WO2023012049A1 - Procédé et système pour déterminer la direction d'un miroir de circulation - Google Patents

Procédé et système pour déterminer la direction d'un miroir de circulation Download PDF

Info

Publication number
WO2023012049A1
WO2023012049A1 PCT/EP2022/071315 EP2022071315W WO2023012049A1 WO 2023012049 A1 WO2023012049 A1 WO 2023012049A1 EP 2022071315 W EP2022071315 W EP 2022071315W WO 2023012049 A1 WO2023012049 A1 WO 2023012049A1
Authority
WO
WIPO (PCT)
Prior art keywords
traffic mirror
vehicle
traffic
mirror
placement angle
Prior art date
Application number
PCT/EP2022/071315
Other languages
English (en)
Inventor
Noriaki Itagaki
Yoshitomo ASAI
Original Assignee
Continental Autonomous Mobility Germany GmbH
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Continental Autonomous Mobility Germany GmbH filed Critical Continental Autonomous Mobility Germany GmbH
Priority to JP2024506705A priority Critical patent/JP2024528992A/ja
Publication of WO2023012049A1 publication Critical patent/WO2023012049A1/fr

Links

Classifications

    • 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
    • G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
    • G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
    • 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
    • G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/46—Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems
    • G08G1/167—Driving aids for lane monitoring, lane changing, e.g. blind spot detection

Definitions

  • the present disclosure is generally related to autonomous vehicles and specifically related to method and system for determining direction and angle of a traffic mirror using image analysis.
  • Traffic mirrors or road safety mirrors are used for enhancing traffic safety in streets, road junctions and curved or bent lanes. Traffic mirrors provide better visibility to blind spots on the roads and help prevent common hazards or accidents. In general, traffic mirrors benefit both drivers and pedestrians to see if there is any other vehicle coming from the opposite side.
  • ADAS Advanced Driver Assistance System
  • AD Autonomous Driving
  • aspects of the present disclosure provide methods and system for determining direction of a traffic mirror by analyzing images of the traffic mirror.
  • One aspect of this disclosure provides a method for determining the direction of the traffic mirror and comprises detecting a region of a traffic mirror from a real-time image of a field of view of a vehicle.
  • the real-time image of the field of view of the vehicle is captured by an image sensor in the vehicle, during movement of the vehicle.
  • the method comprises identifying one or more edges of the traffic mirror by processing the region of the traffic mirror detected in the real-time image.
  • the method comprises identifying a shape of the traffic mirror based on analysis of the one or more edges.
  • the method comprises measuring a placement angle of the traffic mirror using a predefined angle measurement technique corresponding to the shape of the traffic mirror. Subsequently, the method comprises determining the direction of the traffic mirror based on value of the placement angle of the traffic mirror.
  • the system comprises a processor and at least one memory coupled to the processor.
  • the at least one memory stores instructions executable by the processor and cause the processor to detect a region of a traffic mirror from a real-time image of a field of view of a vehicle.
  • the real-time image of the field of view of the vehicle is captured by an image sensor of the vehicle, during movement of the vehicle.
  • the processor is configured to identify one or more edges of the traffic mirror by processing the region of the traffic mirror detected in the real-time image. After identifying the one or more edges, the processor identifies a shape of the traffic mirror based on analysis of the one or more edges. Thereafter, the processor measures a placement angle of the traffic mirror using a predefined angle measurement technique corresponding to the shape of the traffic mirror. Finally, the processor determines the direction of the traffic mirror based on value of the placement angle of the traffic mirror.
  • Embodiments of the disclosure according to the above method and system may bring about several advantages.
  • the method and system of the disclosure are useful for determining a direction of the traffic mirrors on the roads.
  • direction and/or angle of the traffic mirror is an important input for Advanced Driver Assistance System (ADAS) and Autonomous Driving (AD) integrated vehicles for accurate risk prediction. Consequently, determining the direction of the traffic mirror enhances traffic safety and mitigates risk of traffic hazards or accidents.
  • ADAS Advanced Driver Assistance System
  • AD Autonomous Driving
  • FIG. 1 is an exemplary schematic diagram illustrating a vehicle capturing real-time image of a traffic mirror according to one embodiment of the present disclosure.
  • FIG. 2 shows a block diagram of a safety monitoring system for determining direction of a traffic mirror according to one embodiment of the present disclosure.
  • FIG. 3A - FIG. 3D illustrate sequence of steps involved in the method of determining direction of the traffic mirror according to one embodiment of the present disclosure.
  • FIG. 4A and 4B illustrate exemplary variations of the present disclosure according to alternative embodiments of the present disclosure.
  • FIG. 5 is a flow diagram illustrating an exemplary method for determining direction of the according to one embodiment of the present disclosure. It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and executed by a computer or processor, whether such computer or processor is explicitly shown.
  • FIG. 1 is an exemplary schematic diagram illustrating a method of determining direction of a traffic mirror 106 using a real-time image of the traffic mirror 106 according to one embodiment of the present disclosure.
  • the traffic mirror 106 may be a road safety mirror or a traffic safety mirror, which is placed at a predetermined location on a road 102 to avoid traffic collisions.
  • the real-time image of the traffic mirror 106 may be an image captured by a vehicle 104 in real-time, while the vehicle 104 is moving on the road 102.
  • the safety monitoring system of the present disclosure has been integrated to the vehicle 104 shown in FIG. 1.
  • the vehicle 104 is moving on the road 102, leading to a crossroad junction through which one or more other vehicles 110A and 110B pass by.
  • a traffic mirror 106 has been positioned at the crossroad junction, such that the traffic mirror 106 displays one or more other vehicles 110A, 110B converging and/or diverging from either sides of the road 102.
  • the one or more other vehicles 110A, 110B shown in FIG. 1 is for representation purpose only and these may include other types of obstacles and hazards on the road 102 including, without limitation, potholes, pedestrians, and the like.
  • the vehicle 104 may be, without limitation, an autonomous or selfdriving vehicle, an assisted driving vehicle with Advanced Driver-Assi stance Systems (ADAS). To avoid possibilities of collisions, accidents, or other types of hazards, it may be necessary for the vehicle 104 to read and analyze the reflections displayed on the traffic mirror 106. In addition, for accurately predicting position of an incoming vehicle or hazard, the vehicle 104 may need to estimate the right direction and the angle in which the traffic mirror 106 is placed on the road 102. For this purpose, the vehicle 104 may continuously capture real-time images of the traffic mirror 106, when the traffic mirror 106 appears in the Field of View (FOV) 108 of image sensors associated with the vehicle 104. In an embodiment, the vehicle 104 may capture the real-time images of the traffic mirror 106 using the image sensors configured at the front portion of the vehicle 104.
  • ADAS Advanced Driver-Assi stance Systems
  • the safety monitoring system on the vehicle 104 may perform one or more Artificial Intelligence (Al) and Deep Learning (DL) based operations on the real-time image of the traffic mirror 106.
  • a processor associated with the safety monitoring system may process the real-time image for detecting a region of a traffic mirror 106 in the real-time image.
  • the region of the traffic mirror 106 in the real-time image may be a portion of the real-time image representing the traffic mirror 106.
  • the processor may identify one or more edges of the traffic mirror 106 by further processing the region of the traffic mirror 106 detected in the real-time image.
  • the one or more edges of the traffic mirror 106 may represent a boundary of the traffic mirror 106 in the real-time image.
  • the processor may identify a shape of the traffic mirror 106 based on analysis of the one or more edges.
  • the processor may measure a placement angle of the traffic mirror 106 using a predefined angle measurement technique corresponding to the shape of the traffic mirror 106.
  • the processor may determine the direction of the traffic mirror 106 based on value of the placement angle of the traffic mirror 106.
  • the direction of the traffic mirror 106 may be used as an input for accurately predicting position, speed, and movement of one or other vehicles 110A, 110B on the road 102.
  • the direction information of the traffic mirror 106 may be used to carefully scan and estimate position and speed of the incoming vehicle (that is, one of the one or more other vehicles 110A, 110B) or other obstacles. Consequently, this helps in reducing processing load on autonomous path monitoring control units of the vehicle 104, since the control units may easily alter the course of driving based on the direction of the traffic mirror 106.
  • the direction information of the traffic mirror 106 may be used for alerting the driver of the vehicle 104 about position and speed of one or more other vehicles 110A, 110B. Subsequently, the course of the vehicle 104 may be suitably altered to mitigate any risks.
  • FIG. 2 shows a block diagram of a safety monitoring system 200 for determining direction of a traffic mirror 106 according to one embodiment of the present disclosure.
  • the safety monitoring system 200 may be configured within a vehicle 104 for determining direction of the traffic mirror 106 encountered during movement of the vehicle 104.
  • the safety monitoring system 200 may be remotely connected to the vehicle 104.
  • the safety monitoring system 200 may comprise, without limiting to, a processor 202, a memory 204 and an I/O interface 206.
  • the processor 202 may, for example, be a microcontroller or Graphics Processing Unit (GPU) capable of accessing the memory 204 to store information and execute instructions stored therein.
  • the processor 202 may be a part Engine Control Unit (ECU) in the vehicle 104.
  • the processor 202 and memory 204 may be integrated on a single integrated circuit.
  • the memory 204 stores information accessible by the processor 202 such as instructions executable by the processor 202 and data which may be stored, retrieved, or otherwise used by the processor 202.
  • the processor 202 may execute a method for determining direction of a traffic mirror 106 according to some embodiments of the instant disclosure based on instructions stored in the memory 204.
  • the data stored in the memory 204 may include, without limiting to, reference images of the one or more traffic mirrors, measured placement angle of the traffic mirror 106 and the like.
  • the I/O interface 206 of the safety monitoring system 200 may be used for interfacing the safety monitoring system 200 with one or more other components of the vehicle 104.
  • the safety monitoring system 200 may comprise one or more functional modules including an image sensor module 208, a region detection module 210, an edge detection module 212, a shape identification module 214, an angle measurement module 216 and a user interface 218.
  • each of the above modules may be communicatively coupled to each of the other modules via a Controller Area Network (CAN) bus. Further, each of the modules may be controller and supervised by the processor 202 based on the instructions and data stored in the memory 204 for determining the direction of the traffic mirror 106.
  • CAN Controller Area Network
  • the image sensor module 208 may comprise one or more image sensors.
  • the image sensors may be mounted externally at different parts of the vehicle 104. Further, the image sensors may be configured to continuously capture real-time images in the Field of View (FOV) 108 ahead of the vehicle 104, as long as the vehicle 104 is in movement. Alternatively, the image sensors may capture the real-time images only when a traffic mirror 106 has been detected in the FOV 108 of the vehicle 104.
  • the image sensors may, for example, be vision image sensors such as mono cameras or wide-angle fisheye cameras mounted at the front bumper of the vehicle 104.
  • the image sensor module 208 may comprise other types and numbers of image sensors than the ones mentioned above based on requirement of the vehicle 104.
  • the region detection module 210 may be configured for detecting a region of of the traffic mirror 106 from the real-time image of the FOV 108 of the vehicle 104.
  • the region detection module 210 may detect the region of the traffic mirror 106 using one or more predetermined computer vision techniques such as, single-object localization or object detection.
  • the region detection module 210 may use any other Artificial Intelligence (Al) and Deep Learning (DL) technique for localizing and detecting the region of the traffic mirror 106 in the real-time image.
  • Al Artificial Intelligence
  • DL Deep Learning
  • the input to the region detection module 210 may be a raw real-time image captured by the image sensor module 208 and the output of the region detection module 210 may be a processed image, highlighting the region of the traffic mirror 106 in the real-time image.
  • An exemplary output of the region detection module 210 is shown in image 302 of FIG. 3A.
  • the edge detection module 212 may be configured for identifying one or more edges of the traffic mirror 106 by processing the region of the traffic mirror 106 detected in the real-time image. In an embodiment, the edge detection module 212 may identify the one or more edges using a pretrained Convolution Neural Network (CNN). Alternatively, the edge detection module 212 may use any other Al and DL technique for identifying the one or more edges corresponding to the region of the traffic mirror 106. An exemplary output of the edge detection module 212 is shown in image 304 of FIG. 3B.
  • CNN Convolution Neural Network
  • the shape identification module 214 may be configured for identifying the shape of the traffic mirror 106 by analysing the one or more detected edges of the traffic mirror 106.
  • the shape identification module 214 may identify the shape by determining a basic geometric shape that may be formed using the one or more edges identified in the real-time image.
  • the shape of the traffic mirror 106 may be identified as one of circular, elliptical or polygonal.
  • the angle measurement module 216 may be configured for measuring a placement angle of the traffic mirror 106 based on analysis of the shape of the traffic mirror 106.
  • the placement angle may be the angle between the traffic mirror 106 and a horizontal axis representing the road 102 on which the vehicle 104 is moving.
  • the placement angle may be 90 degrees when the traffic mirror 106 is placed perpendicularly and/or exactly in an upright position on the road 102.
  • the placement angle may vary when the traffic mirror 106 is tilted and/or bent in a particular direction.
  • the angle measurement module 216 may use different angle measurement techniques for measuring the placement angle based on the identified shape of the traffic mirror 106.
  • the angle measurement technique used for measuring the placement angle of the traffic mirror 106 in an elliptical shape may be an existing technique such as, without limiting to, ‘findContours’ function of OpenCV®.
  • the angle measurement module 216 may use a predetermined auto angle compensation technique for handling any irregularities in the identified shape, before measuring the placement angle.
  • one of the predetermined auto angle compensation technique may be rotating the identified shape in a clockwise or anticlockwise direction by a predefined angle.
  • the user interface 218 may be used for displaying and/or notifying the determined direction and angle of the traffic mirror 106 to a driver or a passenger in the vehicle 104.
  • the user interface 218 may be used for communicating audio and/or visual messages to the driver/passenger of the vehicle 104.
  • the user interface 218 may comprise one or more components such as an instrument panel, an electronic display, and an audio system.
  • the instrument panel may be a dashboard or a centre display which displays for example, a speedometer, tachometer, and warning light indicators.
  • the user interface 218 may also comprise an electronic display such as an infotainment or heads-up display for communicating other visual messages to the driver/passenger and an audio system for playing audio messages, warning, or music.
  • FIG. 3A - FIG. 3D illustrates sequence of steps involved in the method of determining direction of the traffic mirror 106 according to one embodiment of the present disclosure.
  • FIG. 3A shows an exemplary real-time image 300 of the Field of View (FOV) 108 of the vehicle 104, captured by an image sensor module 208 of the vehicle 104, while the vehicle 104 is in movement and a traffic mirror 106 has been detected in the FOV 108 of the vehicle 104.
  • the safety monitoring system 200 configured in the vehicle 104 may process the real-time image for detecting a region of the traffic mirror 106 in the real-time image 300.
  • a cropped and/or extracted portion of the real-time image 300, comprising the region of the traffic mirror 106 is shown in image 302 of FIG. 3A.
  • the safety monitoring system 200 may identify one or more edges of the traffic mirror 106 as shown in image 304 of FIG. 3B. Subsequently, the safety monitoring system 200 may identify the shape of the traffic mirror 106 based on analysis of the one or more edges detected in the image 304. In the instant example, since the traffic mirror 106, as shown in image 300 of FIG. 3A, is elliptical in shape, the safety monitoring system 200 may detect an elliptical boundary from the one or more edges, as shown in image 306 of FIG. 3C.
  • the safety monitoring system 200 may measure a placement angle of the traffic mirror 106 by calculating an angle of tilt and/or turn of the traffic mirror 106 with respect to a reference horizontal axis 309 corresponding to the vehicle 104.
  • the reference horizontal axis 309 may be x- coordinate axis, which represents plane of the road 102 and/or the vehicle 104.
  • the placement angle may be calculated as the angle between a titled axis of the detected shape (marked with dotted lines in image 308) and the reference horizontal axis 309. In the image 308, the placement angle is represented by angle ‘0’.
  • the safety monitoring system 200 may determine the direction of the traffic mirror 106 based on value of the placement angle.
  • the direction of the traffic mirror 106 may be determined as ‘right’ when the value of the placement angle is less than 90 degrees.
  • the direction of the traffic mirror 106 may be determined as ‘left’ when the value of the placement angle is a value between 90 degrees and 180 degrees.
  • the direction of the traffic mirror 106 may be determined as ‘right’ since the placement angle ‘0’ is a value between 90 degrees and 180 degrees. Same steps may be repeated for determining the placement angle of the traffic mirror 106 of any shape and size.
  • FIG. 4A illustrates an alternative embodiment in which the traffic mirror 106 is tilted right.
  • the placement angle ‘0’ may be a value less than 90 degrees, and hence, the direction of the traffic mirror 106 may be determined as left.
  • FIG. 4B illustrates yet another embodiment of the proposed disclosure, for detecting direction of the traffic mirror 106 which has a polygonal shape (for example, square shape).
  • image 406 of FIG. 4B may be a real-time image of the traffic mirror 106.
  • Image 408 may represent the region of the traffic mirror 106 extracted from the real-time image 406.
  • the placement angle may be measured by running an auto angle compensation technique on the region of the traffic mirror 106 shown in image 408. For example, since the region of the traffic mirror 106 in the image 408 is tilted right, the auto compensation technique may rotate the region of the traffic mirror 106 in an anticlockwise direction to compensate the titled angle of the region of the traffic mirror 106.
  • the image 408 may be rotated anticlockwise by 8 degrees to compensate the tilted angle and obtain a compensated image 410.
  • the placement angle of the traffic mirror 106 may be measured as magnitude of the angle with which the image 408 has been rotated. In the instant example, the placement angle may be measured as 8 degrees since the image 408 was rotated by 8 degrees.
  • the direction of the traffic mirror 106 of image 406 may be determined as ‘right’ since the placement angle is a value less than 90 degrees.
  • the process illustrated above may be used for measuring the placement angle of the traffic mirrors of any other size and shape.
  • FIG. 5 is a flow diagram illustrating an exemplary method 500 for determining direction of the traffic mirror 106 according to one embodiment of the present disclosure.
  • the method 500 may be executed sequentially or in parallel with other embodiments of this disclosure for determining direction of traffic mirror 106.
  • the traffic mirrors may be used in different sizes and shapes including, but not limiting to, elliptical, square, or other polygonal shapes.
  • two or more processes may be executed in contemporaneously or sequentially to determine direction of the traffic mirrors irrespective of their size and shape.
  • the operations of the method 500 will be described with reference to the safety monitoring system 200 in FIG. 1. However, it will be appreciated that other similar systems may also be suitable.
  • the method 500 starts at step 502 and may be initiated upon the ignition of a vehicle 104 being switched on. Other events for initiating the start of the method 500 may also be suitable and the method may also be initiated on demand.
  • the method 500 causes the processor 202 in the safety monitoring system 200 to start detecting a region of a traffic mirror 106 from a real-time image of a Field of View (FOV) 108 of a vehicle 104.
  • the vehicle 104 may be an autonomous vehicle or a vehicle 104 comprising Advanced Driver Assistance System (ADAS).
  • ADAS Advanced Driver Assistance System
  • the vehicle 104 may capture the real-time image of the FOV 108 of the vehicle 104, comprising the traffic mirror 106, using an image sensor configured in the vehicle 104.
  • the region of the traffic mirror 106 may be detecting using a predetermined Artificial Intelligence (Al) technique such as, without limiting to, a pretrained Convolutional Neural Network (CNN)
  • the method 500 causes the processor 202 in the safety monitoring system 200 to identify one or more edges of the traffic mirror 106 by processing the region of the traffic mirror 106 detected in the real-time image.
  • the one or more edges of the traffic mirror 106 may be identified using predetermined edge detection techniques.
  • the method 500 causes the processor 202 in the safety monitoring system 200 to identify a shape of the traffic mirror 106 based on analysis of the one or more edges identified.
  • the shape of the traffic mirror 106 may be at least one of, without limitation, circular, elliptical or polygonal.
  • the method 500 causes the processor 202 in the safety monitoring system 200 to measure a placement angle of the traffic mirror 106.
  • the placement angle may be an angle of placement of the traffic mirror 106 with respect to the road 102.
  • the placement angle may be the tilted angle of the traffic mirror 106 with respect to of the road 102.
  • the placement angle may be measured using a predefined angle measurement technique corresponding to the shape of the traffic mirror 106.
  • measuring the placement angle may further include applying an angle compensation technique on the identified shape before measuring the placement angle. The compensation technique may be selected based on the identified shape of the traffic mirror 106.
  • the method 500 causes the processor 202 in the safety monitoring system 200 to determine the direction of the traffic mirror 106 based on value of the placement angle of the traffic mirror 106.
  • the direction of the traffic mirror 106 may be determined as ‘right’ when the value of the placement angle is less than 90 degrees or between zero to 90 degrees.
  • the direction of the traffic mirror 106 may be determined as ‘left’ when the value of the placement angle is between 90 degrees and 180 degrees.
  • an embodiment means “one or more (but not all) embodiments of the invention(s)" unless expressly specified otherwise.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Traffic Control Systems (AREA)
  • Image Analysis (AREA)

Abstract

La présente invention concerne un procédé et un système (200) pour déterminer la direction d'un miroir de circulation (106). Le procédé comprend la détection d'une région d'un miroir de circulation (106) à partir d'une image en temps réel d'un champ de vision (108) d'un véhicule (104). L'image en temps réel du champ de vision (108) du véhicule (104) est capturée pendant le déplacement du véhicule (104). En outre, le procédé comprend l'identification d'un ou plusieurs bords du miroir de circulation (106) par traitement de la région du miroir de circulation (106) détectée dans l'image en temps réel. Ensuite, le procédé comprend l'identification d'une forme du miroir de circulation (106) sur la base de l'analyse du ou des bords. En outre, le procédé comprend la mesure d'un angle de placement du miroir de circulation (106) à l'aide d'une technique de mesure d'angle prédéfinie correspondant à la forme du miroir de circulation (106). Enfin, le procédé comprend la détermination de la direction du miroir de circulation (106) sur la base de la valeur de l'angle de placement du miroir de circulation (106).
PCT/EP2022/071315 2021-08-03 2022-07-29 Procédé et système pour déterminer la direction d'un miroir de circulation WO2023012049A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
JP2024506705A JP2024528992A (ja) 2021-08-03 2022-07-29 交通ミラーの方向を特定するための方法及びシステム

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
GB2111145.5A GB2609464A (en) 2021-08-03 2021-08-03 Method and system for determining direction of a traffic mirror
GB2111145.5 2021-08-03

Publications (1)

Publication Number Publication Date
WO2023012049A1 true WO2023012049A1 (fr) 2023-02-09

Family

ID=77651396

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/EP2022/071315 WO2023012049A1 (fr) 2021-08-03 2022-07-29 Procédé et système pour déterminer la direction d'un miroir de circulation

Country Status (3)

Country Link
JP (1) JP2024528992A (fr)
GB (1) GB2609464A (fr)
WO (1) WO2023012049A1 (fr)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB2624627A (en) * 2022-11-22 2024-05-29 Continental Autonomous Mobility Germany GmbH A system and method of detecting curved mirrors within an image

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3249627A1 (fr) * 2015-01-22 2017-11-29 Pioneer Corporation Dispositif d'assistance à la conduite et procédé d'assistance à la conduite
DE102016215115A1 (de) * 2016-08-12 2018-02-15 Continental Automotive Gmbh Vorrichtung und Verfahren zum Detektieren von Verkehrsteilnehmern in einem Umfeld eines Ego-Fahrzeugs
US20180342046A1 (en) * 2017-05-23 2018-11-29 Toyota Jidosha Kabushiki Kaisha Providing Traffic Mirror Content to a Driver
DE102019213791A1 (de) * 2019-09-11 2021-03-11 Robert Bosch Gmbh Einsetzen von Informationen aus Verkehrsspiegeln für automatisiertes Fahren

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3249627A1 (fr) * 2015-01-22 2017-11-29 Pioneer Corporation Dispositif d'assistance à la conduite et procédé d'assistance à la conduite
DE102016215115A1 (de) * 2016-08-12 2018-02-15 Continental Automotive Gmbh Vorrichtung und Verfahren zum Detektieren von Verkehrsteilnehmern in einem Umfeld eines Ego-Fahrzeugs
US20180342046A1 (en) * 2017-05-23 2018-11-29 Toyota Jidosha Kabushiki Kaisha Providing Traffic Mirror Content to a Driver
DE102019213791A1 (de) * 2019-09-11 2021-03-11 Robert Bosch Gmbh Einsetzen von Informationen aus Verkehrsspiegeln für automatisiertes Fahren

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
MIDORI MORI ET AL: "Ergonomics Study of Direct and Indirect Visibility Evaluation at Uncontrolled Intersections Based on Three-Dimensional Computer Simulation", 21 July 2013, ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, pages: 70 - 77, XP047033693 *

Also Published As

Publication number Publication date
GB202111145D0 (en) 2021-09-15
JP2024528992A (ja) 2024-08-01
GB2609464A (en) 2023-02-08

Similar Documents

Publication Publication Date Title
JP6658905B2 (ja) 走行支援装置及びコンピュータプログラム
EP2492888B1 (fr) Appareil et système d'avertissement de sortie de voie
CN112349144B (zh) 一种基于单目视觉的车辆碰撞预警方法及系统
US20170293895A1 (en) Device and method for calculating damage repair cost
JP2001233150A (ja) 車両用危険判断装置及び、車両用周辺監視装置
JP6425857B2 (ja) 認知領域推定装置、認知領域推定方法および認知領域推定プログラム
JP2009211624A (ja) 運転支援装置、運転支援方法及びコンピュータプログラム
JP6129268B2 (ja) 車両用運転支援システムおよび運転支援方法
JP2007102691A (ja) 車両用視界支援装置
JP2009123182A (ja) 安全確認判定装置および運転教示支援システム
JP2000097714A (ja) カーナビゲーション装置
WO2022062000A1 (fr) Procédé d'aide à la conduite basé sur un pied avant transparent
WO2018149539A1 (fr) Procédé et appareil d'estimation d'une plage d'un objet mobile
JP2008134165A (ja) ナビゲーションシステム
WO2023012049A1 (fr) Procédé et système pour déterminer la direction d'un miroir de circulation
CN108725319B (zh) 一种影像式倒车指导方法
JP2010176592A (ja) 車両用運転支援装置
JP2008090683A (ja) 車載用ナビゲーション装置
US20240247937A1 (en) Method and system for creating a virtual lane for a vehicle
US10380437B2 (en) Systems and methods for traffic sign assistance
CN110763244B (zh) 一种电子地图生成系统和方法
JP2005205983A (ja) 自車周辺視認装置
CN106157662A (zh) 车辆的限制速度显示装置
JP2017531268A5 (fr)
JP2020131957A (ja) 車両、表示方法及びプログラム

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 22757585

Country of ref document: EP

Kind code of ref document: A1

ENP Entry into the national phase

Ref document number: 2024506705

Country of ref document: JP

Kind code of ref document: A

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 22757585

Country of ref document: EP

Kind code of ref document: A1