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41. 发明申请

US20190340465A1 SYSTEM AND METHOD OF PREDICTING HUMAN INTERACTION WITH VEHICLES 审中-公开
公开(公告)号：US20190340465A1
公开(公告)日：2019-11-07
申请号：US16512560
申请日：2019-07-16
申请人： Perceptive Automata, Inc.
发明人： Samuel English Anthony , Kshitij Misra , Avery Wagner Faller
IPC分类号： G06K9/62 , G06N3/04 , G06N3/08 , G06K9/00 , G05D1/00 , G08G1/04 , G08G1/16 , B60W30/00
摘要： Systems and methods for predicting user interaction with vehicles. A computing device receives an image and a video segment of a road scene, the first at least one of an image and a video segment being taken from a perspective of a participant in the road scene and then generates stimulus data based on the image and the video segment. Stimulus data is transmitted to a user interface and response data is received, which includes at least one of an action and a likelihood of the action corresponding to another participant in the road scene. The computing device aggregates a subset of the plurality of response data to form statistical data and a model is created based on the statistical data. The model is applied to another image or video segment and a prediction of user behavior in the another image or video segment is generated.

42. 发明授权

US3761079A Document feeding mechanism 失效
公开(公告)号：US3761079A
公开(公告)日：1973-09-25
申请号：US3761079D
申请日：1971-03-05
申请人： AUTOMATA CORP
发明人： AZURE L
IPC分类号： G06K13/10 , G06K13/103 , B65H3/06
CPC分类号： G06K13/10 , G06K13/103
摘要： A feeding mechanism receives a stack of documents, such as databearing cards, in a hopper, the hopper defining a feed throat through which the cards are fed individually and in succession. A feed roller is positioned for engaging the lowermost one of the stacks of cards, generally centrally of the planar surface thereof, the cards tending to bow about the feed roller and toward the support surface. The initial feeding effect of the feed roller in each cycle is to buckle or spring the card engaged thereby away from the stack and toward the support surface, thereby breaking frictional contact between that card and the adjacent card of the stack, greatly facilitating the feed operation. Drive rollers receive each card as it is fed through the throat from the hopper and transport the card from the hopper. In one embodiment, the feed roller is driven through a lost motion clutch from a drive shaft, in turn driven at a constant rotational speed. By virtue of the lost motion clutch, and the relative surface tangential velocities of the drive rollers and the feed rollers, the cards of the stack are fed individually and in succession, with uniform spacing between the trailing and leading edges of successive cards regardless of the lengths of the cards. Means are provided in the support surface of the hopper for separating the stack from the feed roller, thereby to interrupt the feed cycle. In a second embodiment of the invention, the feed roller is driven at a constant speed without the use of a lost motion clutch, and control means are provided for automatically effecting the separation of the stacks of cards from the feed roller. The automatic control means includes suitable detection means for enabling the feed means to feed successive cards of the stack, and thus provide the individual and successive feeding of the cards with the uniform spacing therebetween as aforenoted. In a typical application, the drive rollers drive the card through a sensing station, such as for optical sensing of indicia presented on the cards, and provide a stabilizing control on the card in the region of the sensing station for improving the accuracy of the sensing operation.

43. 发明授权

US3487561A Test grading and marking method and apparatus 失效
公开(公告)号：US3487561A
公开(公告)日：1970-01-06
申请号：US3487561D
申请日：1967-03-07
申请人： AUTOMATA CORP
发明人： AZURE LEO L JR , HASSFURTHER MAURICE EDMUND
IPC分类号： G06K17/00 , G09B7/10
CPC分类号： G06K17/0032

44. 发明授权

US3445942A Multiple printout test grading machine 失效
标题翻译：多功能打印机测试分级机
公开(公告)号：US3445942A
公开(公告)日：1969-05-27
申请号：US3445942D
申请日：1968-12-16
申请人： AUTOMATA CORP
发明人： AZURE LEO L JR
IPC分类号： G06K17/00 , G09B3/00
CPC分类号： G06K17/0032

45. 发明授权

US2973695A Electric control for copying machines 失效
标题翻译：复印机电控
公开(公告)号：US2973695A
公开(公告)日：1961-03-07
申请号：US75149358
申请日：1958-07-28
申请人： AUTOMATA SOC
发明人： WOLFGANG SCHMID
IPC分类号： B23Q35/12 , B23Q35/122
CPC分类号： B23Q35/122 , B23Q35/12 , B23Q2735/065 , Y10T409/30196

46. 发明授权

US2938132A Electric control of machine tools using control impulses of different frequencies 失效
标题翻译：使用不同频率的控制脉冲对机床进行电气控制
公开(公告)号：US2938132A
公开(公告)日：1960-05-24
申请号：US30698752
申请日：1952-08-29
申请人： AUTOMATA
发明人： WOLFGANG SCHMID
IPC分类号： G05B19/42
CPC分类号： G05B19/4207 , Y10T409/300896 , Y10T477/32

47. 发明授权

US12097878B2 Generating training data for machine learning based models for autonomous vehicles 有权
公开(公告)号：US12097878B2
公开(公告)日：2024-09-24
申请号：US17730048
申请日：2022-04-26
申请人： Perceptive Automata, Inc.
发明人： Elon Gaffin-Cahn
IPC分类号： B60W60/00 , B60W40/04
CPC分类号： B60W60/0011 , B60W40/04 , B60W2554/402 , B60W2554/4041 , B60W2556/10
摘要： A system receives information describing paths traversed by vehicles of a vehicle type, for example, a bicycle or a motorcycle. The system determines locations along the paths. For each location the system determines a measure of likelihood of encountering vehicles of the vehicle type in traffic at the location. The system selects a subset of locations based on the measure of likelihood and obtains sensor data captured at the subset of locations. The system uses the sensor data as training dataset for training a machine learning based model configured to receive input sensor data describing traffic and output a score used for navigation of autonomous vehicles. The machine learning model is provided to a vehicle, for example, an autonomous vehicle for navigation of the autonomous vehicle.

48. 发明授权

US11987272B2 System and method of predicting human interaction with vehicles 有权
公开(公告)号：US11987272B2
公开(公告)日：2024-05-21
申请号：US17190619
申请日：2021-03-03
申请人： Perceptive Automata, Inc.
发明人： Samuel English Anthony , Kshitij Misra , Avery Wagner Faller
IPC分类号： G06N3/08 , B60W30/00 , B60W60/00 , G05D1/00 , G06F18/214 , G06F18/40 , G06N3/04 , G06N3/084 , G06V10/778 , G06V20/40 , G06V20/58 , G06V40/20 , G08G1/04 , G08G1/16 , G06N5/01 , G06N20/10 , G06V10/62
CPC分类号： B60W60/00274 , B60W30/00 , G05D1/0088 , G06F18/214 , G06F18/41 , G06N3/04 , G06N3/08 , G06N3/084 , G06V10/7784 , G06V20/41 , G06V20/58 , G06V40/20 , G08G1/04 , G08G1/166 , G05D2201/0213 , G06N5/01 , G06N20/10 , G06V10/62
摘要： Systems and methods for predicting user interaction with vehicles. A computing device receives an image and a video segment of a road scene, the first at least one of an image and a video segment being taken from a perspective of a participant in the road scene and then generates stimulus data based on the image and the video segment. Stimulus data is transmitted to a user interface and response data is received, which includes at least one of an action and a likelihood of the action corresponding to another participant in the road scene. The computing device aggregates a subset of the plurality of response data to form statistical data and a model is created based on the statistical data. The model is applied to another image or video segment and a prediction of user behavior in the another image or video segment is generated.

49. 发明授权

US11919545B2 Scenario identification for validation and training of machine learning based models for autonomous vehicles 有权
公开(公告)号：US11919545B2
公开(公告)日：2024-03-05
申请号：US17321309
申请日：2021-05-14
申请人： Perceptive Automata, Inc.
发明人： Jeffrey D. Zaremba , Till S. Hartmann , Samuel English Anthony
IPC分类号： B60W60/00 , G05D1/00 , G05D1/02 , G06F18/21 , G06F18/2113 , G06F18/214 , G06F18/40 , G06N3/08 , G06N5/04 , G06N20/00 , G06V10/764 , G06V10/82 , G06V20/40 , G06V20/56 , G06V20/58 , G06V40/20
CPC分类号： B60W60/00272 , B60W60/001 , G05D1/0221 , G05D1/0246 , G06F18/2113 , G06F18/214 , G06F18/217 , G06F18/40 , G06N3/08 , G06N5/04 , G06N20/00 , G06V10/764 , G06V10/82 , G06V20/41 , G06V20/56 , G06V20/58 , G06V40/23 , B60W2420/42 , B60W2420/52 , B60W2552/05 , B60W2554/4029 , B60W2554/4041 , B60W2554/4044 , B60W2554/4045 , B60W2554/4046 , B60W2554/408 , B60W2554/801 , G05D2201/0213
摘要： A system uses a machine learning based model to determine attributes describing states of mind and behavior of traffic entities in video frames captured by an autonomous vehicle. The system classifies video frames according to traffic scenarios depicted, where each scenario is associated with a filter based on vehicle attributes, traffic attributes, and road attributes. The system identifies a set of video frames associated with ground truth scenarios for validating the accuracy of the machine learning based model and predicts attributes of traffic entities in the video frames. The system analyzes video frames captured after the set of video frames to determine actual attributes of the traffic entities. Based on a comparison of the predicted attributes and actual attributes, the system determines a likelihood of the machine learning based model making accurate predictions and uses the likelihood to generate a navigation action table for controlling the autonomous vehicle.

50. 发明公开

US20230351772A1 FRAMEWORK FOR EVALUATION OF MACHINE LEARNING BASED MODEL USED FOR AUTONOMOUS VEHICLE 审中-公开
公开(公告)号：US20230351772A1
公开(公告)日：2023-11-02
申请号：US18308622
申请日：2023-04-27
申请人： Perceptive Automata, Inc.
发明人： Sonia Poltoraski , Till S. Hartman , Jeffrey Donald Zaremba , Samuel English Anthony , Chuan Yen Ian Goh , Omar Al Assad
IPC分类号： G06V20/58 , B60W60/00 , G06V10/774 , G06V20/40
CPC分类号： G06V20/58 , B60W60/0027 , G06V10/774 , G06V20/40 , B60W2420/42 , G06V10/82
摘要： A system evaluates modifications to components of an autonomous vehicle (AV) stack. The system receives driving recommendations traffic scenarios based on user annotations of video frames showing each traffic scenario. For each traffic scenario, the system predicts driving recommendations based on the AV stack. The system determines a measure of quality of driving recommendation by comparing predicted driving recommendations based on the AV stack with the driving recommendations received for the traffic scenario. The measure of quality of driving recommendation is used for evaluating components of the AV stack. The system determines a driving recommendation for an AV corresponding to ranges of SOMAI (state of mind) score and sends signals to controls of the autonomous vehicle to navigate the autonomous vehicle according to the driving recommendation. The system identifies additional training data for training machine learning model based on the measure of driving quality.

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