JP5311793B2 - 脇見状態判定装置 - Google Patents
脇見状態判定装置 Download PDFInfo
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- JP5311793B2 JP5311793B2 JP2007269455A JP2007269455A JP5311793B2 JP 5311793 B2 JP5311793 B2 JP 5311793B2 JP 2007269455 A JP2007269455 A JP 2007269455A JP 2007269455 A JP2007269455 A JP 2007269455A JP 5311793 B2 JP5311793 B2 JP 5311793B2
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- 238000010586 diagram Methods 0.000 description 5
- 238000003708 edge detection Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
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- 238000013528 artificial neural network Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/02—Alarms for ensuring the safety of persons
- G08B21/06—Alarms for ensuring the safety of persons indicating a condition of sleep, e.g. anti-dozing alarms
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/25—Fusion techniques
- G06F18/251—Fusion techniques of input or preprocessed data
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/29—Graphical models, e.g. Bayesian networks
- G06F18/295—Markov models or related models, e.g. semi-Markov models; Markov random fields; Networks embedding Markov models
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
- G06V10/80—Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level
- G06V10/803—Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level of input or preprocessed data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/84—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using probabilistic graphical models from image or video features, e.g. Markov models or Bayesian networks
- G06V10/85—Markov-related models; Markov random fields
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/59—Context or environment of the image inside of a vehicle, e.g. relating to seat occupancy, driver state or inner lighting conditions
- G06V20/597—Recognising the driver's state or behaviour, e.g. attention or drowsiness
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- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Data Mining & Analysis (AREA)
- Software Systems (AREA)
- Multimedia (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Bioinformatics & Cheminformatics (AREA)
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- General Health & Medical Sciences (AREA)
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- Health & Medical Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Databases & Information Systems (AREA)
- Business, Economics & Management (AREA)
- Emergency Management (AREA)
- Probability & Statistics with Applications (AREA)
- Traffic Control Systems (AREA)
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
- Instructional Devices (AREA)
- Fittings On The Vehicle Exterior For Carrying Loads, And Devices For Holding Or Mounting Articles (AREA)
Description
10 観測量取得手段
11 脇見状態学習手段
12 脇見状態判定手段
15 記憶部
20 道路撮影用カメラ
21 運転者撮影用カメラ
22 操舵角センサ
23 アクセル踏み込み量センサ
24 ブレーキ踏み込み量センサ
25 方向指示器
26 速度センサ
27 加速度センサ
28 ナビゲーション装置
29 入力装置
30 音声出力装置
100 脇見状態判定装置
150 遷移モデル
151 観測モデル
DR 被験者
DS ドライビングシミュレータ
MC 検証用カメラ
P1、P2 プロジェクタ
PG 投影画像
SC スクリーン
TG ターゲット画像
VC 車両模型
W1、W2 壁面
Claims (8)
- 運転者の視線に関する情報と、運転操作に関する情報及び走行状態に関する情報のうちの少なくとも一つとを観測量として取得する観測量取得手段と、
所定の脇見発生状態と観測量との間の確率的関係及び前記観測量取得手段が取得した観測量に基づいて該所定の脇見発生状態が脇見であるか否かを判定する脇見状態判定手段と、
を備え、
前記所定の脇見発生状態は、少なくとも1種類の観測量の現在時刻における値を用いて定義され、
前記脇見状態判定手段は、運転者が脇見をしている確率を算出し、算出した確率が閾値を超えた場合で、且つ、前記所定の脇見発生状態が検出されている場合に、運転者が脇見をしていると判定する、
ことを特徴とする脇見状態判定装置。 - 前記脇見状態判定手段は、ベイジアンネットワークを用いて運転者が脇見をしている確率を算出する、
ことを特徴とする請求項1に記載の脇見状態判定装置。 - 前記脇見状態判定手段は、遷移モデル及び観測モデルを事前に学習し、前記遷移モデル及び前記観測モデルと前記観測量取得手段が取得した観測量とに基づいて運転者が脇見をしている確率を算出する、
ことを特徴とする請求項1又は2に記載の脇見状態判定装置。 - 前記運転操作に関する情報は、操舵角、アクセル踏み込み量、ブレーキ踏み込み量又はウィンカー操作に関する情報を含む、
ことを特徴とする請求項1乃至3に記載の脇見状態判定装置。 - 前記走行状態に関する情報は、車両の速度又は加速度を含む、
ことを特徴とする請求項1乃至3に記載の脇見状態判定装置。 - 前記走行状態に関する情報は、車両とセンターラインとの間の距離又は車両とセンターラインとの間の角度を含む、
ことを特徴とする請求項1乃至3に記載の脇見状態判定装置。 - 前記走行状態に関する情報は、周囲環境に関する情報を含む、
ことを特徴とする請求項1乃至3に記載の脇見状態判定装置。 - 前記運転者の視線に関する情報は、頭部の傾斜角度を含む、
ことを特徴とする請求項1乃至3に記載の脇見状態判定装置。
Priority Applications (4)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
JP2007269455A JP5311793B2 (ja) | 2007-10-16 | 2007-10-16 | 脇見状態判定装置 |
US12/738,576 US8665099B2 (en) | 2007-10-16 | 2008-10-16 | Inattentive state determination device and method of determining inattentive state |
PCT/IB2008/002739 WO2009050564A2 (en) | 2007-10-16 | 2008-10-16 | Inattentive state determination device and method of determining inattentive state |
EP08840164.1A EP2201496B1 (en) | 2007-10-16 | 2008-10-16 | Inattentive state determination device and method of determining inattentive state |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
JP2007269455A JP5311793B2 (ja) | 2007-10-16 | 2007-10-16 | 脇見状態判定装置 |
Publications (2)
Publication Number | Publication Date |
---|---|
JP2009098900A JP2009098900A (ja) | 2009-05-07 |
JP5311793B2 true JP5311793B2 (ja) | 2013-10-09 |
Family
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Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
JP2007269455A Active JP5311793B2 (ja) | 2007-10-16 | 2007-10-16 | 脇見状態判定装置 |
Country Status (4)
Country | Link |
---|---|
US (1) | US8665099B2 (ja) |
EP (1) | EP2201496B1 (ja) |
JP (1) | JP5311793B2 (ja) |
WO (1) | WO2009050564A2 (ja) |
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-
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2008
- 2008-10-16 WO PCT/IB2008/002739 patent/WO2009050564A2/en active Application Filing
- 2008-10-16 EP EP08840164.1A patent/EP2201496B1/en active Active
- 2008-10-16 US US12/738,576 patent/US8665099B2/en active Active
Also Published As
Publication number | Publication date |
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JP2009098900A (ja) | 2009-05-07 |
EP2201496B1 (en) | 2018-05-23 |
US8665099B2 (en) | 2014-03-04 |
EP2201496A2 (en) | 2010-06-30 |
US20100265074A1 (en) | 2010-10-21 |
WO2009050564A3 (en) | 2009-09-03 |
WO2009050564A8 (en) | 2010-07-15 |
WO2009050564A2 (en) | 2009-04-23 |
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