SE1850271A1 - Method, control arrangement and machine learning based system for proactively acting on situations involving an increased traffic accident risk - Google Patents

Method, control arrangement and machine learning based system for proactively acting on situations involving an increased traffic accident risk

Info

Publication number
SE1850271A1
SE1850271A1 SE1850271A SE1850271A SE1850271A1 SE 1850271 A1 SE1850271 A1 SE 1850271A1 SE 1850271 A SE1850271 A SE 1850271A SE 1850271 A SE1850271 A SE 1850271A SE 1850271 A1 SE1850271 A1 SE 1850271A1
Authority
SE
Sweden
Prior art keywords
traffic accident
accident risk
increased traffic
environmental data
situations involving
Prior art date
Application number
SE1850271A
Other versions
SE541635C2 (en
Inventor
Christoffer Norén
Mikael Johansson
Original Assignee
Scania Cv Ab
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 Scania Cv Ab filed Critical Scania Cv Ab
Priority to SE1850271A priority Critical patent/SE541635C2/en
Priority to DE112019000582.0T priority patent/DE112019000582T5/en
Priority to PCT/SE2019/050185 priority patent/WO2019177511A1/en
Publication of SE1850271A1 publication Critical patent/SE1850271A1/en
Publication of SE541635C2 publication Critical patent/SE541635C2/en

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/042—Knowledge-based neural networks; Logical representations of neural networks
    • 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
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • 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
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/02—Knowledge representation; Symbolic representation
    • G06N5/022—Knowledge engineering; Knowledge acquisition
    • 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
    • B60W2540/00—Input parameters relating to occupants
    • 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
    • B60W2552/00—Input parameters relating to infrastructure
    • 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
    • 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
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/18—Propelling the vehicle
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D2101/00—Details of software or hardware architectures used for the control of position
    • G05D2101/10—Details of software or hardware architectures used for the control of position using artificial intelligence [AI] techniques
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D2109/00—Types of controlled vehicles
    • G05D2109/10—Land vehicles
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • General Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Mechanical Engineering (AREA)
  • Automation & Control Theory (AREA)
  • Transportation (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Evolutionary Computation (AREA)
  • Computational Linguistics (AREA)
  • Computing Systems (AREA)
  • General Physics & Mathematics (AREA)
  • Artificial Intelligence (AREA)
  • Data Mining & Analysis (AREA)
  • Human Computer Interaction (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Traffic Control Systems (AREA)

Abstract

Method (600) for creating an artificial intuition functionality, capable of proactively act on situations involving an increased traffic accident risk. The method (600) comprises: collecting (601) vehicle environmental data via at least one sensor (110a, 110b, 110c) in a manned vehicle (100a, 100b); detecting (602) a precautious driver action/ reaction, indicating an increased traffic accident risk; labelling (604) the vehicle environmental data at the moment when the precautious driver action/ reaction, is detected (602); extracting (605) vehicle environmental data to be used for learning, based on the labelled (604) vehicle environmental data; creating (606) an artificial intuition functionality, capable of proactively act on situations involving an increased traffic accident risk, by training a machine learning based system (200), based on the extracted (605) vehicle environmental data; and implementing (607) the created (606) artificial intuition functionality in a control arrangement (510) of an autonomous vehicle (500).
SE1850271A 2018-03-12 2018-03-12 Method, control arrangement and machine learning based system for proactively acting on situations involving an increased traffic accident risk SE541635C2 (en)

Priority Applications (3)

Application Number Priority Date Filing Date Title
SE1850271A SE541635C2 (en) 2018-03-12 2018-03-12 Method, control arrangement and machine learning based system for proactively acting on situations involving an increased traffic accident risk
DE112019000582.0T DE112019000582T5 (en) 2018-03-12 2019-03-04 Method, control arrangement and machine learning based system for autonomous vehicles for acting proactively with regard to situations involving an increased risk of a traffic accident
PCT/SE2019/050185 WO2019177511A1 (en) 2018-03-12 2019-03-04 Method, control arrangement and machine learning based system for autonomous vehicles for proactively acting on situations involving an increased traffic accident risk

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
SE1850271A SE541635C2 (en) 2018-03-12 2018-03-12 Method, control arrangement and machine learning based system for proactively acting on situations involving an increased traffic accident risk

Publications (2)

Publication Number Publication Date
SE1850271A1 true SE1850271A1 (en) 2019-09-13
SE541635C2 SE541635C2 (en) 2019-11-19

Family

ID=67908347

Family Applications (1)

Application Number Title Priority Date Filing Date
SE1850271A SE541635C2 (en) 2018-03-12 2018-03-12 Method, control arrangement and machine learning based system for proactively acting on situations involving an increased traffic accident risk

Country Status (3)

Country Link
DE (1) DE112019000582T5 (en)
SE (1) SE541635C2 (en)
WO (1) WO2019177511A1 (en)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113673826B (en) * 2021-07-20 2023-06-02 中国科学技术大学先进技术研究院 Driving risk assessment method and system based on individual factors of driver
US11922378B2 (en) * 2021-12-10 2024-03-05 Tekion Corp Machine learning based vehicle service recommendation system

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8780195B1 (en) * 2011-08-31 2014-07-15 The United States Of America As Represented By The Secretary Of The Navy Fusion of multi-sensor information with operator-learned behavior for automatic and efficient recognition of objects and control of remote vehicles
RU2607977C1 (en) * 2015-06-30 2017-01-11 Александр Игоревич Колотыгин Method of creating model of object
KR101876051B1 (en) * 2016-08-31 2018-08-02 현대자동차주식회사 Machine learning system and method for learning user controlling pattern thereof

Also Published As

Publication number Publication date
SE541635C2 (en) 2019-11-19
WO2019177511A1 (en) 2019-09-19
DE112019000582T5 (en) 2020-11-26

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