EP3803833A4 - System und verfahren zur vorhersage der absicht eines folgenden fahrzeugs für autonome fahrzeuge - Google Patents

System und verfahren zur vorhersage der absicht eines folgenden fahrzeugs für autonome fahrzeuge Download PDF

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Publication number
EP3803833A4
EP3803833A4 EP19810795.5A EP19810795A EP3803833A4 EP 3803833 A4 EP3803833 A4 EP 3803833A4 EP 19810795 A EP19810795 A EP 19810795A EP 3803833 A4 EP3803833 A4 EP 3803833A4
Authority
EP
European Patent Office
Prior art keywords
autonomous vehicles
intention prediction
proximate vehicle
vehicle intention
proximate
Prior art date
Legal status (The legal status 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 status listed.)
Pending
Application number
EP19810795.5A
Other languages
English (en)
French (fr)
Other versions
EP3803833A1 (de
Inventor
Zhipeng YAN
Mingdong WANG
Siyuan LIU
Xiaodi Hou
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Tusimple Inc
Original Assignee
Tusimple Inc
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
Priority claimed from US15/994,138 external-priority patent/US11104334B2/en
Priority claimed from US15/994,103 external-priority patent/US20190367019A1/en
Application filed by Tusimple Inc filed Critical Tusimple Inc
Publication of EP3803833A1 publication Critical patent/EP3803833A1/de
Publication of EP3803833A4 publication Critical patent/EP3803833A4/de
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems
    • G08G1/166—Anti-collision systems for active traffic, e.g. moving vehicles, pedestrians, bikes
    • 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
    • B60W60/00—Drive control systems specially adapted for autonomous road vehicles
    • B60W60/001—Planning or execution of driving tasks
    • B60W60/0027—Planning or execution of driving tasks using trajectory prediction for other traffic participants
    • B60W60/00272—Planning or execution of driving tasks using trajectory prediction for other traffic participants relying on extrapolation of current movement
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/34—Smoothing or thinning of the pattern; Morphological operations; Skeletonisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/84—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using probabilistic graphical models from image or video features, e.g. Markov models or Bayesian networks
    • G06V10/85—Markov-related models; Markov random fields
    • 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
    • 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
    • B60W2050/0001—Details of the control system
    • B60W2050/0002—Automatic control, details of type of controller or control system architecture
    • B60W2050/0014—Adaptive controllers
    • 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
    • B60W2554/4045—Intention, e.g. lane change or imminent movement

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Software Systems (AREA)
  • Evolutionary Computation (AREA)
  • Computing Systems (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Health & Medical Sciences (AREA)
  • Databases & Information Systems (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Probability & Statistics with Applications (AREA)
  • Automation & Control Theory (AREA)
  • Human Computer Interaction (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Traffic Control Systems (AREA)
EP19810795.5A 2018-05-31 2019-05-31 System und verfahren zur vorhersage der absicht eines folgenden fahrzeugs für autonome fahrzeuge Pending EP3803833A4 (de)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
US15/994,138 US11104334B2 (en) 2018-05-31 2018-05-31 System and method for proximate vehicle intention prediction for autonomous vehicles
US15/994,103 US20190367019A1 (en) 2018-05-31 2018-05-31 System and method for proximate vehicle intention prediction for autonomous vehicles
PCT/US2019/034882 WO2019232355A1 (en) 2018-05-31 2019-05-31 System and method for proximate vehicle intention prediction for autonomous vehicles

Publications (2)

Publication Number Publication Date
EP3803833A1 EP3803833A1 (de) 2021-04-14
EP3803833A4 true EP3803833A4 (de) 2022-03-09

Family

ID=68697246

Family Applications (1)

Application Number Title Priority Date Filing Date
EP19810795.5A Pending EP3803833A4 (de) 2018-05-31 2019-05-31 System und verfahren zur vorhersage der absicht eines folgenden fahrzeugs für autonome fahrzeuge

Country Status (4)

Country Link
EP (1) EP3803833A4 (de)
CN (2) CN112272844B (de)
AU (1) AU2019278974A1 (de)
WO (1) WO2019232355A1 (de)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102020200183A1 (de) * 2020-01-09 2021-07-15 Continental Automotive Gmbh Verfahren zum Erstellen einer probabilistischen Freiraumkarte mit statischen und dynamischen Objekten
CN113206786B (zh) * 2020-01-31 2022-12-27 华为技术有限公司 训练智能体的方法和装置
US11312373B2 (en) 2020-03-05 2022-04-26 Ford Global Technologies, Llc Vehicle detection and response

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100104199A1 (en) * 2008-04-24 2010-04-29 Gm Global Technology Operations, Inc. Method for detecting a clear path of travel for a vehicle enhanced by object detection

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
FR3020616B1 (fr) * 2014-04-30 2017-10-27 Renault Sas Dispositif de signalisation d'objets a un module de navigation de vehicule equipe de ce dispositif
DE102014009253B4 (de) * 2014-06-20 2022-10-27 Audi Ag Verfahren zum Steuern einer Lichtverteilung eines Scheinwerfers eines Kraftfahrzeugs
US20170199880A1 (en) * 2014-09-11 2017-07-13 Sony Corporation Information processing device, information processing method, and program
US9248834B1 (en) * 2014-10-02 2016-02-02 Google Inc. Predicting trajectories of objects based on contextual information

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100104199A1 (en) * 2008-04-24 2010-04-29 Gm Global Technology Operations, Inc. Method for detecting a clear path of travel for a vehicle enhanced by object detection

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
LENZ DAVID ET AL: "Deep neural networks for Markovian interactive scene prediction in highway scenarios", 2017 IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV), IEEE, 11 June 2017 (2017-06-11), pages 685 - 692, XP033133828, DOI: 10.1109/IVS.2017.7995797 *
LIU PENG ET AL: "Trajectory prediction of a lane changing vehicle based on driver behavior estimation and classification", 17TH INTERNATIONAL IEEE CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), IEEE, 8 October 2014 (2014-10-08), pages 942 - 947, XP032685771, DOI: 10.1109/ITSC.2014.6957810 *
MARTIN LIEBNER ET AL: "Driver intent inference at urban intersections using the intelligent driver model", INTELLIGENT VEHICLES SYMPOSIUM (IV), 2012 IEEE, IEEE, 3 June 2012 (2012-06-03), pages 1162 - 1167, XP032452922, ISBN: 978-1-4673-2119-8, DOI: 10.1109/IVS.2012.6232131 *
SCHREIER MATTHIAS ET AL: "An Integrated Approach to Maneuver-Based Trajectory Prediction and Criticality Assessment in Arbitrary Road Environments", IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, IEEE, PISCATAWAY, NJ, USA, vol. 17, no. 10, 1 October 2016 (2016-10-01), pages 2751 - 2766, XP011625592, ISSN: 1524-9050, [retrieved on 20161010], DOI: 10.1109/TITS.2016.2522507 *
See also references of WO2019232355A1 *

Also Published As

Publication number Publication date
WO2019232355A1 (en) 2019-12-05
CN115848416A (zh) 2023-03-28
CN112272844A (zh) 2021-01-26
AU2019278974A1 (en) 2021-01-07
CN112272844B (zh) 2023-02-03
EP3803833A1 (de) 2021-04-14

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