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公开(公告)号：US20220406032A1
公开(公告)日：2022-12-22
申请号：US17777207
申请日：2020-11-13
Applicant: DENSO WAVE INCORPORATED , DENSO IT LABORATORY, INC.
Inventor： Yusuke SEKIKAWA , Yang SHI
Abstract: The image restoration device has an initialization block that initializes the luminance value of each pixel coordinate to an intermediate value in the luminance array list that stores one of a pair of polarity values and intermediate values as the luminance value for each pixel coordinate. The image restoration device also has an update block that updates the initialized luminance array list according to the pixel coordinates and polarity values for each event, and an output block that outputs the luminance array list updated by the update block over the shooting period as a binary image. By the update performed in the update block, the luminance values of the firing coordinates where the event fired in the luminance array list are overwritten by the polarity values of the event. In addition, the update preserves the luminance values of the non-firing coordinates in the luminance array list, excluding the firing coordinates.
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公开(公告)号：US11521057B2
公开(公告)日：2022-12-06
申请号：US15795691
申请日：2017-10-27
Applicant: DENSO IT LABORATORY, INC. , Tokyo Institute of Technology
Inventor： Ikuro Sato , Ryo Fujisaki , Akihiro Nomura , Yosuke Oyama , Satoshi Matsuoka
Abstract: According to one embodiment of the present disclosure, provided is a learning system that updates a parameter for a neural network, the learning system including: a plurality of differential value calculators; and a parameter update module.
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3.发明授权公开(公告)号：US09449505B2
公开(公告)日：2016-09-20
申请号：US13795610
申请日：2013-03-12
Applicant: DENSO IT LABORATORY, INC.
Inventor： Osamu Masutani
CPC classification number: G08G1/00 , G08G1/0112 , G08G1/0133 , G08G1/0141 , G08G1/0145
Abstract: Provided is a traffic congestion prediction method which is able to perform a prediction process using floating information with higher accuracy. The traffic congestion prediction method includes: a step of receiving information by a prediction device; a step of predicting a route of each floating car based on the current position information and destination information received; a step of calculating, for the each floating car, a first passing time group which is a set of respective passing times at a plurality of predetermined spots on the route predicted; a step of calculating the number of existing floating cars per link based on the first passing time group, if any of a plurality of floating cars exists on the link at a predetermined time; and a step of calculating a second passing time group by use of the number of existing floating cars and a predetermined calculation technique.
Abstract translation: 提供了能够以更高精度执行使用浮动信息的预测处理的交通拥堵预测方法。 交通拥堵预测方法包括：通过预测装置接收信息的步骤; 基于接收到的当前位置信息和目的地信息来预测每个浮动车辆的路线的步骤; 对于每个浮动车计算作为预测路线上的多个预定点处的各个通过时间的集合的第一通过时间组的步骤; 如果在预定时间内在链路上存在多个浮动车辆中的任何一个，则基于第一通过时间组计算每个链路的现有浮动车辆的数量的步骤; 以及通过使用现有浮动车辆的数量和预定的计算技术来计算第二通过时间组的步骤。
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公开(公告)号：US12136032B2
公开(公告)日：2024-11-05
申请号：US15812118
申请日：2017-11-14
Applicant: DENSO IT LABORATORY, INC.
Inventor： Jingo Adachi
Abstract: A technique for stable and fast computation of a variance representing a confidence interval for an estimation result in an estimation apparatus using a neural network including an integrated layer that combines a dropout layer for dropping out part of input data and an FC layer for computing a weight is provided. When input data having a multivariate distribution is supplied to the integrated layer, a data analysis unit 30 determines, based on a numerical distribution of terms formed by respective products of each vector element of the input data and the weight, a data type of each vector element of output data from the integrated layer. An estimated confidence interval computation unit 20 applies an approximate computation method associated with the data type, to analytically compute a variance of each vector element of the output data from the integrated layer based on the input data to the integrated layer.
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5.发明授权公开(公告)号：US10980463B2
公开(公告)日：2021-04-20
申请号：US15465784
申请日：2017-03-22
Applicant: DENSO IT LABORATORY, INC.
Inventor： Tomokatsu Okuya
Abstract: Disclosed is a technique of determining a driver's tension level (tension state degree) in vehicle driving in detail with a simple configuration. According to the technique, a nonlinear analyzing unit 110 of a driver's tension level determining apparatus 100 determining the tension level in driving of a driver acquires the driving operation amounts relating to driving operations of a driver (the operation amounts relating to operations of an accelerator pedal, a brake pedal, a handle, and the like), and then calculates the Lyapunov exponents about the driving operation amounts by performing nonlinear analysis processing. A frequency spectrum analyzing unit 120 calculates the power spectral density of time series data of the Lyapunov exponents, and then calculates an integrated value of a predetermined low frequency band in the calculated power spectral density. A driver's tension level determining unit 130 determines that the driver's tension level is any one of an excessive tension state, a moderate tension state, and an insufficient tension state using the integrated value of the predetermined low frequency band.
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Patent Agency Ranking
公开(公告)号：US20180181863A1
公开(公告)日：2018-06-28
申请号：US15795691
申请日：2017-10-27
Applicant: DENSO IT LABORATORY, INC. , Tokyo Institute of Technology
Inventor： Ikuro SATO , Ryo FUJISAKI , Akihiro NOMURA , Yosuke OYAMA , Satoshi MATSUOKA
CPC classification number: G06N3/08 , G06F17/10 , G06N3/0454 , G06N3/084
Abstract: According to one embodiment of the present disclosure, provided is a learning system that updates a parameter for a neural network, the learning system including: a plurality of differential value calculators; and a parameter update module.
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7.发明申请公开(公告)号：US20170269201A1
公开(公告)日：2017-09-21
申请号：US15456666
申请日：2017-03-13
Applicant: DENSO IT LABORATORY, INC.
Inventor： Jingo Adachi , Chiharu Yamano
CPC classification number: G01S13/56 , G01S13/42 , G01S13/584 , G01S13/726 , G01S13/89 , G01S13/931 , G01S2013/0263
Abstract: A technique of estimating a surrounding environment with higher accuracy from observation data obtained by using sensing equipment is disclosed. According to the technique, a certain pixel in an observation data space which includes the observation data obtained by a sensor 50 is sampled. With regard to the sampled pixel, a grid-unspecified logarithm-likelihood ratio indicating whether a received signal is a signal reflected from a detected object is calculated from an intensity value of the received signal, and a distribution function of an angle indicating the degree of dispersion centering on an azimuth angle is calculated. Then, the product of the grid-unspecified logarithm-likelihood ratio and the distribution function is calculated and thereby a calculation for updating an occupation probability of the object is performed across a plurality of grids in an occupancy grid map by using a value obtained by dispersing the likelihood using the distribution function.
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8.发明申请公开(公告)号：US20130253812A1
公开(公告)日：2013-09-26
申请号：US13795610
申请日：2013-03-12
Applicant: DENSO IT LABORATORY, INC.
Inventor： Osamu Masutani
IPC: G08G1/00
CPC classification number: G08G1/00 , G08G1/0112 , G08G1/0133 , G08G1/0141 , G08G1/0145
Abstract: Provided is a traffic congestion prediction method which is able to perform a prediction process using floating information with higher accuracy. The traffic congestion prediction method includes: a step of receiving information by a prediction device; a step of predicting a route of each floating car based on the current position information and destination information received; a step of calculating, for the each floating car, a first passing time group which is a set of respective passing times at a plurality of predetermined spots on the route predicted; a step of calculating the number of existing floating cars per link based on the first passing time group, if any of a plurality of floating cars exists on the link at a predetermined time; and a step of calculating a second passing time group by use of the number of existing floating cars and a predetermined calculation technique.
Abstract translation: 提供了能够以更高精度执行使用浮动信息的预测处理的交通拥堵预测方法。 交通拥堵预测方法包括：通过预测装置接收信息的步骤; 基于接收到的当前位置信息和目的地信息来预测每个浮动车辆的路线的步骤; 对于每个浮动车计算作为预测路线上的多个预定点处的各个通过时间的集合的第一通过时间组的步骤; 如果在预定时间内在链路上存在多个浮动车辆中的任何一个，则基于第一通过时间组计算每个链路的现有浮动车辆的数量的步骤; 以及通过使用现有浮动车辆的数量和预定的计算技术来计算第二通过时间组的步骤。
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9.发明授权公开(公告)号：US10769479B2
公开(公告)日：2020-09-08
申请号：US15947009
申请日：2018-04-06
Applicant: Denso IT Laboratory, Inc.
Inventor： Ikuro Sato , Mitsuru Ambai , Hiroshi Doi
Abstract: A recognition system includes: a sensor processing unit (SPU) that performs sensing to output a sensor value; a task-specific unit (TSU) including an object detection part that performs an object detection task based on the sensor value and a semantic segmentation part that performs a semantic segmentation task based on the sensor value; and a generic-feature extraction part (GEU) including a generic neural network disposed between the sensor processing unit and the task-specific unit, the generic neural network being configured to receive the sensor value as an input to extract a generic feature to be input in common into the object detection part and the semantic segmentation part.
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公开(公告)号：US20190286982A1
公开(公告)日：2019-09-19
申请号：US16318779
申请日：2017-07-20
Applicant: Denso IT Laboratory, Inc.
Inventor： Mitsuru Ambai
Abstract: A neural network apparatus (20) includes a storage unit (24) storing a neural network model, and an arithmetic unit (22) inputting input information into an input layer of the neural network and outputting an output layer. A weight matrix (W) of an FC layer of the neural network model is constituted by a product of a weight basis matrix (Mw) of integers and a weight coefficient matrix (Cw) of real numbers. In the FC layer, the arithmetic unit (22) uses an output vector from a previous layer as an input vector (x) to decompose the input vector (x) into a product of a binary input basis matrix (Mx) and an input coefficient vector (cx) of real numbers and an input bias (bx) and derives a product of the input vector (x) and a weight matrix (W).
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