JP7089832B2 - 状態推定器 - Google Patents
状態推定器 Download PDFInfo
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- JP7089832B2 JP7089832B2 JP2020529361A JP2020529361A JP7089832B2 JP 7089832 B2 JP7089832 B2 JP 7089832B2 JP 2020529361 A JP2020529361 A JP 2020529361A JP 2020529361 A JP2020529361 A JP 2020529361A JP 7089832 B2 JP7089832 B2 JP 7089832B2
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/08—Interaction between the driver and the control system
- B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/049—Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/08—Interaction between the driver and the control system
- B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
- B60W2050/146—Display means
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2554/00—Input parameters relating to objects
- B60W2554/40—Dynamic objects, e.g. animals, windblown objects
- B60W2554/404—Characteristics
- B60W2554/4041—Position
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
- B62D—MOTOR VEHICLES; TRAILERS
- B62D15/00—Steering not otherwise provided for
- B62D15/02—Steering position indicators ; Steering position determination; Steering aids
- B62D15/025—Active steering aids, e.g. helping the driver by actively influencing the steering system after environment evaluation
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
- B62D—MOTOR VEHICLES; TRAILERS
- B62D15/00—Steering not otherwise provided for
- B62D15/02—Steering position indicators ; Steering position determination; Steering aids
- B62D15/029—Steering assistants using warnings or proposing actions to the driver without influencing the steering system
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
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Description
Claims (9)
- 自動車運転者支援システムであって、
自車両の周辺の状況を検出するセンサと;
回帰神経回路網(「RNN」)を有し、自車両の後続の状態を推定する状態推定器と;を備え、
前記RNNは、前記センサによる検出値と、現在の演算サイクルに先行する演算サイクルの結果である複数の第1の状態とに基づいて、後続する第2の状態を決定し、
前記RNNが、複数のフィードバック結合を含み、前記RNNが、前記現在の演算サイクルに先行する複数の先行する演算サイクルからの情報を使用するように構成されており、各先行する演算サイクルが、前記複数のフィードバック結合のうちの少なくとも1つに対応し、
前記第1の状態及び前記第2の状態が各々、前記自車両の近傍に位置する局所的物体を表す局所的物体属性を含み、
当該装置が、前記第2の状態に基づいて、自車両の動作に能動的に介在する能動的運転者支援デバイスを更に含むことを特徴とする自動車運転者支援システム。 - 前記装置が、前記運転者支援システムによって使用するために前記RNNから少なくとも1つの現実世界の属性を導出するように更に構成されている、請求項1に記載の装置。
- 前記RNNに接続された出力人工神経回路網(「ANN」)が、前記RNNから前記少なくとも1つの現実世界の属性を導出するように構成されている、請求項2に記載の装置。
- 前記第1の状態及び前記第2の状態が各々、前記自車両の運動の一態様を記述する少なくとも1つの自車両の属性を含む、請求項1~3のいずれか一項に記載の装置。
- 前記局所的物体が、前記自車両の近傍に位置する他の車両であることを特徴とする請求項4に記載の装置。
- 前記少なくとも1つの局所的物体属性が、前記局所的物体の位置を含む、請求項5に記載の装置。
- 前記第1の状態の前記局所的物体の第1の位置を使用して、前記推定された第2の状態の前記局所的物体の第2の位置を推定するように構成されている、請求項6に記載の装置。
- 前記第2の状態の測定値に対応する前記少なくとも1つの値が、前記局所的車両の前記第2の位置の測定値を含む、請求項7に記載の装置。
- 前記第1の状態及び前記第2の状態が各々、前記自車両の周辺環境を表す環境属性を含むことを特徴とする請求項1~8のいずれか一項に記載の装置。
Applications Claiming Priority (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
EP17208656.3A EP3502977A1 (en) | 2017-12-19 | 2017-12-19 | A state estimator |
EP17208656.3 | 2017-12-19 | ||
PCT/EP2018/082275 WO2019120865A1 (en) | 2017-12-19 | 2018-11-22 | A state estimator |
Publications (2)
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JP2021504222A JP2021504222A (ja) | 2021-02-15 |
JP7089832B2 true JP7089832B2 (ja) | 2022-06-23 |
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JP2020529361A Active JP7089832B2 (ja) | 2017-12-19 | 2018-11-22 | 状態推定器 |
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US (1) | US20200339146A1 (ja) |
EP (1) | EP3502977A1 (ja) |
JP (1) | JP7089832B2 (ja) |
WO (1) | WO2019120865A1 (ja) |
Families Citing this family (7)
Publication number | Priority date | Publication date | Assignee | Title |
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CA3118155A1 (en) * | 2018-12-11 | 2020-06-18 | SafeAI, Inc. | Techniques for kinematic and dynamic behavior estimation in autonomous vehicles |
DE102019128115B4 (de) * | 2019-10-17 | 2024-07-18 | Bayerische Motoren Werke Aktiengesellschaft | Fahrzeugmodell für Längsdynamik |
CN111062589B (zh) * | 2019-12-02 | 2022-08-16 | 武汉理工大学 | 一种基于目的地预测的城市出租车调度方法 |
KR20210129913A (ko) * | 2020-04-21 | 2021-10-29 | 주식회사 만도모빌리티솔루션즈 | 운전자 보조 시스템 |
CN113997947B (zh) * | 2021-10-27 | 2022-09-27 | 山西大鲲智联科技有限公司 | 驾驶信息提示方法、装置、电子设备和计算机可读介质 |
CN118176502A (zh) * | 2021-12-31 | 2024-06-11 | 华为技术有限公司 | 一种状态估计方法和装置 |
CN114312811B (zh) * | 2022-01-27 | 2023-11-07 | 清华大学 | 自动驾驶汽车的自车状态近似最优估计方法、装置及设备 |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2007223494A (ja) | 2006-02-24 | 2007-09-06 | Fuji Heavy Ind Ltd | 車両挙動推定予測装置および車両安定化制御システム |
JP2017154725A (ja) | 2015-04-21 | 2017-09-07 | パナソニックIpマネジメント株式会社 | 情報処理システム、情報処理方法、およびプログラム |
US20170286826A1 (en) | 2016-03-30 | 2017-10-05 | Nec Laboratories America, Inc. | Real-time deep learning for danger prediction using heterogeneous time-series sensor data |
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JP4093076B2 (ja) * | 2003-02-19 | 2008-05-28 | 富士重工業株式会社 | 車両運動モデルの生成装置および車両運動モデルの生成方法 |
WO2016145547A1 (en) * | 2015-03-13 | 2016-09-22 | Xiaoou Tang | Apparatus and system for vehicle classification and verification |
WO2016156236A1 (en) * | 2015-03-31 | 2016-10-06 | Sony Corporation | Method and electronic device |
KR20180094725A (ko) * | 2017-02-16 | 2018-08-24 | 삼성전자주식회사 | 자율 주행을 위한 차량 제어 방법, 차량 제어 장치 및 자율 주행을 위한 학습 방법 |
US10268191B1 (en) * | 2017-07-07 | 2019-04-23 | Zoox, Inc. | Predictive teleoperator situational awareness |
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2017
- 2017-12-19 EP EP17208656.3A patent/EP3502977A1/en not_active Ceased
-
2018
- 2018-11-22 WO PCT/EP2018/082275 patent/WO2019120865A1/en active Application Filing
- 2018-11-22 US US16/956,285 patent/US20200339146A1/en not_active Abandoned
- 2018-11-22 JP JP2020529361A patent/JP7089832B2/ja active Active
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2007223494A (ja) | 2006-02-24 | 2007-09-06 | Fuji Heavy Ind Ltd | 車両挙動推定予測装置および車両安定化制御システム |
JP2017154725A (ja) | 2015-04-21 | 2017-09-07 | パナソニックIpマネジメント株式会社 | 情報処理システム、情報処理方法、およびプログラム |
US20170286826A1 (en) | 2016-03-30 | 2017-10-05 | Nec Laboratories America, Inc. | Real-time deep learning for danger prediction using heterogeneous time-series sensor data |
Also Published As
Publication number | Publication date |
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EP3502977A1 (en) | 2019-06-26 |
US20200339146A1 (en) | 2020-10-29 |
JP2021504222A (ja) | 2021-02-15 |
WO2019120865A1 (en) | 2019-06-27 |
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