EP4031904A1 - Procédé de suivi d'objet - Google Patents

Procédé de suivi d'objet

Info

Publication number
EP4031904A1
EP4031904A1 EP20756767.8A EP20756767A EP4031904A1 EP 4031904 A1 EP4031904 A1 EP 4031904A1 EP 20756767 A EP20756767 A EP 20756767A EP 4031904 A1 EP4031904 A1 EP 4031904A1
Authority
EP
European Patent Office
Prior art keywords
movement information
radar
search window
radar targets
targets
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
EP20756767.8A
Other languages
German (de)
English (en)
Inventor
Andreas Eisenbarth
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.)
Continental Autonomous Mobility Germany GmbH
Original Assignee
Continental Autonomous Mobility Germany GmbH
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Continental Autonomous Mobility Germany GmbH filed Critical Continental Autonomous Mobility Germany GmbH
Publication of EP4031904A1 publication Critical patent/EP4031904A1/fr
Pending legal-status Critical Current

Links

Classifications

    • 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
    • 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
    • G01S13/66—Radar-tracking systems; Analogous systems
    • 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
    • G01S13/66—Radar-tracking systems; Analogous systems
    • G01S13/72—Radar-tracking systems; Analogous systems for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar

Definitions

  • the present invention relates to a method, in particular a computer-implemented method, for object tracking and accident detection by means of a radar sensor of an assistance system or driver assistance system and a driver assistance system in which object tracking and accident detection takes place in particular using the method according to the invention, a computer program for carrying out the method and a portable computer-readable storage medium on which the computer program for carrying out the method is stored.
  • Modern means of transport such as motor vehicles or motorcycles are increasingly being equipped with driver assistance systems, which capture the environment with the help of suitable sensors or sensor systems, recognize traffic situations and can support the driver zen, z. B. by braking and / or steering intervention or by the output of a visual, haptic or acoustic warning.
  • Radar sensors, lidar sensors, camera sensors, ultrasonic sensors or the like are regularly used as sensor systems for detecting the surroundings.
  • Conclusions about the environment can then be drawn from the sensor data determined by the sensors.
  • the environment detection by means of radar sensors is based z. B. on the emission of bundled electromagnetic waves and their reflection on objects such. B. other road users, obstacles on the road or the edge of the road.
  • the individual reflections or detections associated with an object are recorded as so-called radar targets by the radar sensor and z.
  • B. net zugeord the corresponding object by a suitable algorithm.
  • Such objects can be observed or tracked in a tracking manner, the object tracking (object tracking) should take place without gaps, ie. H. a tracked object should not be lost, e.g. B. as a result of a so-called "track demolition", as this can lead to incorrect conclusions or incorrect interpretation of the traffic scene. This in turn can mean that the driver assistance system of the host vehicle does not or only too late in the situation z. B. intervenes braking to prevent a rear-end collision.
  • One of the most complex situations that can arise for driver assistance systems is an accident situation involving a vehicle in front. For example, a vehicle driving ahead runs into an obstacle, although the host vehicle is not yet in this situation is affected. However, this represents an increased risk situation for the host vehicle, since the state of motion of the vehicle involved in the accident changes very quickly, to which the host vehicle has to react.
  • the dynamic range of the tracking algorithms stored in the driver assistance system can be adapted, but the dynamics that occur in such a situation represent extreme cases that can lead to track breaks, so that the desired continuity of the object being detected is interrupted.
  • the following problems can arise for tracking algorithms for accident situations involving objects traveling in front:
  • the situation often occurs spontaneously, i. H. without warning, so that the tracking algorithm cannot adapt to the situation in order to react accordingly.
  • the physical quantities occurring during an accident can exceed the physical quantities during the normal flow of traffic many times over.
  • a method for supporting a driver of a motor vehicle with a driver assistance system is known from DE 10 2011 001 248 A1, in which movement information of an object is recorded by means of a radar measuring device and is used for object tracking.
  • the problem arises that some movement parameters, such as the relative acceleration, cannot be measured by the radar measuring device or that inaccurate object models are available, which can result in object losses. If it is detected again, a new object is then initialized, although this measured value comes from the object that has already been tracked. Such measurement situations occur in particular if the measurement values are too far removed from the position predicted by the object model.
  • the loss of an object as a result of an abrupt change in acceleration is a problem that cannot be neglected for assistance functions of the generic type.
  • the present invention is therefore based on the object of providing an improved method for object tracking and accident detection and a corresponding assistance system in which the disadvantages of the prior art are overcome and object tracking is improved in a simple and cost-effective manner.
  • a radar sensor sends out radar signals in several successive measurement cycles, which are then reflected by the object and detected by the radar sensor as radar targets.
  • movement information of the object for object tracking is then determined, with the movement information defining a search window for the radar targets of the object.
  • the search window is expanded if, in successive measuring cycles, a change in the movement information is determined that exceeds a definable threshold value, and / or no more radar targets are detected for the tracked object.
  • the speed and / or the acceleration of the object is preferably used as the movement information.
  • This information is usually already determined in generic radar sensors or driver assistance systems comprising radar sensors, so that no additional or only insignificant hardware and / or computing effort is required.
  • the search window in relation to the speed can thus be expanded, e.g. B. by expanding the speed search window from 1-2 m / s (in the starting state) to at least 5 m / s, preferably to 7 m / s, in particular to 10 m / s.
  • the threshold value of the movement information can also be defined as a determined speed difference between two measuring cycles of more than 1 m / s, preferably more than 3 m / s, in particular more than 5 m / s.
  • the current measuring cycle can be repeated or the following measuring cycle can be started.
  • motion information patterns are stored ter for object tracking, for. B. in a memory of the vehicle or a Steue tion of a driver assistance system.
  • the object and / or a traffic situation can be classified by z. B. a comparison or comparison of the detected movement information of the object and the stored movement information patterns takes place.
  • the movement information patterns are, in particular, certain details, parameters and / or variables which allow conclusions to be drawn about an object class (e.g. accident vehicle) or a certain traffic scenario. For example, in the event of an abrupt and sharply changing speed of a vehicle driving ahead, e.g. B.
  • an accident scenario can be concluded.
  • an accident hypothesis can be substantiated, so that the respective vehicle can be classified as an accident vehicle.
  • radar targets can be assigned to the object, which were recorded in the extended search window if they correspond to a movement information pattern.
  • the expansion of the search window can expediently be reversed if no radar targets of the expanded search window can be assigned to the object in a definable number of measuring cycles.
  • the expansion of the search window is preferably limited to a definable number of measurement cycles, e.g. B. for the following three, in particular the following five, in particular the following ten or the like measuring cycles.
  • the acceleration of the object is determined based on a difference quotient from the speed and assigned to the object. For example, the high dynamics of an accident object or accident vehicle can be reported to the host vehicle, since the very high acceleration is transferred directly to the accident object as a property via the difference quotient without filtering.
  • a means can be provided with which the movement information of the object, the classification of the object and / or the classification of the traffic situation can be forwarded or sent.
  • This has the advantage that a classified as an accident object Vehicle or an accident situation via an interface, e.g. B. radio transmission or the like, is communicated to other road users (especially car-2-car communication or car-to-x communication) so that they can react accordingly to the situation (e.g. braking, accelerating, evasive maneuvers , Trajectory re-planning, sending emergency calls, sending optical, acoustic and haptic warnings and the like).
  • the classification can expediently be restricted to a definable area of the movement information.
  • a plausibility check of the determined movement information and / or the classification of the object and / or the classification of the traffic situation based on several measurement cycles is preferably provided, e.g. B. three, five, ten or the like measuring cycles.
  • the present invention further comprises a driver assistance system which carries out object tracking, in particular using the method according to the invention.
  • the driver assistance system has a radar sensor for object tracking, which sends out radar signals in successive measurement cycles, which are reflected by the object to be tracked and detected by the radar sensor as radar targets.
  • movement information such as B. the speed and / or acceleration of the object can be determined for object tracking.
  • the movement information defines a search window for the object's radar targets.
  • the search window can be determined on the basis of the speed of the object in such a way that the object would also have to be located within this search window in the following measurement cycles given predicted locomotion or trajectory.
  • a change in the movement information is determined in successive measuring cycles that exceeds a definable limit value (e.g. falls below or exceeds a definable speed or change in speed) and / or abruptly no radar targets or detections for the tracked object
  • a definable limit value e.g. falls below or exceeds a definable speed or change in speed
  • the search window is expanded.
  • the radar sensor is preferably a sensor which detects objects on the basis of emitted electromagnetic waves which are reflected on the objects and received again.
  • the electromagnetic waves can have different wave and frequency ranges.
  • the electromagnetic waves in a wavelength range from 1 mm to 10 km or frequency range from 300 GHz to 30 kHz, preferably in a wavelength range from 1 cm to 1000 m or frequency range from 30 GHz to 300 kHz, preferably in a wavelength range from 10 cm to 100 m or frequency range from 3 GHz to 3 MHz, particularly preferably in a wavelength range from 1 m to 10 m or frequency range from 300 MHz to 30 MHz.
  • the electromagnetic waves can also be in a wavelength range from 10 nm to 3 mm or frequency range from 30 PHz to 0.1 THz, preferably in a wavelength range from 380 nm to 1 mm or frequency range from 789 THz to 300 GHz, preferably in a wavelength range from 780 nm to 1 mm or frequency range from 385 THz to 300 GHz, particularly preferably in a wavelength range from 780 nm to 3 pm or frequency range from 385 THz to 100 THz.
  • the present invention further comprises a computer program with program code for carrying out the method according to the invention when the computer program is executed in a computer or another programmable computer known from the prior art.
  • the method can also be designed as a purely computer-implemented method, the term “computer-implemented method” in the context of the invention describing a sequence planning or procedure that is implemented or carried out using a computer.
  • the calculator such as B. a computer, a computer network or another known from the prior art programmable device (z. B. A computer device comprising a processor, microcontroller or the like) can process data by means of programmable arithmetic rules.
  • essential properties such. B. caused by a new program, new programs, an algorithm or the like.
  • the present invention comprises a computer-readable storage medium which comprises instructions which cause the computer on which they are executed to carry out a method according to at least one of the preceding claims.
  • the invention also expressly includes combinations of features of the features or claims, so-called sub-combinations, which are not explicitly mentioned.
  • FIG. 1 shows a simplified schematic representation of a traffic situation in which a host vehicle follows a vehicle traveling ahead and tracks it by means of suitable sensors;
  • FIG. 2 shows a simplified schematic illustration of a traffic situation following the one in FIG. 1, in which the vehicle traveling in front has an accident;
  • FIG. 3 shows a simplified schematic representation of the traffic situation from FIG. 2, with the predicted object position of the vehicle traveling ahead;
  • FIG. 4 shows a simplified schematic representation of the scan modes “near-scan” and “far-scan” of a long-range radar sensor, as well as
  • FIG. 5 shows a simplified schematic representation of a radar scan of a vehicle involved in an accident in front, which was continuously recorded and abruptly exhibits a negative acceleration.
  • Reference number 1 in Fig. 1 describes an ego vehicle which is equipped with a driver assistance system, the functions such as. B. ACC (Adaptive Cruise Control or distance control tempomat) and / or EBA (Emergency Breaking Assist or Notbremsassistent) and / or LKA (Lane Keep Assist or Lane Keep Assist or Lane Keeping / Lane Change Assistant) run or control and the environment or that Can detect the vehicle environment by means of suitable sensors and preferably classify it by means of a classifier.
  • the driver assistance system comprises a central control unit (ECU - Electronic Control Unit, ADCU Assisted & Automated Driving Control Unit), not shown in the figures.
  • the classifier can be stored as an independent module or as a software application or algorithm on the central control unit of the driver assistance system.
  • the sensor in the ego vehicle 1 is a radar sensor 2, in particular a long-range radar sensor, which has a forward-facing detection area 3.
  • a radar sensor 2 In front of the host vehicle 1 there is also another vehicle 4 driving ahead of the host vehicle 1, which is detected by the driver assistance system in the course of object tracking (object tracking) by means of the radar sensor 2.
  • the vehicle 4 can then be tracked by determining movement information (e.g. the speed or the acceleration of the vehicle 4) from the reflected detections or radar targets 5 of the vehicle 4.
  • the host vehicle 1 can predict the following movement or the trajectory of the vehicle 4 and the search area for the vehicle 4 associated with it Align or adapt the expected detections or the search window accordingly to the predicted object 6 shown in FIG. Furthermore, the vehicle 4 can then be classified by the classifier (for example as a car, truck, in the event of an accident as an accident vehicle and the like). The classification can also be included in the motion prediction. The traffic situation can thus be determined so that changes or hazards can be reacted to in good time with braking and / or steering interventions or speed adjustments, sending out warnings or the like.
  • the classifier for example as a car, truck, in the event of an accident as an accident vehicle and the like.
  • the classification can also be included in the motion prediction.
  • the traffic situation can thus be determined so that changes or hazards can be reacted to in good time with braking and / or steering interventions or speed adjustments, sending out warnings or the like.
  • the vehicle 2 driving ahead has an accident due to an obstacle 7, according to FIG. 3.
  • the average acceleration here during the accident at a speed of approx. 50 km / h is approx. 200 m / s 2 .
  • the maximum absolute acceleration when starting is only about 3-7 m / s 2 and the maximum absolute acceleration when braking hard is about -10 m / s 2 .
  • FIG. 4 shows the expected object position or the predicted object 6 from FIG. 2 as well as the actually recorded radar targets of the accident vehicle 4 from FIG. 3. These radar targets 5 are now outside the search area (which is here in the area of the predicted object 6). As a result, the original object or the vehicle 4 traveling ahead is lost. If the vehicle 4 is then recognized, a new object is created at a much lower speed or a stationary object. The information about the transition of the movement of the vehicle 4 from “moving” to “standing” is lost.
  • the vehicle 4 is marked as an accident object if its movement information is below a certain threshold value or limit value (threshold). For example, if its absolute acceleration is below -12 m / s 2 , ie it can no longer be explained by emergency braking. This takes place in that, in a first step, it is recognized that a disproportionately large change in the speed of the assigned radar targets 5 is present between two successive measurement cycles in the case of an object or vehicle that has hitherto been stably tracked. The radar targets 5 must still be within the normal search window for speeds.
  • a certain threshold value or limit value for example, if its absolute acceleration is below -12 m / s 2 , ie it can no longer be explained by emergency braking. This takes place in that, in a first step, it is recognized that a disproportionately large change in the speed of the assigned radar targets 5 is present between two successive measurement cycles in the case of an object or vehicle that has hitherto been stably tracked. The radar targets 5 must still be within the normal search window for
  • an object that has hitherto been stably tracked is no longer measured for no apparent reason, ie the object is not measured in the current computing cycle Radar targets 5 more assigned.
  • This can e.g. B. are due to the fact that the speed of the radar targets 5 has already changed so much that they are no longer within the search window age. Should such a situation be recognized, the speed search window for this object is enlarged in such a way that the radar targets 5 are also searched far outside of the previous speed search window. The position of the radar targets 5 must be in front of the object. Such measures can further reduce the probability of false positive events.
  • a search is made either again in the current measurement cycle or, beginning in the next measurement cycle, for radar detections which correspond to the accident hypothesis of the object or a movement information pattern stored in a memory. If corresponding detections are found, they are assigned to the accident candidate.
  • the entire accident scenario is over after just a few measuring cycles, possibly even after a single (with a cycle time of 70 ms) measuring cycle, and the accident object comes to a standstill. Therefore, if possible, the expansion of the search window should only be limited to a few measuring cycles. If no accident is confirmed within this time, the search window can be normalized again.
  • the inventive method is applicable to all, in particular, radar-based driver assistance systems, eg. B. Emergency Brake Assist (EBA, Emergency Brake Assist), active Lane Keeping Assist with steering assistance (LKA, Lane Keeping Assist), adaptive cruise control (ACC, Adaptive Cruise Control) or the like, but the focus is primarily on forward-facing sensor systems (front radar or long-range radar).
  • EBA Emergency Brake Assist
  • LKA Lane Keeping Assist
  • ACC Adaptive Cruise Control
  • Generic radar sensors can, for example, have different scan modes which can also include different opening angles of the detection area in order to illuminate the near range (near scan SR) and / or the far range (far scan FR) for the respective application, as in FIG. 5 shown.
  • the described traffic situation is detected in both radar scans (Near-Scan SR and Far-Scan FR), the difference in speed between two successive measurements being greater than a definable value for both scans independently of one another, preferably greater than 1 m / s.
  • This enables the expansion of the speed search windows, e.g. B. to 7 m / s (standard are approx. 1-2 m / s) for the following five measuring cycles.
  • a potential accident should be completely over by the end of this cycle time.
  • the acceleration can also be calculated using the difference quotient. If the acceleration value exceeds z. B.
  • the object is classified as an accident object and the acceleration is assigned to the tracked object. Furthermore, the use of a second scan for plausibility checks is not absolutely necessary, but this can reduce the number of incorrectly triggered events or even prevent them.
  • this information can then via an interface z. B. are made available to other vehicles or customers (car-2-car communication or car-to-x communication), for example as data information, as a radar scan or the like.
  • a radar scan (plotted as a function of acceleration a in m / s and time t in s) as a measurement result is shown in FIG. 6, in which the object or the vehicle in front of the accident was continuously recorded and the acceleration of -50 m for a short period of time / s 2 occurs.
  • the specified parameters can also be varied in such a way that accidents are limited to a certain speed range, which means that the determination reliability can be improved even further.

Abstract

L'invention concerne un procédé de suivi d'objet, dans lequel un capteur radar (2) émet des signaux radar dans des cycles de mesure successifs, lesdits signaux radar étant réfléchis par l'objet et capturés par le capteur radar (2) en tant que cibles radar (5), des informations de mouvement concernant l'objet pour le suivi d'objet étant déterminées sur la base des cibles radar (5) et une fenêtre de recherche pour les cibles radar (5) de l'objet étant réglée sur la base des informations de mouvement, la fenêtre de recherche étant élargie si un changement des informations de mouvement qui dépasse une valeur limite définissable est déterminé dans des cycles de mesure successifs et/ou si aucune cible radar (5) de l'objet suivi n'est plus détectée.
EP20756767.8A 2019-09-20 2020-07-15 Procédé de suivi d'objet Pending EP4031904A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102019214383.0A DE102019214383A1 (de) 2019-09-20 2019-09-20 Verfahren zur Objektverfolgung
PCT/DE2020/200058 WO2021052542A1 (fr) 2019-09-20 2020-07-15 Procédé de suivi d'objet

Publications (1)

Publication Number Publication Date
EP4031904A1 true EP4031904A1 (fr) 2022-07-27

Family

ID=72086653

Family Applications (1)

Application Number Title Priority Date Filing Date
EP20756767.8A Pending EP4031904A1 (fr) 2019-09-20 2020-07-15 Procédé de suivi d'objet

Country Status (6)

Country Link
US (1) US20220334246A1 (fr)
EP (1) EP4031904A1 (fr)
JP (1) JP7457104B2 (fr)
CN (1) CN114402224A (fr)
DE (1) DE102019214383A1 (fr)
WO (1) WO2021052542A1 (fr)

Family Cites Families (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP3269471B2 (ja) 1998-12-04 2002-03-25 三菱電機株式会社 マルチビーム・レーダ装置
JP5183661B2 (ja) * 2010-03-29 2013-04-17 三菱電機株式会社 車載レーダ装置
DE102011001248A1 (de) * 2011-03-14 2012-09-20 Dr. Ing. H.C. F. Porsche Aktiengesellschaft Verfahren zur Unterstützung eines Fahrers
JP5910046B2 (ja) * 2011-12-05 2016-04-27 トヨタ自動車株式会社 障害物検出装置
JP5962706B2 (ja) * 2014-06-04 2016-08-03 トヨタ自動車株式会社 運転支援装置
JP6453695B2 (ja) * 2015-03-31 2019-01-16 株式会社デンソー 運転支援装置、及び運転支援方法
JP6650344B2 (ja) * 2015-10-02 2020-02-19 パナソニック株式会社 物体検出装置及び物体検出方法
JP6857971B2 (ja) * 2016-06-13 2021-04-14 株式会社デンソーテン レーダ装置および信号処理方法
JP6597517B2 (ja) * 2016-08-10 2019-10-30 株式会社デンソー 物標検出装置
US10564276B2 (en) * 2017-03-02 2020-02-18 GM Global Technology Operations LLC Adaptive process noise description for improved kalman filter target tracking
JP7173735B2 (ja) * 2018-02-20 2022-11-16 株式会社デンソー レーダ装置及び信号処理方法

Also Published As

Publication number Publication date
CN114402224A (zh) 2022-04-26
US20220334246A1 (en) 2022-10-20
DE102019214383A1 (de) 2021-03-25
JP2022545513A (ja) 2022-10-27
JP7457104B2 (ja) 2024-03-27
WO2021052542A1 (fr) 2021-03-25

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