US20180292823A1 - Motion-characteristic based object classification for automated vehicle - Google Patents

Motion-characteristic based object classification for automated vehicle Download PDF

Info

Publication number
US20180292823A1
US20180292823A1 US15/480,520 US201715480520A US2018292823A1 US 20180292823 A1 US20180292823 A1 US 20180292823A1 US 201715480520 A US201715480520 A US 201715480520A US 2018292823 A1 US2018292823 A1 US 2018292823A1
Authority
US
United States
Prior art keywords
vehicle
host
air
movement
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.)
Granted
Application number
US15/480,520
Other versions
US10114375B1 (en
Inventor
Junqing Wei
Wenda Xu
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.)
Aptiv Technologies Ltd
Original Assignee
Aptiv Technologies Ltd
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 Aptiv Technologies Ltd filed Critical Aptiv Technologies Ltd
Assigned to DELPHI TECHNOLOGIES, INC. reassignment DELPHI TECHNOLOGIES, INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: WEI, JUNQING, XU, Wenda
Priority to US15/480,520 priority Critical patent/US10114375B1/en
Priority to CN202211021470.7A priority patent/CN115476846B/en
Priority to EP22150897.1A priority patent/EP4016500A1/en
Priority to CN201810298912.XA priority patent/CN108693533B/en
Priority to EP18165666.1A priority patent/EP3404641B1/en
Assigned to APTIV TECHNOLOGIES LIMITED reassignment APTIV TECHNOLOGIES LIMITED ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: DELPHI TECHNOLOGIES INC.
Publication of US20180292823A1 publication Critical patent/US20180292823A1/en
Priority to US16/174,241 priority patent/US10877478B2/en
Publication of US10114375B1 publication Critical patent/US10114375B1/en
Application granted granted Critical
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/0088—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours
    • 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
    • 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
    • B60W30/09—Taking automatic action to avoid collision, e.g. braking and steering
    • 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
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
    • B62D—MOTOR VEHICLES; TRAILERS
    • B62D15/00—Steering not otherwise provided for
    • B62D15/02—Steering position indicators ; Steering position determination; Steering aids
    • B62D15/025—Active steering aids, e.g. helping the driver by actively influencing the steering system after environment evaluation
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
    • B62D—MOTOR VEHICLES; TRAILERS
    • B62D15/00—Steering not otherwise provided for
    • B62D15/02—Steering position indicators ; Steering position determination; Steering aids
    • B62D15/025—Active steering aids, e.g. helping the driver by actively influencing the steering system after environment evaluation
    • B62D15/0265—Automatic obstacle avoidance by steering
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/93—Radar or analogous systems specially adapted for specific applications for anti-collision purposes
    • G01S13/931—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of land vehicles
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/02—Systems using the reflection of electromagnetic waves other than radio waves
    • G01S17/50—Systems of measurement based on relative movement of target
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/86—Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G01S17/93—Lidar systems specially adapted for specific applications for anti-collision purposes
    • G01S17/931—Lidar systems specially adapted for specific applications for anti-collision purposes of land vehicles
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G01S7/415—Identification of targets based on measurements of movement associated with the target
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
    • G01S7/4802—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
    • G05D1/0246—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using a video camera in combination with image processing means
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0257—Control of position or course in two dimensions specially adapted to land vehicles using a radar
    • 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
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/20—Movements or behaviour, e.g. gesture recognition
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems
    • G08G1/165—Anti-collision systems for passive traffic, e.g. including static obstacles, trees
    • 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

Definitions

  • This disclosure generally relates to an object-classification system for an automated vehicle, and more particularly relates to system that operates a host-vehicle to avoid striking an object with the host-vehicle when a density of the object is classified as dense.
  • Automated vehicles are generally programmed to avoid running-over or striking any detected object. However, sudden evasive maneuvers are not necessary when the detected objected is an empty paper bag or small tumbleweed.
  • an object-classification system for an automated vehicle includes an object-detector and a controller.
  • the object-detector detects an object proximate to a host-vehicle.
  • the controller is in communication with the object-detector.
  • the controller is configured to determine a density of the object based on a motion-characteristic of the object caused by air-movement proximate to the object, and operate the host-vehicle to avoid striking the object with the host-vehicle when the density of the object is classified as dense.
  • FIG. 1 is a diagram of an object-classification system in accordance with one embodiment.
  • FIG. 2 is a scenario encountered by the system of FIG. 1 in accordance with one embodiment.
  • FIG. 1 illustrates a non-limiting example of an object-classification system 10 , hereafter referred to as the system 10 .
  • the system 10 is suitable for use by an automated vehicle, a host-vehicle 12 for example.
  • the term automated vehicle may apply to instances when the host-vehicle 12 is being operated in an automated-mode 14 , i.e. a fully autonomous mode, where a human-operator (not shown) of the host-vehicle 12 may do little more than designate a destination in order to operate the host-vehicle 12 .
  • full automation is not a requirement.
  • teachings presented herein are useful when the host-vehicle 12 is operated in a manual-mode 16 where the degree or level of automation may be limited to momentarily taking control of the steering of the host-vehicle 12 to avoid a collision with, for example, an object 18 .
  • the system 10 includes an object-detector 20 that detects objects proximate to, e.g. within two-hundred-meters (200 m) of, the host-vehicle 12 .
  • the object-detector 20 may include or consist of devices such as a camera, radar, lidar, or any combination thereof.
  • the one or multiple devices that form the object-detector 20 are preferably, but not necessarily, mounted on the host-vehicle 12 . If multiple devices are used, they may be co-located as suggested by FIG. 1 , but this is not a requirement.
  • the multiple devices may be distributed at different locations on the host-vehicle 12 . It is also contemplated that some or all of the devices could be located remote from the host-vehicle 12 as part of traffic-monitoring-infrastructure.
  • FIG. 2 illustrates a non-limiting example of a scenario 22 that could be encountered by the host-vehicle 12 .
  • the illustration shows an example field-of-view of the object-detector 20 (not shown in FIG. 2 ) that includes a roadway 24 forward of a host-vehicle 12 (not shown in FIG. 2 ).
  • Various non-limiting examples of the object 18 are illustrated that include a log 18 A that may have fallen from a logging-truck (not shown), and an empty plastic-bag 18 B.
  • the system 10 described herein is advantageous over prior systems for automated vehicles because the system 10 is able distinguish the log 18 A from the empty plastic-bag 18 B.
  • the system 10 classifies the log 18 A as being something that the host-vehicle 12 should not run-over, i.e. the host-vehicle 12 should avoid.
  • the empty plastic-bag 18 B e.g. the type of plastic bag commonly used by grocery stores
  • the host-vehicle 12 could run-over if avoiding the empty plastic-bag 18 B could cause the host-vehicle 12 to do something undesirable, e.g. swerve into the travel-path of an oncoming-vehicle 26 .
  • the system 10 includes a controller 28 in communication with the object-detector 20 .
  • the controller 28 may include a processor (not specifically shown) such as a microprocessor or other control circuitry such as analog and/or digital control circuitry including an application specific integrated circuit (ASIC) for processing data as should be evident to those in the art.
  • the controller 28 may include memory (not specifically shown), including non-volatile memory, such as electrically erasable programmable read-only memory (EEPROM) for storing one or more routines, thresholds, and captured data.
  • the one or more routines may be executed by the processor to perform steps for controlling the host-vehicle 12 based on signals received by the controller 28 from the object-detector 20 as described herein.
  • the controller 28 is configured to or programmed to determine a density 30 of the object 18 (e.g. log 18 A, empty plastic-bag 18 B) based on a motion-characteristic 32 of the object 18 that is caused by an air-movement 34 proximate to (i.e. close enough to influence movement of) the object 18 .
  • a density 30 of the object 18 e.g. log 18 A, empty plastic-bag 18 B
  • the controller 28 is configured to or programmed to determine a density 30 of the object 18 (e.g. log 18 A, empty plastic-bag 18 B) based on a motion-characteristic 32 of the object 18 that is caused by an air-movement 34 proximate to (i.e. close enough to influence movement of) the object 18 .
  • a variety of techniques used to determine the presence of, or detect, the air-movement 34 are described below.
  • the amount or degree to which a particular instance of the object 18 moves in response to the air-movement 34 indicates the motion-characteristic and is used to determine the density
  • the density 30 is determined based on the motion-characteristic 32 , i.e. how much the object 18 moves for a given instance of the air-movement 34 .
  • the empty plastic-bag 18 B would be characterized as having a low-density, so the system 10 or more specifically the controller 28 may not make any effort to avoid running-over the empty plastic-bag 18 B.
  • the log 18 A would be characterized or classified as dense, so the controller 28 may operate the host-vehicle 12 to avoid striking the object 18 with the host-vehicle 12 when the density 30 of the object is classified as dense.
  • the object 18 may be characterized as moving or motionless.
  • the amount of the air-movement 34 is used to classify the object 18 as dense or low-density, or some other indicator useful to estimate/determine if the host-vehicle 12 could be damaged by striking, i.e. running-over, the object 18 .
  • Non-limiting examples of how the air-movement 34 may be determined or estimated will now be described in reference to FIGS. 1 and 2 .
  • the air-movement 34 proximate to the object 18 may be determined based on a steering-correction 36 necessary to keep the host-vehicle 12 centered in a roadway 24 .
  • a cross-wind 38 e.g. the air-movement 34 indicated by the arrow in FIG. 2
  • a road-angle 40 i.e. how much the roadway 24 slopes from center-line to shoulder
  • a constant steering-torque i.e. the steering-correction 36
  • the camera (if available) of the object-detector 20 and/or an angle-sensor (not shown) mounted on the host-vehicle 12 may be used to detect the road-angle 40 so that the effects of or contributions due to the cross-wind 38 may be separated from the steering-correction 36 .
  • the air-movement 34 proximate to the object 18 may be determined based on movement of an other-object 42 , a tumbleweed 42 A and/or a cluster of tall-grasses 42 B, and/or a tree 42 C for example, proximate to the roadway 24 .
  • Linear movement by the tumbleweed 42 A, waving movement by the tall-grasses 42 B, and/or oscillatory-movement by the tree 42 C may all be used an indicator of the air-movement 34 .
  • Image-processing of a signal from the camera, or noise-analysis of signals from the radar and/or the lidar may all be used to determine the air-movement 34 , as will be recognized by those in the art.
  • the air-movement 34 proximate to the object 18 may be determined based on air-displacement-model 44 of an other-vehicle 46 passing near, e.g. within five meters (5 m), the object 18 .
  • FIG. 2 illustrates the motion-characteristic 32 of the empty plastic-bag 18 B as being first blown away from the travel-path of the other-vehicle 46 , and then pulled back behind the other-vehicle 46 .
  • the air-movement 34 is caused by the movement of the other-vehicle 46 rather than necessarily only by wind.
  • the amount that the empty plastic-bag 18 B is moved about by the passing if the other-vehicle 46 is dependent on the size, speed, and/or distance between the other-vehicle 46 and the object 18 . That is, the air-displacement-model 44 associated with or assigned to the other-vehicle 46 is characterized by the size of the other-vehicle 46 , the speed of the other-vehicle 46 , and/or a distance between the other-vehicle 46 and the object 18 .
  • the air-displacement-model 44 may be established by empirical testing, computer-modeling, or a combination thereof.
  • an object-classification system (the system 10 ), a controller 28 for the system 10 , and a method of operating the system 10 is provided.
  • the controller 28 preferably steers the host-vehicle 12 around or away from the log 18 A.
  • the controller 28 may operate the host-vehicle 12 to straddle the object.
  • the controller 28 may allow the host-vehicle 12 to run-over the object 18 .

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Remote Sensing (AREA)
  • Radar, Positioning & Navigation (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Electromagnetism (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Automation & Control Theory (AREA)
  • Multimedia (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Theoretical Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Human Computer Interaction (AREA)
  • General Health & Medical Sciences (AREA)
  • Psychiatry (AREA)
  • Social Psychology (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Game Theory and Decision Science (AREA)
  • Medical Informatics (AREA)
  • Business, Economics & Management (AREA)
  • Traffic Control Systems (AREA)
  • Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
  • Control Of Driving Devices And Active Controlling Of Vehicle (AREA)

Abstract

An object-classification system for an automated vehicle includes an object-detector and a controller. The object-detector may be a camera, radar, lidar or any combination thereof. The object-detector detects an object proximate to a host-vehicle. The controller is in communication with the object-detector. The controller is configured to determine a density of the object based on a motion-characteristic of the object caused by air-movement proximate to the object, and operate the host-vehicle to avoid striking the object with the host-vehicle when the density of the object is classified as dense.

Description

    TECHNICAL FIELD OF INVENTION
  • This disclosure generally relates to an object-classification system for an automated vehicle, and more particularly relates to system that operates a host-vehicle to avoid striking an object with the host-vehicle when a density of the object is classified as dense.
    • BACKGROUND OF INVENTION
  • Automated vehicles are generally programmed to avoid running-over or striking any detected object. However, sudden evasive maneuvers are not necessary when the detected objected is an empty paper bag or small tumbleweed.
    • SUMMARY OF THE INVENTION
  • In accordance with one embodiment, an object-classification system for an automated vehicle is provided. The system includes an object-detector and a controller. The object-detector detects an object proximate to a host-vehicle. The controller is in communication with the object-detector. The controller is configured to determine a density of the object based on a motion-characteristic of the object caused by air-movement proximate to the object, and operate the host-vehicle to avoid striking the object with the host-vehicle when the density of the object is classified as dense.
  • Further features and advantages will appear more clearly on a reading of the following detailed description of the preferred embodiment, which is given by way of non-limiting example only and with reference to the accompanying drawings.
    • BRIEF DESCRIPTION OF DRAWINGS
  • The present invention will now be described, by way of example with reference to the accompanying drawings, in which:
  • FIG. 1 is a diagram of an object-classification system in accordance with one embodiment; and
  • FIG. 2 is a scenario encountered by the system of FIG. 1 in accordance with one embodiment.
    •  DETAILED DESCRIPTION
  • FIG. 1 illustrates a non-limiting example of an object-classification system 10, hereafter referred to as the system 10. In general, the system 10 is suitable for use by an automated vehicle, a host-vehicle 12 for example. As used herein, the term automated vehicle may apply to instances when the host-vehicle 12 is being operated in an automated-mode 14, i.e. a fully autonomous mode, where a human-operator (not shown) of the host-vehicle 12 may do little more than designate a destination in order to operate the host-vehicle 12. However, full automation is not a requirement. It is contemplated that the teachings presented herein are useful when the host-vehicle 12 is operated in a manual-mode 16 where the degree or level of automation may be limited to momentarily taking control of the steering of the host-vehicle 12 to avoid a collision with, for example, an object 18.
  • The system 10 includes an object-detector 20 that detects objects proximate to, e.g. within two-hundred-meters (200 m) of, the host-vehicle 12. The object-detector 20 may include or consist of devices such as a camera, radar, lidar, or any combination thereof. The one or multiple devices that form the object-detector 20 are preferably, but not necessarily, mounted on the host-vehicle 12. If multiple devices are used, they may be co-located as suggested by FIG. 1, but this is not a requirement. The multiple devices may be distributed at different locations on the host-vehicle 12. It is also contemplated that some or all of the devices could be located remote from the host-vehicle 12 as part of traffic-monitoring-infrastructure.
  • FIG. 2 illustrates a non-limiting example of a scenario 22 that could be encountered by the host-vehicle 12. The illustration shows an example field-of-view of the object-detector 20 (not shown in FIG. 2) that includes a roadway 24 forward of a host-vehicle 12 (not shown in FIG. 2). Various non-limiting examples of the object 18 are illustrated that include a log 18A that may have fallen from a logging-truck (not shown), and an empty plastic-bag 18B. As will be explained in more detail below, the system 10 described herein is advantageous over prior systems for automated vehicles because the system 10 is able distinguish the log 18A from the empty plastic-bag 18B. The system 10 classifies the log 18A as being something that the host-vehicle 12 should not run-over, i.e. the host-vehicle 12 should avoid. In contrast, the empty plastic-bag 18B (e.g. the type of plastic bag commonly used by grocery stores) is something the host-vehicle 12 could run-over if avoiding the empty plastic-bag 18B could cause the host-vehicle 12 to do something undesirable, e.g. swerve into the travel-path of an oncoming-vehicle 26.
  • Referring back to FIG. 1, the system 10 includes a controller 28 in communication with the object-detector 20. The controller 28 may include a processor (not specifically shown) such as a microprocessor or other control circuitry such as analog and/or digital control circuitry including an application specific integrated circuit (ASIC) for processing data as should be evident to those in the art. The controller 28 may include memory (not specifically shown), including non-volatile memory, such as electrically erasable programmable read-only memory (EEPROM) for storing one or more routines, thresholds, and captured data. The one or more routines may be executed by the processor to perform steps for controlling the host-vehicle 12 based on signals received by the controller 28 from the object-detector 20 as described herein.
  • The controller 28 is configured to or programmed to determine a density 30 of the object 18 (e.g. log 18A, empty plastic-bag 18B) based on a motion-characteristic 32 of the object 18 that is caused by an air-movement 34 proximate to (i.e. close enough to influence movement of) the object 18. A variety of techniques used to determine the presence of, or detect, the air-movement 34 are described below. The amount or degree to which a particular instance of the object 18 moves in response to the air-movement 34 indicates the motion-characteristic and is used to determine the density 30 of the object 18. By way of example, almost any instance of the air-movement 34 would cause the empty plastic-bag 18B to move on or across the roadway 24. However, it would take a very strong instance of the air-movement 34 to move the log 18A. That is, the density 30 is determined based on the motion-characteristic 32, i.e. how much the object 18 moves for a given instance of the air-movement 34.
  • It follows that the empty plastic-bag 18B would be characterized as having a low-density, so the system 10 or more specifically the controller 28 may not make any effort to avoid running-over the empty plastic-bag 18B. However, the log 18A would be characterized or classified as dense, so the controller 28 may operate the host-vehicle 12 to avoid striking the object 18 with the host-vehicle 12 when the density 30 of the object is classified as dense. For a given instance or amount or quantity or value or magnitude/direction of the air-movement 34, the object 18 may be characterized as moving or motionless. If the object 18 is characterized as moving, then the amount of the air-movement 34 is used to classify the object 18 as dense or low-density, or some other indicator useful to estimate/determine if the host-vehicle 12 could be damaged by striking, i.e. running-over, the object 18. Non-limiting examples of how the air-movement 34 may be determined or estimated will now be described in reference to FIGS. 1 and 2.
  • In one embodiment of the system 10, the air-movement 34 proximate to the object 18 may be determined based on a steering-correction 36 necessary to keep the host-vehicle 12 centered in a roadway 24. As will be recognized by those in the art, a cross-wind 38 (e.g. the air-movement 34 indicated by the arrow in FIG. 2) and/or a road-angle 40 (i.e. how much the roadway 24 slopes from center-line to shoulder) may make it necessary for the steering-system of the host-vehicle 12 to apply a constant steering-torque (i.e. the steering-correction 36) to keep the host-vehicle 12 centered in a roadway 24. The camera (if available) of the object-detector 20 and/or an angle-sensor (not shown) mounted on the host-vehicle 12 may be used to detect the road-angle 40 so that the effects of or contributions due to the cross-wind 38 may be separated from the steering-correction 36.
  • In another embodiment of the system 10, the air-movement 34 proximate to the object 18 may be determined based on movement of an other-object 42, a tumbleweed 42A and/or a cluster of tall-grasses 42B, and/or a tree 42C for example, proximate to the roadway 24. Linear movement by the tumbleweed 42A, waving movement by the tall-grasses 42B, and/or oscillatory-movement by the tree 42C may all be used an indicator of the air-movement 34. Image-processing of a signal from the camera, or noise-analysis of signals from the radar and/or the lidar may all be used to determine the air-movement 34, as will be recognized by those in the art.
  • In another embodiment of the system 10, the air-movement 34 proximate to the object 18 may be determined based on air-displacement-model 44 of an other-vehicle 46 passing near, e.g. within five meters (5 m), the object 18. FIG. 2 illustrates the motion-characteristic 32 of the empty plastic-bag 18B as being first blown away from the travel-path of the other-vehicle 46, and then pulled back behind the other-vehicle 46. In this instance, the air-movement 34 is caused by the movement of the other-vehicle 46 rather than necessarily only by wind. The amount that the empty plastic-bag 18B is moved about by the passing if the other-vehicle 46 is dependent on the size, speed, and/or distance between the other-vehicle 46 and the object 18. That is, the air-displacement-model 44 associated with or assigned to the other-vehicle 46 is characterized by the size of the other-vehicle 46, the speed of the other-vehicle 46, and/or a distance between the other-vehicle 46 and the object 18. The air-displacement-model 44 may be established by empirical testing, computer-modeling, or a combination thereof.
  • Accordingly, an object-classification system (the system 10), a controller 28 for the system 10, and a method of operating the system 10 is provided. If the object 18 is dense and large enough, the log 18A for example, the controller 28 preferably steers the host-vehicle 12 around or away from the log 18A. If the object 18 were a rock (not shown) which is clearly dense, but possibly small enough for the host-vehicle 12 to straddle (i.e. pass over and between the wheels of the host-vehicle 12) without striking, then the controller 28 may operate the host-vehicle 12 to straddle the object. If the object 18 is of low-density (e.g. the empty plastic-bag 18B or the tumble-weed 42A), even if too large to straddle without striking, the controller 28 may allow the host-vehicle 12 to run-over the object 18.
  • While this invention has been described in terms of the preferred embodiments thereof, it is not intended to be so limited, but rather only to the extent set forth in the claims that follow.

Claims (5)

1. An object-classification system for an automated vehicle, said system comprising:
an object-detector that detects an object proximate to a host-vehicle; and
a controller in communication with the object-detector, said controller configured to characterize a density of the object based on a motion-characteristic of the object caused by air-movement proximate to the object, and operate the host-vehicle to avoid striking the object with the host-vehicle when the density of the object is classified as dense.
2. The system in accordance with claim 1, wherein the air-movement proximate to the object is determined based on a steering-correction necessary to keep the host-vehicle centered in a roadway.
3. The system in accordance with claim 1, wherein the air-movement proximate to the object is determined based on movement of an other-object proximate to the roadway.
4. The system in accordance with claim 1, wherein the air-movement proximate to the object is determined based on air-displacement-model of an other-vehicle passing near the object.
5. The system in accordance with claim 4, wherein the air-displacement-model of the other-vehicle is characterized by a size of the other-vehicle, a speed of the other-vehicle, and a distance between the other-vehicle and the object.
US15/480,520 2017-04-06 2017-04-06 Motion-characteristic based object classification for automated vehicle Active US10114375B1 (en)

Priority Applications (6)

Application Number Priority Date Filing Date Title
US15/480,520 US10114375B1 (en) 2017-04-06 2017-04-06 Motion-characteristic based object classification for automated vehicle
EP18165666.1A EP3404641B1 (en) 2017-04-06 2018-04-04 Motion-characteristic based object classification for automated vehicle
EP22150897.1A EP4016500A1 (en) 2017-04-06 2018-04-04 Motion-characteristic based object classification for automated vehicle
CN201810298912.XA CN108693533B (en) 2017-04-06 2018-04-04 Motion characteristic based object classification for automated vehicles
CN202211021470.7A CN115476846B (en) 2017-04-06 2018-04-04 System for classifying objects and operating an automated vehicle and automated vehicle
US16/174,241 US10877478B2 (en) 2017-04-06 2018-10-29 Motion-characteristic based object classification for automated vehicle

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
US15/480,520 US10114375B1 (en) 2017-04-06 2017-04-06 Motion-characteristic based object classification for automated vehicle

Related Child Applications (1)

Application Number Title Priority Date Filing Date
US16/174,241 Continuation US10877478B2 (en) 2017-04-06 2018-10-29 Motion-characteristic based object classification for automated vehicle

Publications (2)

Publication Number Publication Date
US20180292823A1 true US20180292823A1 (en) 2018-10-11
US10114375B1 US10114375B1 (en) 2018-10-30

Family

ID=61971905

Family Applications (2)

Application Number Title Priority Date Filing Date
US15/480,520 Active US10114375B1 (en) 2017-04-06 2017-04-06 Motion-characteristic based object classification for automated vehicle
US16/174,241 Active US10877478B2 (en) 2017-04-06 2018-10-29 Motion-characteristic based object classification for automated vehicle

Family Applications After (1)

Application Number Title Priority Date Filing Date
US16/174,241 Active US10877478B2 (en) 2017-04-06 2018-10-29 Motion-characteristic based object classification for automated vehicle

Country Status (3)

Country Link
US (2) US10114375B1 (en)
EP (2) EP3404641B1 (en)
CN (2) CN108693533B (en)

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20190204833A1 (en) * 2017-04-06 2019-07-04 Aptiv Technologies Limited Motion-characteristic based object classification for automated vehicle
GB2578914A (en) * 2018-11-14 2020-06-03 Jaguar Land Rover Ltd Vehicle control system and method
US20220097713A1 (en) * 2020-09-28 2022-03-31 Ford Global Technologies, Llc Crosswind risk determination
US11760348B2 (en) 2021-09-27 2023-09-19 Ford Global Technologies, Llc Vehicle boundary control
DE102022127833A1 (en) 2022-10-21 2024-05-02 Bayerische Motoren Werke Aktiengesellschaft Driver assistance system and driver assistance method for a vehicle

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3410362B1 (en) * 2017-05-31 2022-03-30 HERE Global B.V. Method and apparatus for next token prediction based on previously observed tokens
US11724735B2 (en) * 2018-12-19 2023-08-15 Hl Mando Corporation Steering control apparatus, steering control method, and steering apparatus

Family Cites Families (24)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6025796A (en) * 1996-12-09 2000-02-15 Crosby, Ii; Robert G. Radar detector for pre-impact airbag triggering
US20120116632A1 (en) * 1997-04-02 2012-05-10 Bechtel Jon H System for controlling vehicle equipment
US7660436B2 (en) * 2003-06-13 2010-02-09 Sarnoff Corporation Stereo-vision based imminent collision detection
US7248718B2 (en) * 2004-02-19 2007-07-24 Siemens Corporate Research, Inc. System and method for detecting a passing vehicle from dynamic background using robust information fusion
JP4684698B2 (en) * 2005-03-22 2011-05-18 本田技研工業株式会社 Vehicle steering control device
DE102005046045A1 (en) * 2005-09-27 2007-03-29 Robert Bosch Gmbh Object detection sensor controlling method for motor vehicle, involves transmitting important and detection probability density functions to coordinator, and selecting suitable operating mode for driver assistance function by coordinator
US20080262669A1 (en) * 2006-09-22 2008-10-23 Jadi, Inc. Autonomous vehicle controller
DE102007042220A1 (en) * 2007-09-05 2009-03-12 Robert Bosch Gmbh Object classification method, parking assistance method and parking assistance system
US8108147B1 (en) * 2009-02-06 2012-01-31 The United States Of America As Represented By The Secretary Of The Navy Apparatus and method for automatic omni-directional visual motion-based collision avoidance
EP2491344B1 (en) * 2009-10-22 2016-11-30 TomTom Global Content B.V. System and method for vehicle navigation using lateral offsets
CN103328299B (en) * 2010-12-29 2016-04-06 沃尔沃拉斯特瓦格纳公司 Adaptive learning algorithms and the method for the speed that controls the power actuated vehicle being equipped with adaptive cruise control system
WO2012127815A1 (en) * 2011-03-22 2012-09-27 パナソニック株式会社 Moving object detecting apparatus and moving object detecting method
US8976040B2 (en) * 2012-02-16 2015-03-10 Bianca RAY AVALANI Intelligent driver assist system based on multimodal sensor fusion
US8798841B1 (en) * 2013-03-14 2014-08-05 GM Global Technology Operations LLC System and method for improving sensor visibility of vehicle in autonomous driving mode
ES2972160T3 (en) * 2014-01-16 2024-06-11 Polestar Performance Ab A vehicle adapted for autonomous driving and a method to detect obstructing objects
US9834207B2 (en) * 2014-04-15 2017-12-05 GM Global Technology Operations LLC Method and system for detecting, tracking and estimating stationary roadside objects
JP2016162424A (en) * 2015-03-05 2016-09-05 パイオニア株式会社 Estimation device, estimation method, and program
US9594378B2 (en) * 2015-07-31 2017-03-14 Delphi Technologies, Inc. Variable object detection field-of-focus for automated vehicle control
US9764736B2 (en) * 2015-08-14 2017-09-19 Toyota Motor Engineering & Manufacturing North America, Inc. Autonomous vehicle operation relative to unexpected dynamic objects
DE102015217486A1 (en) * 2015-09-14 2017-03-16 Volkswagen Ag Device and method for the automated driving of a motor vehicle
US9862364B2 (en) * 2015-12-04 2018-01-09 Waymo Llc Collision mitigated braking for autonomous vehicles
US9633560B1 (en) * 2016-03-30 2017-04-25 Jason Hao Gao Traffic prediction and control system for vehicle traffic flows at traffic intersections
JP6520874B2 (en) * 2016-09-12 2019-05-29 トヨタ自動車株式会社 Vehicle control device
US10114375B1 (en) * 2017-04-06 2018-10-30 Delphi Technologies, Inc. Motion-characteristic based object classification for automated vehicle

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20190204833A1 (en) * 2017-04-06 2019-07-04 Aptiv Technologies Limited Motion-characteristic based object classification for automated vehicle
US10877478B2 (en) * 2017-04-06 2020-12-29 Motional Ad Llc Motion-characteristic based object classification for automated vehicle
GB2578914A (en) * 2018-11-14 2020-06-03 Jaguar Land Rover Ltd Vehicle control system and method
GB2578914B (en) * 2018-11-14 2021-05-12 Jaguar Land Rover Ltd Vehicle control system and method
US20220097713A1 (en) * 2020-09-28 2022-03-31 Ford Global Technologies, Llc Crosswind risk determination
US11400940B2 (en) * 2020-09-28 2022-08-02 Ford Global Technologies, Llc Crosswind risk determination
US11760348B2 (en) 2021-09-27 2023-09-19 Ford Global Technologies, Llc Vehicle boundary control
DE102022127833A1 (en) 2022-10-21 2024-05-02 Bayerische Motoren Werke Aktiengesellschaft Driver assistance system and driver assistance method for a vehicle

Also Published As

Publication number Publication date
US10114375B1 (en) 2018-10-30
CN108693533A (en) 2018-10-23
US20190204833A1 (en) 2019-07-04
EP3404641B1 (en) 2022-01-12
CN115476846A (en) 2022-12-16
CN115476846B (en) 2024-04-19
CN108693533B (en) 2022-09-13
EP4016500A1 (en) 2022-06-22
EP3404641A1 (en) 2018-11-21
US10877478B2 (en) 2020-12-29

Similar Documents

Publication Publication Date Title
US10114375B1 (en) Motion-characteristic based object classification for automated vehicle
US11194328B2 (en) Operation-security system for an automated vehicle
US10276049B2 (en) Camera based trailer identification and blind zone adjustment
US11250276B2 (en) Object height determination for automated vehicle steering control system
CN108146410B (en) Automatic braking system
US20210231774A1 (en) Annotation of Radar-Profiles of Objects
EP3936401A2 (en) Object position history playback for automated vehicle transition from autonomous-mode to manual-mode
US10848718B2 (en) Vehicle sensor configuration based on map data
EP3640121A1 (en) Vehicle lane-bias system and method
US20180292836A1 (en) Automated vehicle operation-rules selected based on automation-level other vehicles
EP3409545B1 (en) Automatic braking system for slow moving objects
US20210223775A1 (en) Automated vehicle operation to compensate for sensor field-of-view limitations
WO2017184236A1 (en) Automated vehicle operation based on observed movement of other vehicles
EP3428680A1 (en) Automated braking system
US20180276484A1 (en) Transparency-characteristic based object classification for automated vehicle

Legal Events

Date Code Title Description
AS Assignment

Owner name: DELPHI TECHNOLOGIES, INC., MICHIGAN

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:WEI, JUNQING;XU, WENDA;REEL/FRAME:041880/0224

Effective date: 20170404

AS Assignment

Owner name: APTIV TECHNOLOGIES LIMITED, BARBADOS

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:DELPHI TECHNOLOGIES INC.;REEL/FRAME:047153/0902

Effective date: 20180101

STCF Information on status: patent grant

Free format text: PATENTED CASE

MAFP Maintenance fee payment

Free format text: PAYMENT OF MAINTENANCE FEE, 4TH YEAR, LARGE ENTITY (ORIGINAL EVENT CODE: M1551); ENTITY STATUS OF PATENT OWNER: LARGE ENTITY

Year of fee payment: 4