EP4181101A1 - Verfahren, vorrichtung, speichermedium und fahrzeug zur vorhersage des verkehrsflusses - Google Patents

Verfahren, vorrichtung, speichermedium und fahrzeug zur vorhersage des verkehrsflusses Download PDF

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Publication number
EP4181101A1
EP4181101A1 EP22206769.6A EP22206769A EP4181101A1 EP 4181101 A1 EP4181101 A1 EP 4181101A1 EP 22206769 A EP22206769 A EP 22206769A EP 4181101 A1 EP4181101 A1 EP 4181101A1
Authority
EP
European Patent Office
Prior art keywords
vehicle
state determination
following state
target vehicle
determination counter
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
EP22206769.6A
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English (en)
French (fr)
Inventor
Hang Zhou
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.)
NIO Technology Anhui Co Ltd
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NIO Technology Anhui Co 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 NIO Technology Anhui Co Ltd filed Critical NIO Technology Anhui Co Ltd
Publication of EP4181101A1 publication Critical patent/EP4181101A1/de
Pending legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
    • G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G08G1/0133—Traffic data processing for classifying traffic situation
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
    • G08G1/0141—Measuring and analyzing of parameters relative to traffic conditions for specific applications for traffic information dissemination
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
    • G08G1/0145—Measuring and analyzing of parameters relative to traffic conditions for specific applications for active traffic flow control
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
    • G08G1/0175—Detecting movement of traffic to be counted or controlled identifying vehicles by photographing vehicles, e.g. when violating traffic rules
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed

Definitions

  • the disclosure relates to the field of intelligent driving, and specifically provides a method, apparatus, storage medium, and a vehicle for predicting traffic flow.
  • traffic flow is an important type of input information, which can assist a vehicle in making a better decision, including: adjusting a maximum driving speed limit in real time, such as where when there is congestion ahead, the maximum speed limit may be lowered in advance, to avoid poor passenger experience due to sudden deceleration; making a lane-level decision, such as to prevent a lane change to a lane with a similar or lower traffic speed; and assisting target prediction, such as where when the traffic flow of a lane next to a current lane is significantly lower than that of the current lane, a target vehicle on the front side has a greater probability of invading the current lane, in which case predicting a behavior of a neighboring vehicle in advance can effectively avoid a collision risk.
  • a high-definition map can be used to provide roadside-based traffic congestion information.
  • the high-definition map depends on statistical data, and has a lower real-time performance than a sensing result from a sensor on the vehicle.
  • the congestion information is provided mostly based on roads, which results in a failure to describe the traffic flow in each lane in a subdivided manner. How to obtain vehicle information through a vehicle sensor to accurately predict lane-level traffic flow information has become an urgent problem to be solved.
  • the disclosure aims to solve the foregoing technical problem, that is, to solve the problem of how to obtain vehicle information of a target lane through a sensor on the vehicle and accurately predict lane-level traffic flow.
  • the disclosure provides a method for predicting traffic flow on the vehicle.
  • the method includes:
  • the step of "determining whether the target vehicle is in a following state based on the vehicle information of the target vehicle and the reference vehicle and a non-free driving state of the target vehicle relative to the reference vehicle" specifically includes:
  • the step of "performing traffic flow prediction on the vehicle based on the vehicle information of the target vehicle when the target vehicle is in the following state" specifically includes: taking the speed of the target vehicle as an average speed of the vehicles in the target lane.
  • ⁇ is the non-free driving state determination identifier
  • ⁇ n ( t - T) is a speed value of the target vehicle at a moment t - T
  • ⁇ n +1 ( t - T) is a speed value of the reference vehicle at a moment t - T
  • ⁇ n +1 ( t ) is an acceleration value at a moment t
  • T is a reaction time constant of a driver.
  • step S34 further includes:
  • the disclosure provides apparatus for predicting traffic flow on the vehicle.
  • the apparatus includes:
  • the following state determination module is further configured to perform the following operations:
  • the traffic flow prediction module is further configured to perform the following operation: taking the speed of the target vehicle as an average speed of the vehicles in the target lane.
  • the disclosure provides a storage medium adapted to store a plurality of program codes, where the program codes are adapted to be loaded and run by a processor to perform a method for predicting traffic flow on the vehicle according to any one of the foregoing solutions.
  • the disclosure provides a vehicle including a vehicle body, a processor, and a memory, where the memory is adapted to store a plurality of program codes, and the program codes are adapted to be loaded and run by the processor to perform a method for predicting traffic flow on the vehicle according to any one of the foregoing solutions.
  • the disclosure makes it possible to obtain vehicle data of a vehicle in the target lane through a sensor on the vehicle, and determine whether a moving speed of the target vehicle may represent average vehicle traffic of the lane by analyzing a motion state between the target vehicle and an adjacent vehicle, that is, a vehicle that has a following relationship with the target vehicle, thereby obtaining more accurate average lane-level traffic flow and providing more accurate data support for intelligent driving.
  • FIG. 1 is a schematic diagram of positions of vehicles on a road according to an embodiment of the disclosure.
  • simply a driving speed of a vehicle closest to an ego vehicle or an average speed of vehicles in a detected lane is used to represent a traffic flow rate.
  • each vehicle on a road is diverse in terms of motion, and the above processing method is often one-sided, which may sometimes result in a misleading to an accurate determination of the traffic flow, and then causes a driving assistance system to make some inappropriate decisions, thereby affecting the user experience.
  • FIG. 1 there is a large difference in driving speed between a vehicle Veh1 in a left lane that is adjacent to a vehicle Ego1 and a plurality of vehicles far away from the adjacent vehicle.
  • traffic flow of the lane is determined simply based on the speed of the adjacent vehicle, there may be inaccurate predictions, which will prevent the driving assistance system from making a correct decision.
  • the Ego1 may mistakenly consider that the traffic flow speed in the left lane is high due to a high speed of the Veh1.
  • the driving assistance system may give inappropriate prompt information indicating a lane change to the left side, in order to achieve a higher driving speed.
  • the method for predicting traffic flow on the vehicle based on an analysis of a non-free moving state of a vehicle of the disclosure is desired to determine whether the motion of one of vehicles in the target lane that have a leading-and-following relationship is representative by determining whether there is a restrictive relationship between the motions of the vehicles, thereby obtaining predicted traffic flow information of the target lane.
  • FIG. 2 is a flowchart of main steps of a method for predicting traffic flow on the vehicle according to an embodiment of the disclosure. As shown in FIG. 2 , the method for predicting traffic flow on the vehicle of the disclosure includes:
  • both the target vehicle and the reference vehicle should be located in a same lane, and the reference vehicle is located in front of the target vehicle and is adjacent to the target vehicle.
  • an adjacent vehicle on the front side of an ego vehicle that may have a great influence on the driving of the ego vehicle is selected as the target vehicle.
  • the ego vehicle is Ego1
  • a vehicle Veh1 in an adjacent lane on the left side of the ego vehicle is selected as the target vehicle.
  • An adjacent vehicle Veh2 in the same lane as and in front of the vehicle Veh1 is selected as the reference vehicle, and there should be no other vehicles between the vehicle Veh1 and the vehicle Veh2.
  • step S2 the vehicle information of the target vehicle and the reference vehicle is obtained, where the vehicle information includes a vehicle code, an acceleration, and a speed.
  • a method for obtaining the vehicle information is not limited in the disclosure.
  • a vehicle sensor such as one or more of an acceleration sensor, a speed sensor, a vehicle image sensor, an onboard laser radar, an onboard ultrasonic radar, etc.
  • data such as license plate numbers, exterior characteristics, and colors, of a plurality of vehicles in a detection area, and the data is fused with the speed, acceleration and other data of the ego vehicle, to obtain the characteristics of each vehicle.
  • Each vehicle is assigned a unique vehicle code, that is, unique ID data, and speed, acceleration and other information of the vehicle are obtained.
  • vehicle characteristics obtained by processing data from the vehicle sensor need to be compared with vehicle characteristics recorded at a previous moment. If they are the same, a same vehicle code is assigned, and if they are different, a new vehicle code is assigned.
  • FIG. 3 is a specific implementation method of step S3.
  • step S31 the vehicle information of the target vehicle and the reference vehicle are obtained in real time at a set time interval, the vehicle information including: a current ID (vehicle code) of the target vehicle, an acceleration of the target vehicle, a speed of the target vehicle, a current ID (vehicle code) of the reference vehicle, an acceleration of the reference vehicle, a speed of the reference vehicle, etc.
  • the time interval at which the vehicle information is obtained may be set depending on factors such as the type of a vehicle sensor, a processing speed of sensor data, and a current speed of the ego vehicle. For example, the time interval may be set to 50 milliseconds.
  • step S31 if the vehicle code of the target vehicle is not obtained in step S31, it indicates that the target vehicle may have moved away from the target lane or not be within a detection range of the vehicle sensor. In this case, there is a need to return to step S1 for reselection of the target vehicle and the reference vehicle.
  • steps S32 and S33 a comparison is made as to whether a real-time vehicle code of the reference vehicle that is obtained in real time is the same as a historical vehicle code of the reference vehicle, that is, a comparison is made as to whether the current ID of the reference vehicle is the same as a historical ID of the reference vehicle, and whether the reference vehicle in front of the target vehicle at the current moment and the reference vehicle at a previous moment are the same vehicle is checked.
  • both the acceleration following state determination counter and the deceleration following state determination counter are adjusted to a preset minimum value (in this embodiment, the preset minimum value is set to 0).
  • step S31 for loop detection the historical vehicle code of the reference vehicle is updated, and an association relationship between the target vehicle and the reference vehicle is re-established.
  • step S35 is performed, in which a non-free driving state determination identifier is calculated.
  • a vehicle in a non-free driving state has the following three main characteristics: restriction, latency, and transfer. It is the three characteristics that are exactly used in the disclosure to determine a motion relationship between leading and following vehicles.
  • T is a parameter related to the driver's responsiveness. As an example, it is usually set to 1
  • Formula (1) reflects that a difference in speeds between the leading and following vehicles may affect the acceleration of subsequent vehicles (restriction), with a delay of T time (latency), and the value of the constant F reflects the association between the leading and following vehicles.
  • step S36 it is determined whether the non-free driving state determination identifier f is greater than 0.
  • step S37 is performed, in which both the acceleration following state determination counter and the deceleration following state determination counter are decreased by a first preset count value (in this embodiment, the first preset count value is 1).
  • the acceleration following state determination counter and the deceleration following state determination counter are respectively less than the preset minimum value. If less than the preset minimum value, the acceleration following state determination counter and/or the deceleration following state determination counter are/is adjusted to the preset minimum value.
  • step S38 is perform, in which it is determined whether the acceleration of the target vehicle is positive or negative.
  • the acceleration following state determination counter is increased by a second preset count value (in this embodiment, the second preset count value is 1) in step S39; in addition, whether the acceleration following state determination counter with the second preset count value added exceeds a first preset threshold is checked, and if so, the acceleration following state determination counter is assigned the first preset threshold, and step S3B is performed.
  • the deceleration following state determination counter is increased by a third preset count value (in this embodiment, the third preset count value is 1) in step S3A; in addition, whether the deceleration following state determination counter with the third preset count value added exceeds a second preset threshold is checked, and if so, the deceleration following state determination counter is assigned the second preset threshold, and step S3B is performed.
  • the third preset count value is 1 in step S3A; in addition, whether the deceleration following state determination counter with the third preset count value added exceeds a second preset threshold is checked, and if so, the deceleration following state determination counter is assigned the second preset threshold, and step S3B is performed.
  • step S3B it is determined whether the acceleration following state determination counter with the second preset count value added is greater than a first following state determination threshold, and it is also determined whether the deceleration following state determination counter with the third preset count value added is greater than a second following state determination threshold.
  • step S3C is performed, in which it is determined that the target vehicle is in the following state relative to the reference vehicle.
  • step S31 is returned for loop detection.
  • Case 1 the acceleration following state determination counter is less than the first following state determination threshold, and the deceleration following state determination counter is greater than the second following state determination threshold.
  • the preset minimum value, the first preset threshold, and the second preset threshold are set to delimit a valid value range of the acceleration following state determination counter and the deceleration following state determination counter, thereby ensuring the real-time performance of following state determination while ensuring the accuracy of following state determination, and thus providing more adaptability to practical applications.
  • the preset minimum value, the first preset threshold, the second preset threshold, the first following state determination threshold, the second following state determination threshold, etc. may be set in combination with the set time interval in step S31, road conditions, etc., and therefore, they are set by practical experience.
  • the first following state determination threshold and the second following state determination threshold may be both set to 10
  • the preset minimum value may be set to 0,
  • the first preset threshold and the third threshold may be set to 20.
  • the first preset count value, the second preset count value, and the third preset count value are all set to 1.
  • Inventors in the art may also set the above preset values according to actual conditions, but such settings of different values should not be considered as going beyond the scope of the disclosure.
  • traffic flow prediction on the vehicle may be performed based on the information of the target vehicle, that is, by taking the speed of the target vehicle as an average speed of the vehicles in the target lane.
  • the disclosure also provides a apparatus for predicting traffic flow on the vehicle.
  • the apparatus for predicting traffic flow on the vehicle 4 in an embodiment of the disclosure mainly includes: a vehicle information obtaining module 41, a following state determination module 42, and a traffic flow prediction module 43.
  • the vehicle information obtaining module 41 is configured to obtain, by a vehicle sensor, vehicle information of a target vehicle and a reference vehicle on a vehicle driving road and of other vehicles within a detection range of the vehicle sensor. As shown in FIG. 5 , in an embodiment, the vehicle information obtaining module 41 may further include a sensor sub-module 41a and a sensor data processing sub-module 41b.
  • the sensor sub-module 41a may be one or more of an acceleration sensor, a speed sensor, a vehicle image sensor, an onboard laser radar, an onboard ultrasonic radar, etc.
  • the sensor sub-module 41a obtains data, such as license plate numbers, exterior features, colors, speeds, and accelerations, of the ego vehicle within the detection range of the vehicle sensor and the plurality of surrounding vehicles.
  • the sensor data processing sub-module 41b performs data fusion to obtain the characteristics of each vehicle, assigns a unique vehicle code to each vehicle, that is, unique ID data, and obtains speed, acceleration and other information of the vehicle.
  • the following state determination module 42 is configured to select a target vehicle and a reference vehicle, and determine whether the target vehicle is in a following state by detecting a non-free driving state of the target vehicle relative to the reference vehicle based on vehicle information, such as a vehicle code, an acceleration, and a speed, of the target vehicle and the reference vehicle. As shown in FIG. 5 , in an embodiment, the following state determination module 42 may further include a target selection sub-module 42a, a data calculation sub-module 42b, and a determination sub-module 42c.
  • the target selection sub-module 42a is configured to select a target vehicle and a reference vehicle from the vehicle information obtained by the vehicle information obtaining module 41.
  • a target vehicle Preferably, an adjacent vehicle on the front side of an ego vehicle that may have a great influence on the driving of the ego vehicle is generally selected as the target vehicle.
  • a vehicle that is located in a same lane as the target vehicle and is in front of the target vehicle is selected as the reference vehicle, and the target vehicle is adjacent to the reference vehicle, that is, there are no other vehicles between the target vehicle and the reference vehicle.
  • the data calculation sub-module 42b is configured to calculate a non-free driving state determination identifier by using formula (2) based on the speed, acceleration, and other information of the target vehicle and the reference vehicle.
  • the determination sub-module 42c is configured to determine whether the target vehicle is in a following state based on ID data of the reference vehicle, the non-free driving state determination identifier, and the acceleration information of the target vehicle.
  • Specific determination conditions include: the ID of the reference vehicle needs to remain unchanged, the target vehicle needs to be in a non-free driving state relative to the reference vehicle, and both the count values of acceleration and deceleration of the target vehicle need to exceed the set determination thresholds.
  • the traffic flow prediction module 43 is configured to perform traffic flow prediction on the vehicle according to the information of the target vehicle upon determining that the target vehicle is in the following state relative to the reference vehicle, that is, by taking the speed of the target vehicle as an average speed of the vehicles in the target lane.
  • the disclosure further provides a storage medium.
  • the storage medium may be configured to store a program for performing the method for predicting traffic flow on the vehicle in the foregoing method embodiments, where the program may be loaded and run by a processor to implement the foregoing the method for predicting traffic flow on the vehicle.
  • the storage medium may be a storage device formed by various electronic devices.
  • the storage medium in the embodiments of the disclosure is a non-transitory computer-readable storage medium.
  • the disclosure further provides a vehicle including a vehicle body, a processor, and a memory.
  • vehicle body may be an electric vehicle; the processor and the memory are mounted on the vehicle body and are powered by the vehicle body; and the memory may be configured to store a program for performing the method for predicting traffic flow on the vehiclein the foregoing method embodiments, where the program may be loaded and run by the processor to implement the foregoing the method for predicting traffic flow on the vehicle.
  • the memory may be a storage device formed by various electronic devices.
  • the memory in the embodiments of the disclosure is a non-transitory readable storage medium.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Traffic Control Systems (AREA)
EP22206769.6A 2021-11-11 2022-11-10 Verfahren, vorrichtung, speichermedium und fahrzeug zur vorhersage des verkehrsflusses Pending EP4181101A1 (de)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111334849.9A CN114005279A (zh) 2021-11-11 2021-11-11 车端交通流预测方法、装置、存储介质和车辆

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EP4181101A1 true EP4181101A1 (de) 2023-05-17

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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130116909A1 (en) * 2010-07-29 2013-05-09 Toyota Jidosha Kabushiki Kaisha Vehicle control system
EP2991055A1 (de) * 2013-05-30 2016-03-02 Mitsubishi Heavy Industries, Ltd. Simulationsvorrichtung, simulationsverfahren und programm

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130116909A1 (en) * 2010-07-29 2013-05-09 Toyota Jidosha Kabushiki Kaisha Vehicle control system
EP2991055A1 (de) * 2013-05-30 2016-03-02 Mitsubishi Heavy Industries, Ltd. Simulationsvorrichtung, simulationsverfahren und programm

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
DURRANI UMAIR ET AL: "Calibrating the Wiedemann's vehicle-following model using mixed vehicle-pair interactions", TRANSPORTATION RESEARCH PART C:EMERGING TECHNOLOGIES, PERGAMON, NEW YORK, NY, GB, vol. 67, 7 March 2016 (2016-03-07), pages 227 - 242, XP029535749, ISSN: 0968-090X, DOI: 10.1016/J.TRC.2016.02.012 *
ZHAO CHEN ET AL: "A Study on an Anthropomorphic Car-Following Strategy Framework of the Autonomous Coach in Mixed Traffic Flow", IEEE ACCESS, IEEE, USA, vol. 8, 6 April 2020 (2020-04-06), pages 64653 - 64665, XP011783725, DOI: 10.1109/ACCESS.2020.2985749 *

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