JP2019518287A - バーチャスサイクルを使用して自動車駐車スペース管理する方法及びシステム - Google Patents
バーチャスサイクルを使用して自動車駐車スペース管理する方法及びシステム Download PDFInfo
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Abstract
Description
本出願は、2016年3月13日出願の米国仮特許出願第62/349,468号である「インターネット、マシンラーニング、及びビッグデータを使用してインテリジェントネットワークを提供する方法及びシステム」に基づく優先権を主張し、その全体を参照して本明細書に組み込まれる。
Claims (20)
- 複数のセンサ、マシンラーニングセンタ、及びクラウドベースのネットワークを利用して自動車駐車スペース(APS)を管理するように構成された方法であって、
車両が動いているときに、前記車両の搭載された複数のセンサによって観察される周囲情報をモニタするステップと、
前記マシンラーニングセンタから受信したコンテナ化したAPSモデルによって生成されたリクエストに従って、前記周囲情報を選択的に記録するステップと、
前記車両が駐車状態にあるときに、APS、及びAPS周囲情報を検出するステップと、
前記APSの検出に至るまで、前記記録された周囲情報を巻き戻し、前記APS及び前記記録された周囲情報に応答して、前記APSに関連付けられたラベル付きデータを生成するステップと、
バーチャスサイクルを介して前記マシンラーニングセンタでのAPSモデルトレーニングを容易にするために、前記ラベル付けされたデータを前記クラウドベースのネットワークにアップロードするステップと、
を備える方法。 - 前記APSに関連する前記関連付けられたラベル付きデータを更新するために、前記ラベル付きデータを、前記クラウドベースのネットワークから取得した位置情報、タイムスタンプ、及び近隣トラフィック条件と関連付けるステップをさらに含む、請求項1に記載の方法。
- 前記APSに関連する前記関連付けしたラベル付きデータを更新するために、前記ラベル付きデータを、前記クラウドベースのネットワークから得られたローカルイベント、追加のサンプリングデータ、及び気象条件と関連付けるステップをさらに含む、請求項2に記載の方法。
- 前記クラウドベースのネットワークから前記マシンラーニングセンタに転送された前記関連付けされたラベル付きデータに従って前記コンテナ化したAPSモデルをトレーニングするステップをさらに含む、請求項3に記載の方法。
- 前記コンテナ化したAPSモデルを、無線通信ネットワークを通じて前記車両内の車載デジタル処理ユニットにプッシュするステップをさらに含む、請求項1に記載の方法。
- 前記車両に搭載された複数のセンサによって観察される周囲の情報をモニタするステップは、前記車両が動いているときに画像を取り込むために、前記車両に配置された外向きカメラを起動するステップを含む、請求項1に記載の方法。
- 前記周囲情報を選択的に記録するステップは、取り込まれた画像からAPS関連画像を識別するステップと、前記APS関連画像を前記車両の車載メモリデバイスに格納するステップとを含む、請求項1に記載の方法。
- 前記APS及び前記APS周囲情報を検出するステップは、駐車スペースを識別するステップを含む、請求項1に記載の方法。
- 前記APSの検出につながる記録された周囲の情報を巻き戻し、前記APSに関連付けられたラベル付きデータを生成するステップは、前記APSを検出する前に、記録された車両の動きに基づいて、前記APSの位置及び向きを識別するステップを含む、請求項1に記載の方法。
- 前記ラベル付きデータを前記クラウドベースのネットワークにアップロードするステップは、前記ラベル付きデータからリアルタイムデータを分離するステップと、前記リアルタイムデータを、無線通信ネットワークを通じてリアルタイムで前記クラウドベースのネットワークにアップロードするステップとを含む、請求項1に記載の方法。
- 前記ラベル付けされたデータを前記クラウドベースのネットワークにアップロードするステップは、前記ラベル付けされたデータからバッチ化されたデータを分離するステップと、前記バッチ化されたデータを後で前記クラウドベースのネットワークにアップロードするステップとを含む、請求項10に記載の方法。
- バーチャスサイクル内のマシンラーニングプロセスを容易にするために、前記ラベル付きデータを前記クラウドベースのネットワークにアップロードするステップは、
ラベル付きデータを関連付けし、修正するために、車両からクラウドベースのネットワークにリアルタイムラベル付きデータを供給するステップと、
改訂されたラベル付きデータを前記マシンラーニングセンタに転送し、前記APSモデルをトレーニングするステップと、
前記周囲情報を収集するために、トレーニングされたAPSモデルを車両にプッシュするステップと、
を含む請求項1に記載の方法。 - バーチャスサイクルを介して地域内の自動車駐車スペース(APS)を管理することができるネットワーク構成であって、
自動車が動いているときにAPSモデルによって生成される信号に従って、複数の搭載センサによって観察される周囲情報を収集し、選択的に記録するように構成されたコンテナ化センシングデバイスを含む自動車と、
コンテナ化センシングデバイスに無線で結合され、コンテナ化センシングデバイスから送信されたAPSクラウドデータの履歴データ及び周囲情報に基づいてAPSに関連付けられたラベル付きデータを関連付けし、生成するように構成されたクラウドベースのネットワークと、
クラウドベースのネットワークに接続され、クラウドベースのネットワークからのラベル付きデータに基づいてAPSモデルをトレーニングし、改善するように構成されたマシンラーニングセンタと、
を備えることを特徴とするネットワーク構成。 - 前記自動車が、地域を横切って移動するときにリアルタイム画像を収集するように構成された前向きカメラを含む、請求項13に記載のネットワーク構成。
- 前記自動車の前記コンテナ化センシングデバイスは、メモリと、コントローラと、送信機とを含み、前記メモリは、前記自動車に設置された前記前向きカメラによって収集されたリアルタイム画像の少なくとも一部を格納する、請求項14に記載のネットワーク構成。
- 前記クラウドベースのネットワークは、ラベル付きAPSクラウドデータを生成するために、前記自動車からのリアルタイムデータを、大きな自動車母集団を有する履歴サンプルと関連付ける、請求項13に記載のネットワーク構成。
- 前記マシンラーニングセンタは、前記クラウドベースのネットワークからの前記ラベル付けされたAPSクラウドデータに応答して前記APSモデルを訓練し、精密化されたAPSモデルを生成するように構成される、請求項16に記載のネットワーク構成。
- 前記自動車は、前記マシンラーニングセンタから前記精密化されたAPSモデルを受信し、前記精密化されたAPSモデルに基づいて周囲の情報を収集し始める、請求項17に記載のネットワーク構成。
- 複数のセンサ、マシンラーニングセンタ、及びクラウドベースのネットワークを利用して自動車駐車スペース(AP)を管理するように構成された方法であって、
複数の車載センサによって収集されたリアルタイムデータを、車両が走行しているときにAPSモデルに基づいて車両内部に位置するメモリに格納するステップと、
APSの位置に関連付けられた駐車イベントを識別するために、駐車状態におけるように車両が停止したときにAPSを検出し、格納されたリアルタイムデータの所定のセクションをメモリから取り出すステップと、
APS及び格納されたリアルタイムデータに応答して、APSに関連付けられたラベル付きデータを生成するステップと、
バーチャスサイクルを介してマシンラーニングプロセスにおけるモデルトレーニングを容易にするために、ラベル付けされたデータをクラウドベースのネットワークにアップロードするステップと、
を備える方法。 - 前記モデルトレーニングを容易にするために、前記ラベル付きデータを、前記クラウドベースのネットワークから得られた位置情報、タイムスタンプ、及び近隣トラフィック条件と関連付けることをさらに含む、請求項19に記載の方法。
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WO2017218595A1 (en) | 2017-12-21 |
EP3468469A4 (en) | 2020-02-19 |
US20210334558A1 (en) | 2021-10-28 |
WO2017218585A1 (en) | 2017-12-21 |
AU2017285130B2 (en) | 2022-04-21 |
US10956758B2 (en) | 2021-03-23 |
EP3469437A1 (en) | 2019-04-17 |
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