JPWO2019180551A5 - - Google Patents
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- JPWO2019180551A5 JPWO2019180551A5 JP2021500364A JP2021500364A JPWO2019180551A5 JP WO2019180551 A5 JPWO2019180551 A5 JP WO2019180551A5 JP 2021500364 A JP2021500364 A JP 2021500364A JP 2021500364 A JP2021500364 A JP 2021500364A JP WO2019180551 A5 JPWO2019180551 A5 JP WO2019180551A5
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Claims (23)
前記被害を受け得る車道ユーザーのうちの1の前記被害を受け得る車道ユーザーが前記横断歩道内の前記道路または前記横断歩道の近くの前記道路に入ろうとしていることの予測を行うために、前記横断歩道の近傍に位置する機器において動作する機械学習モデルに、生成された前記動作データを適用することであって、前記予測が、前記被害を受け得る車道ユーザーが前記横断歩道内の前記道路または前記横断歩道の近くの前記道路に入る前に行われ、前記機械学習モデルが、前記横断歩道の近傍において生成された動作データであって、以前に前記横断歩道内にいた、または前記横断歩道の近くにいた被害を受け得る車道ユーザーの意図または挙動を示す前記動作データを使用して訓練されたものであり、前記動作データが、位置、速度、加速度、および配向を含む、適用することと、
前記被害を受け得る車道ユーザーが前記横断歩道内の前記道路または前記横断歩道の近くの前記道路に入る前に、前記被害を受け得る車道ユーザーに関連したデバイスと、前記道路上の前記横断歩道に接近している他の地上交通エンティティに関連したデバイスと、前記被害を受け得る車道ユーザーまたは運転手に警報を出すように構成された道路標識とのうちの少なくとも1つに、警告を送信することと、
を含む、方法。 To monitor an area within a pedestrian crossing and an area near a pedestrian crossing is to use an electronic sensor located near the pedestrian crossing across the road, wherein the electronic sensor is said to be said. Generates motion data for a vulnerable road user in a pedestrian crossing or a vulnerable road user near the pedestrian crossing , and the motion data determines the location and direction of the vulnerable road user. Including , using and
To make a prediction that one of the vulnerable roadway users is about to enter the road in the pedestrian crossing or the road near the pedestrian crossing. By applying the generated motion data to a machine learning model operating in a device located near the pedestrian crossing, the prediction is that the vulnerable roadway user is the road in the pedestrian crossing or the road. Performed prior to entering the road near the pedestrian crossing, the machine learning model is motion data generated in the vicinity of the pedestrian crossing that was previously in or near the pedestrian crossing. Trained using the motion data to indicate the intent or behavior of a pedestrian crossing user who may have suffered damage, the motion data includes, application, position, speed, acceleration, and orientation .
Before the vulnerable road user enters the road in the pedestrian crossing or the road near the pedestrian crossing , the device associated with the vulnerable roadway user and the pedestrian crossing on the road. Sends a warning to at least one of the devices associated with other nearby ground traffic entities and road signs configured to alert the vulnerable roadway user or driver . That and
Including, how.
請求項1に記載の方法。 The vulnerable roadway users include pedestrians, animals, or cyclists.
The method according to claim 1 .
請求項1に記載の方法。 The device associated with the vulnerable roadway user includes a smartwatch or other wearable device, a smartphone, or another mobile device.
The method according to claim 1 .
請求項1に記載の方法。 The other ground transportation entity, including a motor-driven vehicle,
The method according to claim 1 .
請求項1に記載の方法。 The device associated with the other ground transportation entity includes a smartphone or another mobile device.
The method according to claim 1 .
請求項1に記載の方法。 The machine learning model is provided by a remote server over the Internet to the device located in the vicinity of the pedestrian crossing.
The method according to claim 1 .
請求項1に記載の方法。 The machine learning model is generated in the device located near the pedestrian crossing.
The method according to claim 1 .
請求項1に記載の方法。 Including training the machine learning model using motion data generated by the sensor located in the vicinity of the pedestrian crossing.
The method according to claim 1 .
請求項1に記載の方法。 For use in training the machine learning model, the server comprises transmitting motion data generated by the sensor located in the vicinity of the pedestrian crossing.
The method according to claim 1 .
請求項1に記載の方法。 It comprises segmenting the motion data generated by the sensor located in the vicinity of the pedestrian crossing based on the corresponding zone in the vicinity of the pedestrian crossing.
The method according to claim 1 .
請求項1に記載の方法。 Including the use of the electronic sensors to generate behavior-related data representing the physical properties of the vulnerable roadway user.
The method according to claim 1 .
請求項1に記載の方法。 Includes deriving track information about the vulnerable roadway user from the motion data generated by the sensor.
The method according to claim 1 .
前記平面交差点が、道路の交差点、横断歩道、および鉄道線路を含み、
前記機器が、
前記平面交差点における道路車両および歩行者または前記平面交差点付近における道路車両および歩行者を監視するように配向されたセンサーからデータを受信することと、前記道路上の信号および前記鉄道線路上の信号に対するフェーズおよびタイミングデータを受信することとをする入力と、
前記平面交差点における危険な状況または前記平面交差点付近における危険な状況に関する警告を、地上交通エンティティ、歩行者、または前記鉄道線路上の鉄道車両のうちの1つのデバイスに送信する無線通信デバイスと、
プロセッサと、
ストレージと、
を備え、
前記ストレージが、
現時点における前記平面交差点における地上交通エンティティまたは前記平面交差点付近における地上交通エンティティの挙動を予測することができる機械学習モデルを記憶することであって、前記機械学習モデルが、前記交差点における道路車両および歩行者、または、前記交差点付近における道路車両および歩行者の以前の動作および関連する挙動に関する訓練データと、前記道路上の信号および前記鉄道線路上の信号に対する以前のフェーズおよびタイミングデータとに基づいている、記憶することと、
前記平面交差点における道路車両および歩行者または前記平面交差点付近における道路車両および歩行者に関する、前記センサーから受信された現在の動作データと、前記道路上の信号および前記鉄道線路上の信号に対する現在のフェーズおよびタイミングデータとを、前記道路車両および前記歩行者の切迫した挙動を予測するために、前記機械学習モデルに適用することと、
予測された前記切迫した挙動から、前記交差点における前記鉄道線路上の鉄道車両、または、前記交差点付近における前記鉄道線路上の鉄道車両に対する切迫した危険な状況を推測することと、
前記道路車両、前記歩行者、および前記鉄道車両のうちの少なくとも1つのデバイスに、前記危険な状況に関する前記警告を送信することを前記無線通信デバイスにさせることと、
をするための、前記プロセッサにより実行可能な命令のための前記ストレージである、
装置。 A device equipped with equipment located at a plane intersection of a transportation network.
The plane intersection includes road intersections, pedestrian crossings, and railroad tracks.
The device
Receiving data from sensors oriented to monitor road vehicles and pedestrians at or near the intersection, and for signals on the road and signals on the railroad tracks. With input to receive phase and timing data,
A wireless communication device that transmits a warning about a dangerous situation at the intersection or near the intersection to a device of a ground transportation entity, a pedestrian, or a railroad vehicle on the railroad track.
With the processor
Storage and
Equipped with
The storage is
It is to store a machine learning model capable of predicting the behavior of the ground traffic entity at the plane intersection or the ground traffic entity near the plane intersection at the present time, wherein the machine learning model is a road vehicle and a pedestrian at the intersection. Based on training data on previous movements and related behaviors of road vehicles and pedestrians near a person or the intersection, and previous phase and timing data for signals on the road and signals on the railroad track . , Remembering,
Current motion data received from the sensor for road vehicles and pedestrians at the intersection or road vehicles and pedestrians near the intersection , and current phases for signals on the road and signals on the railroad track. And timing data is applied to the machine learning model to predict the imminent behavior of the road vehicle and the pedestrian.
From the predicted imminent behavior, it is possible to infer an imminent and dangerous situation for a railroad vehicle on the railroad track at the intersection or a railroad vehicle on the railroad track near the intersection.
To have the wireless communication device transmit the warning regarding the dangerous situation to at least one device of the road vehicle, the pedestrian, and the railroad vehicle.
The storage for instructions that can be executed by the processor.
Device.
請求項13に記載の装置。 The warning is transmitted to the in-vehicle device of the railway vehicle,
The device according to claim 13 .
請求項13に記載の装置。 The railroad track is on a separate railroad site,
The device according to claim 13 .
請求項13に記載の装置。 The railroad track is not on a separate railroad site,
The device according to claim 13 .
請求項13に記載の装置。 The equipment includes equipment along the road,
The device according to claim 13 .
請求項13に記載の装置。 Said for reception by any of the ground traffic entity, the pedestrian, or the railcar at the plane intersection, or the ground traffic entity, the pedestrian, or the railcar near the plane intersection. By broadcasting the warning, the warning is transmitted,
The device according to claim 13 .
請求項13に記載の装置。 The imminent and dangerous situation includes a collision or near miss,
The device according to claim 13 .
前記道路車両および前記歩行者の状態に関する、基本的な安全メッセージまたは仮想的な基本的な安全メッセージと個人用安全メッセージと仮想的な個人用安全メッセージとにおけるデータを受信することであって、前記基本的な安全メッセージと前記仮想的な基本的な安全メッセージと前記個人用安全メッセージと前記仮想的な個人用安全メッセージとが、前記道路車両または前記歩行者に関する位置、進行方向、および速度情報を含み、前記基本的な安全メッセージと前記個人用安全メッセージとが、前記道路車両または前記歩行者から受信され、前記仮想的な基本的な安全メッセージと前記仮想的な個人用安全メッセージとが、前記インフラストラクチャーセンサーから受信された前記データから再構成された前記道路車両と前記歩行者とに関する位置、進行方向、および速度情報を含む、受信することと、
前記道路車両または前記歩行者のうちの1つの危険な運転または歩行挙動を識別するために、訓練された機械学習モデルに、前記インフラストラクチャーセンサーから受信された前記データを、および、前記基本的な安全メッセージと前記仮想的な基本的な安全メッセージと前記個人用安全メッセージと前記仮想的な個人用安全メッセージとからのデータを適用することと、
当局に、前記危険な運転または歩行挙動を自動的に報告することと、
を含む、方法。 Receiving data from infrastructure sensors that represent the location and behavior of driving road vehicles or walking pedestrians in the ground transportation network.
Receiving data in a basic safety message or a virtual basic safety message and a personal safety message and a virtual personal safety message relating to the condition of the road vehicle and the pedestrian . The basic safety message, the virtual basic safety message, the personal safety message, and the virtual personal safety message provide position, direction, and speed information regarding the road vehicle or the pedestrian. Including, the basic safety message and the personal safety message are received from the road vehicle or the pedestrian, and the virtual basic safety message and the virtual personal safety message are the said. Receiving, including position, direction of travel, and speed information about the road vehicle and the pedestrian reconstructed from the data received from the infrastructure sensor .
The data received from the infrastructure sensor in a trained machine learning model to identify dangerous driving or walking behavior of the road vehicle or one of the pedestrians , and the basic. Applying the data from the safety message, the virtual basic safety message, the personal safety message, and the virtual personal safety message ,
To automatically report the dangerous driving or walking behavior to the authorities,
Including, how.
請求項20に記載の方法。 Including identifying the road vehicle based on plate number recognition,
The method according to claim 20 .
請求項20に記載の方法。 Including identifying the pedestrian based on biometric recognition,
The method according to claim 20 .
請求項20に記載の方法。 Including identifying the road vehicle or the pedestrian based on social networking,
The method according to claim 20 .
Priority Applications (1)
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JP2023131721A JP2023156430A (en) | 2018-03-19 | 2023-08-11 | Early warning and collision avoidance |
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US201862644725P | 2018-03-19 | 2018-03-19 | |
US62/644,725 | 2018-03-19 | ||
US15/994,702 | 2018-05-31 | ||
US15/994,915 | 2018-05-31 | ||
US15/994,568 | 2018-05-31 | ||
US15/994,826 | 2018-05-31 | ||
US15/994,850 US11257370B2 (en) | 2018-03-19 | 2018-05-31 | Early warning and collision avoidance |
US15/994,702 US11749111B2 (en) | 2018-03-19 | 2018-05-31 | Early warning and collision avoidance |
US15/994,850 | 2018-05-31 | ||
US15/994,915 US20190287394A1 (en) | 2018-03-19 | 2018-05-31 | Early warning and collision avoidance |
US15/994,826 US10235882B1 (en) | 2018-03-19 | 2018-05-31 | Early warning and collision avoidance |
US15/994,568 US10950130B2 (en) | 2018-03-19 | 2018-05-31 | Early warning and collision avoidance |
US16/222,536 | 2018-12-17 | ||
US16/222,536 US10565880B2 (en) | 2018-03-19 | 2018-12-17 | Early warning and collision avoidance |
PCT/IB2019/052064 WO2019180551A1 (en) | 2018-03-19 | 2019-03-14 | Early warning and collision avoidance |
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JPWO2019180551A5 true JPWO2019180551A5 (en) | 2022-03-17 |
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JP2023131721A Pending JP2023156430A (en) | 2018-03-19 | 2023-08-11 | Early warning and collision avoidance |
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EP (1) | EP3753001A1 (en) |
JP (2) | JP7362721B2 (en) |
CN (2) | CN112154492A (en) |
CA (1) | CA3094275A1 (en) |
SA (1) | SA520420162B1 (en) |
SG (1) | SG11202009104UA (en) |
WO (1) | WO2019180551A1 (en) |
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