2
公开(公告)日：2024-11-19
申请号：US17732421
申请日：2022-04-28
Applicant: TOYOTA RESEARCH INSTITUTE, INC.
Inventor： Arjun Bhargava , Chao Fang , Charles Christopher Ochoa , Kun-Hsin Chen , Kuan-Hui Lee , Vitor Guizilini
Abstract: A method for generating a dense light detection and ranging (LiDAR) representation by a vision system includes receiving, at a sparse depth network, one or more sparse representations of an environment. The method also includes generating a depth estimate of the environment depicted in an image captured by an image capturing sensor. The method further includes generating, via the sparse depth network, one or more sparse depth estimates based on receiving the one or more sparse representations. The method also includes fusing the depth estimate and the one or more sparse depth estimates to generate a dense depth estimate. The method further includes generating the dense LiDAR representation based on the dense depth estimate and controlling an action of the vehicle based on identifying a three-dimensional object in the dense LiDAR representation.
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