JP7440490B2 - 手続き的な世界の生成 - Google Patents
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Description
本PCT国際出願は、2018年10月17日に出願された米国特許出願第16/163,478号の優先権の継続および主張であり、これは、2018年8月9日に出願された「Procedural World and Agent Generation」と題する米国仮特許出願第62/716,839の優先権を主張する。また、本PCT国際出願は、2018年10月17日に出願された米国特許出願第16/163,466号の優先権の継続および主張である。前述のすべての出願の内容全体は、参照により本明細書に組み込まれる。
さらに、ある例示において、模擬環境に関連付けられるセンサーデータは、(例えば、オクルージョン、ノイズ、ドリフトなどに起因して)実際の環境に関連付けられるセンサーデータより正確である場合があり、それ故、模擬環境は、実際の環境に関連して得られた観測を検証することに用いられてよい。ある例示において、模擬環境は、(例えば、自律走行車に搭載された1つまたは複数のセンサーシステムの)較正に用いられてよい。上記のように、本明細書で説明される技術は、さまざまな状況において、模擬環境を生成することおよび模擬環境に用いられることに向けられている。
A.コンピュータ実装方法は、実際の環境内で複数のデータ収集デバイスからセンサーデータを受信することと、実際の環境に関連付けられる道路ネットワークデータを実際の環境に関連付けられる道路ネットワークデータにアクセスすることと、少なくとも部分的にセンサーデータに基づいて実際の環境に関連付けられる道路メッシュを生成することと、道路ネットワークデータを道路メッシュと統合させて、模擬環境を生成することと、格納されたオブジェクトのフットプリントのデータストレージにアクセスすることと、格納されたオブジェクトのフットプリントのデータストレージから格納されたオブジェクトのフットプリントを選択することと、格納されたオブジェクトのフットプリントに対応する少なくとも1つのオブジェクトを模擬環境へとレンダリングすることと、少なくとも1つのオブジェクトに関連付けられる表面の詳細をレンダリングすることと、模擬環境を自律ロボットコンピューティングデバイスによって用いられるアルゴリズムのテスト、認証、または訓練のうちの少なくとも1つのために、ナビゲーション、プラニング、または意思決定のうちの少なくとも1つに対して出力することとを備える。
本明細書で説明される技術の1つまたは複数の例示が記載されているが、さまざまな変更、追加、置換、およびそれらの均等物が本明細書で説明される技術の範囲内に含まれる。
Claims (14)
- コンピュータ実装方法であって、
実際の環境内で複数のデータ収集デバイスからセンサーデータを受信するステップと、
前記実際の環境に関連付けられる道路ネットワークデータであって、少なくとも部分的に前記環境に基づく前記道路ネットワークデータにアクセスするステップと、
少なくとも部分的に前記センサーデータに基づいて前記実際の環境に関連付けられるメッシュを決定するステップと、
前記道路ネットワークデータを前記メッシュに関連付けて、模擬環境を生成するステップであって、前記模擬環境は、前記実際の環境に対して不完全であるステップと、
前記実際の環境に関連付けられる補完データにアクセスするステップであって、前記補完データは、前記複数のデータ収集デバイスによって提供される情報とは異なる、前記実際の環境に関連付けられる情報を提供するステップと、
前記補完データの第1の部分と前記メッシュの第2の部分との間の誤差を決定するステップであって、前記第1の部分および前記第2の部分は、前記実際の環境の同一の領域に関連付けられているステップと、
前記誤差が誤差量の閾値を満たしているか、または超えているかを判断するステップと、
前記補完データまたは前記メッシュのうちの少なくとも1つを調整するステップと、
前記補完データを前記模擬環境に関連付けて、前記模擬環境を変更された模擬環境として補完するステップと、
前記変更された模擬環境を自律ロボットコンピューティングデバイスによって用いられるアルゴリズムのテスト、認証、または訓練のうちの少なくとも1つのために、ナビゲーション、プラニング、または意思決定のうちの少なくとも1つに対して出力するステップと
を備えるコンピュータ実装方法。 - 前記補完データは、ラスターベースの数値標高モデルを格納する地理空間ファイルフォーマットを含む、
請求項1に記載のコンピュータ実装方法。 - 前記地理空間ファイルフォーマットは、米国地質調査所(USGS)のデータ評価モデル(DEM)基準に関連付けられている、
請求項2に記載のコンピュータ実装方法。 - 前記模擬環境は、1つまたは複数のオブジェクトまたは前記環境の地形に関連付けられる前記センサーデータにおけるオクルージョンに起因して不完全である、
請求項1に記載のコンピュータ実装方法。 - 前記誤差は、前記実際の環境の同一の領域に関連付けられる平均の誤差のうちの少なくとも1つを含む、
請求項1ないし4のいずれか一項に記載のコンピュータ実装方法。 - 変形格子を前記補完データの少なくとも一部または前記メッシュの対応する部分の1つまたは複数に適用して、前記補完データと前記メッシュとを実質的に整列させ、誤差を低減させるステップをさらに含む、
請求項1ないし4のいずれか一項に記載のコンピュータ実装方法。 - 前記補完データは、格納されたオブジェクトのフットプリント、前記オブジェクトに関連付けられる高さ、前記オブジェクトに関連付けられる分類、または前記オブジェクトに関連付けられるテクスチャを示すルールセットのうちの少なくとも1つを含む、
請求項1ないし4のいずれか一項に記載のコンピュータ実装方法。 - 請求項1ないし7のいずれか一項に記載の方法を実行するように構成された命令を格納する1つまたは複数のコンピュータ可読媒体。
- システムであって、
少なくとも1つのプロセッサと、
前記少なくとも1つのプロセッサによって実行された場合に、前記少なくとも1つのプロセッサに、
実際の環境内で少なくとも1つのデータ収集デバイスからセンサーデータを受信することと、
前記実際の環境に関連付けられる道路ネットワークデータまたは前記実際の環境に関連付けられるメッシュのうちの少なくとも1つにアクセスすることであって、前記メッシュは、少なくとも部分的にセンサーデータに基づいていることと、
模擬環境を前記道路ネットワークデータまたは前記メッシュのうちの前記少なくとも1つに基づいて生成することと、
補完データの第1の部分と前記メッシュの第2の部分との間の誤差を決定することであって、前記第1の部分および前記第2の部分は、前記実際の環境の同一の領域に関連付けられていることと、
前記誤差が誤差量の閾値を満たしているか、または超えているかを判断することと、
前記補完データまたは前記メッシュのうちの少なくとも1つを調整することと、
前記補完データを前記模擬環境に関連付けて、変更された模擬環境を生成することと、
前記変更された模擬環境を自律ロボットコンピューティングデバイスによって用いられるアルゴリズムのテスト、認証、または訓練のうちの少なくとも1つのために、前記自律ロボットコンピューティングデバイスを制御する少なくとも1つに対して出力することと
を含む動作を実行させる1つまたは複数のコンピュータ可読命令と
を備えるシステム。 - 前記模擬環境は、前記実際の環境内で少なくとも1つのオクルージョンに起因して不完全である、
請求項9に記載のシステム。 - 前記補完データは、少なくとも1つのオクルージョンに起因して少なくとも1つのデータ収集デバイスが利用不可能である前記実際の環境に関連付けられる情報を提供する、
請求項9または10に記載のシステム。 - 前記道路ネットワークデータは、前記実際の環境の2次元表現を含み、且つ運転レーン要素、自転車レーン要素、駐車レーン要素、または横断歩道要素のうちの少なくとも1つの表示を含む、
請求項9ないし11のいずれか一項に記載のシステム。 - 前記動作は、前記道路ネットワークデータおよび前記メッシュにアクセスすることと、少なくとも部分的に前記道路ネットワークデータを前記メッシュへと投影することに基づいて、前記道路ネットワークデータおよび前記メッシュを関連させることとをさらに含む、
請求項9ないし12のいずれか一項に記載のシステム。 - 前記動作は、
前記補完データの第1の部分と前記メッシュの第2の部分との間の高さ誤差を測定することと、
前記高さ誤差が誤差量の閾値を満たすか、または超えるかを判断することと、
変形格子を前記第1の部分または前記第2の部分の1つまたは複数に適用して、高さ誤差を低減させることと
をさらに含む、
請求項9ないし13のいずれか一項に記載のシステム。
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Families Citing this family (47)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2018176000A1 (en) | 2017-03-23 | 2018-09-27 | DeepScale, Inc. | Data synthesis for autonomous control systems |
US11893393B2 (en) | 2017-07-24 | 2024-02-06 | Tesla, Inc. | Computational array microprocessor system with hardware arbiter managing memory requests |
US10671349B2 (en) | 2017-07-24 | 2020-06-02 | Tesla, Inc. | Accelerated mathematical engine |
US11157441B2 (en) | 2017-07-24 | 2021-10-26 | Tesla, Inc. | Computational array microprocessor system using non-consecutive data formatting |
US11409692B2 (en) | 2017-07-24 | 2022-08-09 | Tesla, Inc. | Vector computational unit |
US11599751B2 (en) * | 2017-12-28 | 2023-03-07 | Intel Corporation | Methods and apparatus to simulate sensor data |
US11561791B2 (en) | 2018-02-01 | 2023-01-24 | Tesla, Inc. | Vector computational unit receiving data elements in parallel from a last row of a computational array |
US11215999B2 (en) | 2018-06-20 | 2022-01-04 | Tesla, Inc. | Data pipeline and deep learning system for autonomous driving |
US11361457B2 (en) | 2018-07-20 | 2022-06-14 | Tesla, Inc. | Annotation cross-labeling for autonomous control systems |
US11636333B2 (en) | 2018-07-26 | 2023-04-25 | Tesla, Inc. | Optimizing neural network structures for embedded systems |
US11138350B2 (en) * | 2018-08-09 | 2021-10-05 | Zoox, Inc. | Procedural world generation using tertiary data |
US11562231B2 (en) | 2018-09-03 | 2023-01-24 | Tesla, Inc. | Neural networks for embedded devices |
US20200082561A1 (en) * | 2018-09-10 | 2020-03-12 | Mapbox, Inc. | Mapping objects detected in images to geographic positions |
US11030364B2 (en) * | 2018-09-12 | 2021-06-08 | Ford Global Technologies, Llc | Evaluating autonomous vehicle algorithms |
JP7040374B2 (ja) * | 2018-09-14 | 2022-03-23 | トヨタ自動車株式会社 | 物体検出装置、車両制御システム、物体検出方法及び物体検出用コンピュータプログラム |
US11132211B1 (en) * | 2018-09-24 | 2021-09-28 | Apple Inc. | Neural finite state machines |
CN115512173A (zh) | 2018-10-11 | 2022-12-23 | 特斯拉公司 | 用于使用增广数据训练机器模型的系统和方法 |
US11196678B2 (en) | 2018-10-25 | 2021-12-07 | Tesla, Inc. | QOS manager for system on a chip communications |
US11816585B2 (en) | 2018-12-03 | 2023-11-14 | Tesla, Inc. | Machine learning models operating at different frequencies for autonomous vehicles |
US11537811B2 (en) | 2018-12-04 | 2022-12-27 | Tesla, Inc. | Enhanced object detection for autonomous vehicles based on field view |
US11610117B2 (en) | 2018-12-27 | 2023-03-21 | Tesla, Inc. | System and method for adapting a neural network model on a hardware platform |
US11238370B2 (en) * | 2018-12-31 | 2022-02-01 | Woven Planet North America, Inc. | Approaches for determining sensor calibration |
US10997461B2 (en) | 2019-02-01 | 2021-05-04 | Tesla, Inc. | Generating ground truth for machine learning from time series elements |
US11567514B2 (en) | 2019-02-11 | 2023-01-31 | Tesla, Inc. | Autonomous and user controlled vehicle summon to a target |
US10956755B2 (en) | 2019-02-19 | 2021-03-23 | Tesla, Inc. | Estimating object properties using visual image data |
JP7115402B2 (ja) * | 2019-04-10 | 2022-08-09 | 株式会社デンソー | レーダ装置 |
DE102019205962A1 (de) * | 2019-04-25 | 2020-10-29 | Robert Bosch Gmbh | Verfahren zur Generierung von digitalen Bildpaaren als Trainingsdaten für Neuronale Netze |
US11379632B2 (en) * | 2019-05-28 | 2022-07-05 | Applied Intuition, Inc. | Simulated-driving-environment generation |
US11391257B2 (en) * | 2019-07-23 | 2022-07-19 | Ford Global Technologies, Llc | Power supply during vehicle startup |
US11529886B2 (en) | 2019-07-23 | 2022-12-20 | Ford Global Technologies, Llc | Power supply during vehicle off state |
US11625574B2 (en) * | 2019-10-28 | 2023-04-11 | MakinaRocks Co., Ltd. | Method for generating abnormal data |
US20210150799A1 (en) * | 2019-11-15 | 2021-05-20 | Waymo Llc | Generating Environmental Data |
US11820397B2 (en) * | 2019-11-16 | 2023-11-21 | Uatc, Llc | Localization with diverse dataset for autonomous vehicles |
JP7413011B2 (ja) * | 2019-12-27 | 2024-01-15 | キヤノンメディカルシステムズ株式会社 | 医用情報処理装置 |
US11765067B1 (en) * | 2019-12-28 | 2023-09-19 | Waymo Llc | Methods and apparatus for monitoring a sensor validator |
US11694089B1 (en) * | 2020-02-04 | 2023-07-04 | Rockwell Collins, Inc. | Deep-learned photorealistic geo-specific image generator with enhanced spatial coherence |
JP2023515476A (ja) * | 2020-02-21 | 2023-04-13 | エッジ ケース リサーチ,インコーポレイテッド | 知覚システムのための訓練データ候補の自動特定 |
KR102159052B1 (ko) * | 2020-05-12 | 2020-09-23 | 주식회사 폴라리스쓰리디 | 영상 분류 방법 및 장치 |
US11163921B1 (en) * | 2020-09-01 | 2021-11-02 | TeleqoTech | Managing a smart city |
CN112052549B (zh) * | 2020-09-09 | 2021-03-05 | 中国测绘科学研究院 | 一种小网眼聚集区道路选取方法 |
DE102021104110A1 (de) * | 2021-02-22 | 2022-08-25 | Dspace Gmbh | Verfahren zur Parametrierung einer Bildsynthese aus einem 3D-Modell |
WO2022272262A1 (en) * | 2021-06-24 | 2022-12-29 | Paypal, Inc. | Federated machine learning management |
JP7058434B1 (ja) * | 2021-07-07 | 2022-04-22 | 株式会社エクサウィザーズ | 生成方法、情報処理装置、プログラム、及び情報処理システム |
US20230084702A1 (en) * | 2021-09-10 | 2023-03-16 | Cyngn, Inc. | System and method of adaptive, real-time vehicle system identification for autonomous driving |
DE102021133968B4 (de) | 2021-12-21 | 2023-06-29 | Dspace Gmbh | Verfahren und Anordnung zum Parametrieren einer Emulationslogik |
US20240033631A1 (en) * | 2022-07-29 | 2024-02-01 | Niantic, Inc. | Maintaining object alignment in 3d map segments |
CN115394170B (zh) * | 2022-07-29 | 2024-05-03 | 北京城市网邻信息技术有限公司 | 电子沙盘构建方法、装置、电子设备及存储介质 |
Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2010191066A (ja) | 2009-02-17 | 2010-09-02 | Mitsubishi Electric Corp | 三次元地図補正装置及び三次元地図補正プログラム |
JP2016520882A (ja) | 2013-01-25 | 2016-07-14 | グーグル インコーポレイテッド | センサー検出不能場所及びセンサーの制限に基づく自律走行車両の動作の修正 |
Family Cites Families (99)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
ATE255241T1 (de) * | 1998-07-20 | 2003-12-15 | Procter & Gamble | Robotersystem |
US6587787B1 (en) * | 2000-03-15 | 2003-07-01 | Alpine Electronics, Inc. | Vehicle navigation system apparatus and method providing enhanced information regarding geographic entities |
US6941289B2 (en) | 2001-04-06 | 2005-09-06 | Sas Institute Inc. | Hybrid neural network generation system and method |
US6856830B2 (en) | 2001-07-19 | 2005-02-15 | Bin He | Method and apparatus of three dimension electrocardiographic imaging |
CN1549171A (zh) * | 2003-05-15 | 2004-11-24 | 季永萍 | 基于网格计算的高新技术市场界定标准的实现装置 |
CN2629653Y (zh) | 2003-05-30 | 2004-08-04 | 张艳春 | 人体前庭感觉功能稳定性测试仪 |
EP1725960A2 (en) * | 2004-03-10 | 2006-11-29 | Renault s.a.s. | A validation method for embedded systems |
US7660441B2 (en) * | 2004-07-09 | 2010-02-09 | Southern California, University | System and method for fusing geospatial data |
CN100561161C (zh) * | 2006-06-09 | 2009-11-18 | 大连理工大学 | 一种用于海洋平台的调谐质量阻尼器半实物仿真测试方法和系统 |
US20070288410A1 (en) * | 2006-06-12 | 2007-12-13 | Benjamin Tomkins | System and method of using genetic programming and neural network technologies to enhance spectral data |
US20080033645A1 (en) * | 2006-08-03 | 2008-02-07 | Jesse Sol Levinson | Pobabilistic methods for mapping and localization in arbitrary outdoor environments |
CN1916968A (zh) * | 2006-09-01 | 2007-02-21 | 上海大学 | 三维虚拟现实用矩阵实现模拟环境光照射的设置方法 |
US8341595B2 (en) * | 2007-05-30 | 2012-12-25 | Roam Data Inc | System and method for developing rich internet applications for remote computing devices |
CA2629653A1 (en) | 2008-05-30 | 2009-11-30 | Gerard Voon | Neural links/artificial intelligence/computer/multidimensional technologies iii |
US20100312736A1 (en) | 2009-06-05 | 2010-12-09 | The Regents Of The University Of California | Critical Branching Neural Computation Apparatus and Methods |
US8559673B2 (en) * | 2010-01-22 | 2013-10-15 | Google Inc. | Traffic signal mapping and detection |
JP5440219B2 (ja) * | 2010-01-29 | 2014-03-12 | 株式会社デンソー | 地図データ及び地図データ作成方法 |
CN101794459A (zh) * | 2010-02-09 | 2010-08-04 | 北京邮电大学 | 一种立体视觉影像与三维虚拟物体的无缝融合方法 |
CN101887379B (zh) * | 2010-06-18 | 2013-03-06 | 北京航空航天大学 | 一种基于虚拟网卡的无线信道仿真方法 |
US8559976B2 (en) * | 2010-11-09 | 2013-10-15 | Ntt Docomo, Inc. | System and method for population tracking, counting, and movement estimation using mobile operational data and/or geographic information in mobile network |
JP5402957B2 (ja) * | 2011-02-09 | 2014-01-29 | 株式会社デンソー | 電子機器 |
US9736466B2 (en) * | 2011-05-27 | 2017-08-15 | Zspace, Inc. | Optimizing stereo video display |
US20160189544A1 (en) * | 2011-11-16 | 2016-06-30 | Autoconnect Holdings Llc | Method and system for vehicle data collection regarding traffic |
GB201204657D0 (en) * | 2011-11-18 | 2012-05-02 | Tomtom North America Inc | Methods for providing 3D building information |
US20130162665A1 (en) * | 2011-12-21 | 2013-06-27 | James D. Lynch | Image view in mapping |
US8706393B2 (en) | 2012-01-10 | 2014-04-22 | Ford Global Technologies, Llc | Intersection collision avoidance with adaptable vehicle dimensions |
US9122270B2 (en) * | 2012-01-13 | 2015-09-01 | Mitsubishi Electric Research Laboratories, Inc. | Hybrid adaptively sampled distance fields |
US9921069B2 (en) | 2012-04-05 | 2018-03-20 | Hitachi, Ltd. | Map data creation device, autonomous movement system and autonomous movement control device |
US9171464B2 (en) * | 2012-06-10 | 2015-10-27 | Apple Inc. | Encoded representation of route data |
US9989650B2 (en) | 2013-02-13 | 2018-06-05 | Here Global B.V. | Position in urban canyons |
US9141107B2 (en) | 2013-04-10 | 2015-09-22 | Google Inc. | Mapping active and inactive construction zones for autonomous driving |
CN104219074A (zh) * | 2013-05-31 | 2014-12-17 | 成都勤智数码科技股份有限公司 | 数据中心动态可视化系统 |
US9412173B2 (en) | 2013-06-21 | 2016-08-09 | National University Of Ireland, Maynooth | Method for mapping an environment |
US9719801B1 (en) * | 2013-07-23 | 2017-08-01 | Waymo Llc | Methods and systems for calibrating sensors using road map data |
US9786178B1 (en) | 2013-08-02 | 2017-10-10 | Honda Motor Co., Ltd. | Vehicle pedestrian safety system and methods of use and manufacture thereof |
CN103559541A (zh) * | 2013-10-30 | 2014-02-05 | 南京邮电大学 | 一种大数据中面向乱序数据流的反向传播方法 |
CN103593514B (zh) * | 2013-10-30 | 2016-06-01 | 中国运载火箭技术研究院 | 多谱段合成环境模拟系统 |
EP3092590A4 (en) * | 2014-01-07 | 2017-11-01 | Stephen L. Thaler | Device and method for the autonomous bootstrapping of unified sentience |
US9720410B2 (en) * | 2014-03-03 | 2017-08-01 | Waymo Llc | Remote assistance for autonomous vehicles in predetermined situations |
US9547989B2 (en) | 2014-03-04 | 2017-01-17 | Google Inc. | Reporting road event data and sharing with other vehicles |
CN110375755B (zh) | 2014-03-15 | 2023-11-28 | 城市引擎公司 | 用于高度定制的交互式移动地图的解决方案 |
CN104951836A (zh) * | 2014-03-25 | 2015-09-30 | 上海市玻森数据科技有限公司 | 基于神经网络技术的发帖预测系统 |
US9996976B2 (en) * | 2014-05-05 | 2018-06-12 | Avigilon Fortress Corporation | System and method for real-time overlay of map features onto a video feed |
US9475422B2 (en) * | 2014-05-22 | 2016-10-25 | Applied Invention, Llc | Communication between autonomous vehicle and external observers |
US9335764B2 (en) * | 2014-05-27 | 2016-05-10 | Recreational Drone Event Systems, Llc | Virtual and augmented reality cockpit and operational control systems |
KR101593637B1 (ko) * | 2014-08-19 | 2016-02-15 | 재단법인 대구경북과학기술원 | 근육 세포 활성화 모델링 시스템 및 그 방법 |
US9878155B1 (en) | 2015-01-05 | 2018-01-30 | Hrl Laboratories, Llc | Method for neurostimulation enhanced team performance |
US11370422B2 (en) * | 2015-02-12 | 2022-06-28 | Honda Research Institute Europe Gmbh | Method and system in a vehicle for improving prediction results of an advantageous driver assistant system |
KR20160112186A (ko) * | 2015-03-18 | 2016-09-28 | 삼성전자주식회사 | 뉴럴 네트워크에서 이벤트에 기반한 학습 방법 및 장치 |
US9135559B1 (en) * | 2015-03-20 | 2015-09-15 | TappingStone Inc. | Methods and systems for predictive engine evaluation, tuning, and replay of engine performance |
US10026222B1 (en) * | 2015-04-09 | 2018-07-17 | Twc Patent Trust Llt | Three dimensional traffic virtual camera visualization |
CN104850684A (zh) * | 2015-04-17 | 2015-08-19 | 江苏物联网研究发展中心 | 针对vissim的路网参数校正方法 |
US20160342861A1 (en) * | 2015-05-21 | 2016-11-24 | Mitsubishi Electric Research Laboratories, Inc. | Method for Training Classifiers to Detect Objects Represented in Images of Target Environments |
KR101645689B1 (ko) * | 2015-06-26 | 2016-08-05 | (주)네모파트너즈엔이씨 | 스마트 전력수요자원 네모모델링데이터 시뮬레이션 모듈을 통한 전력수요관리사업 프로젝트 경제성 분석장치 및 방법 |
US10169917B2 (en) * | 2015-08-20 | 2019-01-01 | Microsoft Technology Licensing, Llc | Augmented reality |
WO2017035663A1 (en) * | 2015-09-03 | 2017-03-09 | Miovision Technologies Incorporated | System and method for detecting and tracking objects |
US9898869B2 (en) * | 2015-09-09 | 2018-02-20 | Microsoft Technology Licensing, Llc | Tactile interaction in virtual environments |
WO2017079341A2 (en) * | 2015-11-04 | 2017-05-11 | Zoox, Inc. | Automated extraction of semantic information to enhance incremental mapping modifications for robotic vehicles |
US9632502B1 (en) | 2015-11-04 | 2017-04-25 | Zoox, Inc. | Machine-learning systems and techniques to optimize teleoperation and/or planner decisions |
US10496766B2 (en) * | 2015-11-05 | 2019-12-03 | Zoox, Inc. | Simulation system and methods for autonomous vehicles |
US9630619B1 (en) * | 2015-11-04 | 2017-04-25 | Zoox, Inc. | Robotic vehicle active safety systems and methods |
JP7036732B2 (ja) * | 2015-11-04 | 2022-03-15 | ズークス インコーポレイテッド | 自律車両のためのシミュレーションシステムおよび方法 |
US10397019B2 (en) | 2015-11-16 | 2019-08-27 | Polysync Technologies, Inc. | Autonomous vehicle platform and safety architecture |
US10482380B2 (en) | 2015-12-30 | 2019-11-19 | Amazon Technologies, Inc. | Conditional parallel processing in fully-connected neural networks |
US9701307B1 (en) | 2016-04-11 | 2017-07-11 | David E. Newman | Systems and methods for hazard mitigation |
US10545229B2 (en) | 2016-04-22 | 2020-01-28 | Huawei Technologies Co., Ltd. | Systems and methods for unified mapping of an environment |
CN107315571B (zh) | 2016-04-27 | 2020-07-31 | 中科寒武纪科技股份有限公司 | 一种用于执行全连接层神经网络正向运算的装置和方法 |
US20170329332A1 (en) * | 2016-05-10 | 2017-11-16 | Uber Technologies, Inc. | Control system to adjust operation of an autonomous vehicle based on a probability of interference by a dynamic object |
CN109416873B (zh) * | 2016-06-24 | 2022-02-15 | 瑞士再保险有限公司 | 具有自动化风险控制系统的自主或部分自主机动车辆及其相应方法 |
US10140515B1 (en) | 2016-06-24 | 2018-11-27 | A9.Com, Inc. | Image recognition and classification techniques for selecting image and audio data |
CN106157339B (zh) * | 2016-07-05 | 2019-06-18 | 华南理工大学 | 基于低秩顶点轨迹子空间提取的动画网格序列压缩方法 |
US10210451B2 (en) | 2016-07-22 | 2019-02-19 | Alpine Electronics of Silicon Valley, Inc. | Neural network applications in resource constrained environments |
KR102518532B1 (ko) | 2016-11-11 | 2023-04-07 | 현대자동차주식회사 | 자율주행차량의 경로 결정장치 및 그 방법 |
KR20180060860A (ko) | 2016-11-29 | 2018-06-07 | 삼성전자주식회사 | 객체들 간의 충돌을 방지하는 충돌 방지 장치 및 방법 |
CN110062871B (zh) * | 2016-12-09 | 2024-01-19 | 通腾全球信息公司 | 用于基于视频的定位及映射的方法及系统 |
WO2018140969A1 (en) * | 2017-01-30 | 2018-08-02 | Google Llc | Multi-task neural networks with task-specific paths |
US10445928B2 (en) * | 2017-02-11 | 2019-10-15 | Vayavision Ltd. | Method and system for generating multidimensional maps of a scene using a plurality of sensors of various types |
CN106910139A (zh) * | 2017-02-22 | 2017-06-30 | 北京石油化工学院 | 一种煤矿突透水灾害应急疏散模拟方法 |
US20180247156A1 (en) | 2017-02-24 | 2018-08-30 | Xtract Technologies Inc. | Machine learning systems and methods for document matching |
CA2998249A1 (en) * | 2017-03-17 | 2018-09-17 | Edatanetworks Inc. | Artificial intelligence engine incenting merchant transaction with consumer affinity |
US10509692B2 (en) * | 2017-05-31 | 2019-12-17 | 2236008 Ontario Inc. | Loosely-coupled lock-step chaining |
CA3064771A1 (en) * | 2017-06-01 | 2018-12-06 | Royal Bank Of Canada | System and method for test generation |
US10268191B1 (en) | 2017-07-07 | 2019-04-23 | Zoox, Inc. | Predictive teleoperator situational awareness |
FR3069690A1 (fr) | 2017-07-25 | 2019-02-01 | Parrot Drones | Dispositif electronique et procede de generation, a partir d'au moins une paire d'images successives d'une scene, d'une carte de profondeur de la scene, drone et programme d'ordinateur associes |
US10831202B1 (en) * | 2017-09-01 | 2020-11-10 | Zoox, Inc. | Onboard use of scenario description language |
US10802485B2 (en) | 2017-10-09 | 2020-10-13 | Here Global B.V. | Apparatus, method and computer program product for facilitating navigation of a vehicle based upon a quality index of the map data |
RU2756439C1 (ru) * | 2017-10-24 | 2021-09-30 | Ниссан Норт Америка, Инк. | Определение локализации для работы транспортного средства |
GB2568087B (en) * | 2017-11-03 | 2022-07-20 | Imagination Tech Ltd | Activation functions for deep neural networks |
CN107943286B (zh) * | 2017-11-14 | 2023-02-28 | 国网山东省电力公司 | 一种增强漫游沉浸感的方法 |
CN108182650B (zh) * | 2017-12-22 | 2021-02-02 | 王金刚 | 一种城市空间北斗网格标识与仿真可视化系统 |
US10324467B1 (en) | 2017-12-29 | 2019-06-18 | Apex Artificial Intelligence Industries, Inc. | Controller systems and methods of limiting the operation of neural networks to be within one or more conditions |
US20190235521A1 (en) | 2018-02-01 | 2019-08-01 | GM Global Technology Operations LLC | System and method for end-to-end autonomous vehicle validation |
US10944767B2 (en) * | 2018-02-01 | 2021-03-09 | International Business Machines Corporation | Identifying artificial artifacts in input data to detect adversarial attacks |
US11829886B2 (en) | 2018-03-07 | 2023-11-28 | International Business Machines Corporation | Epistemic and aleatoric deep plasticity based on sound feedback |
CN108932289B (zh) | 2018-05-23 | 2021-10-15 | 北京华健蓝海医疗科技有限责任公司 | 一种基于信息抽取和深度学习的问题回答处理方法及系统 |
US10642275B2 (en) * | 2018-06-18 | 2020-05-05 | Zoox, Inc. | Occulsion aware planning and control |
US11514515B2 (en) * | 2018-07-17 | 2022-11-29 | Adobe Inc. | Generating synthetic data using reject inference processes for modifying lead scoring models |
US10783389B2 (en) | 2018-08-02 | 2020-09-22 | Denso International America, Inc. | Systems and methods for avoiding misrecognition of traffic signs and signals by hacking |
US11138350B2 (en) | 2018-08-09 | 2021-10-05 | Zoox, Inc. | Procedural world generation using tertiary data |
-
2018
- 2018-10-17 US US16/163,466 patent/US11138350B2/en active Active
- 2018-10-17 US US16/163,478 patent/US11068627B2/en active Active
- 2018-10-17 US US16/163,435 patent/US10832093B1/en active Active
-
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- 2019-08-08 JP JP2021506712A patent/JP7440490B2/ja active Active
- 2019-08-08 CN CN201980053514.4A patent/CN112639888A/zh active Pending
- 2019-08-08 WO PCT/US2019/045805 patent/WO2020033767A1/en unknown
-
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- 2020-10-15 US US17/071,955 patent/US11615223B2/en active Active
-
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- 2021-08-03 US US17/393,280 patent/US11861790B2/en active Active
-
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- 2024-02-15 JP JP2024021114A patent/JP2024055902A/ja active Pending
Patent Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2010191066A (ja) | 2009-02-17 | 2010-09-02 | Mitsubishi Electric Corp | 三次元地図補正装置及び三次元地図補正プログラム |
JP2016520882A (ja) | 2013-01-25 | 2016-07-14 | グーグル インコーポレイテッド | センサー検出不能場所及びセンサーの制限に基づく自律走行車両の動作の修正 |
Non-Patent Citations (1)
Title |
---|
Hyunchul Roh, et al.,Accurate Mobile Urban Mapping via Digital Map-Based SLAM,sensors,2016年08月18日,https://www.mdpi.com/1424-8220/16/8/1315 |
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