METHOD FOR THE RECOGNITION OF A MOVING PEDESTRIAN

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First Claim
1. A method for the recognition of a moving pedestrian by means of a radar sensor system, whereintransmitting a primary radar signal into an observation space;
 andreceiving a secondary radar signal reflected by the moving pedestrian; and
processing the secondary radar signal, wherein processing the secondary radar signal includes the steps;
(i) generating a MicroDoppler spectrogram of the secondary radar signal;
(ii) determining, based on the MicroDoppler spectrogram, an observed bulk speed of the moving pedestrian, wherein the observed bulk speed corresponds to a speed component that is related to the pedestrian'"'"'s torso and is oriented along a line of sight extending from the moving pedestrian to the radar sensor system;
(iii) determining, based on the MicroDoppler spectrogram, at least one gait cycle parameter of the moving pedestrian; and
(iv) determining, based on the determined observed bulk speed and the determined gait cycle parameter, an illumination angle between a moving direction of the moving pedestrian and the line of sight.
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Abstract
A method for the recognition of a moving pedestrian by means of a radar sensor system includes the steps of transmitting a primary radar signal into an observation space and of receiving a secondary radar signal reflected by the moving pedestrian. The secondary radar signal is processed, wherein the processing of the secondary radar signal includes the steps of generating a MicroDoppler spectrogram of the secondary radar signal, determining, based on the MicroDoppler spectrogram, an observed bulk speed of the moving pedestrian, determining, based on the MicroDoppler spectrogram, at least one gait cycle parameter of the moving pedestrian and determining, based on the determined observed bulk speed and the determined gait cycle parameter, an illumination angle between a moving direction of the moving pedestrian and the line of sight.
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15 Claims
 1. A method for the recognition of a moving pedestrian by means of a radar sensor system, wherein
transmitting a primary radar signal into an observation space;  and
receiving a secondary radar signal reflected by the moving pedestrian; and processing the secondary radar signal, wherein processing the secondary radar signal includes the steps; (i) generating a MicroDoppler spectrogram of the secondary radar signal; (ii) determining, based on the MicroDoppler spectrogram, an observed bulk speed of the moving pedestrian, wherein the observed bulk speed corresponds to a speed component that is related to the pedestrian'"'"'s torso and is oriented along a line of sight extending from the moving pedestrian to the radar sensor system; (iii) determining, based on the MicroDoppler spectrogram, at least one gait cycle parameter of the moving pedestrian; and (iv) determining, based on the determined observed bulk speed and the determined gait cycle parameter, an illumination angle between a moving direction of the moving pedestrian and the line of sight.  View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
 and
1 Specification
The present invention relates to a method for the recognition of a moving pedestrian by means of a radar sensor system, wherein a primary radar signal is transmitted into an observation space, and a secondary radar signal reflected by the moving pedestrian is received and processed.
Radar sensor systems are used in a wide range of applications. For example, modern motor vehicles are often equipped with radar sensor systems to detect other vehicles, obstacles or vulnerable road users such as pedestrians or cyclists. A detection and classification of objects in a traffic space from a host vehicle is in particular needed for various advanced driver assistance systems (ADAS), such as advanced emergency braking (AEB) systems, collision avoidance systems and autonomous driving systems. Often, the known Doppler effect is used to gather information relating to moving objects. The Doppler effect or Doppler shift is a change in frequency observed when a wave source moves relative to the receiver. In case of a pedestrian crossing the street in front of a motor vehicle, the Doppler effect is the less distinct, the closer the pedestrian approaches the center of the lane. In practice, it is therefore difficult to correctly identify pedestrians crossing the lane.
This disclosure describes techniques to enable a reliable recognition of moving pedestrians even under adverse circumstances.
In accordance with the invention, the processing of the secondary radar signal includes the steps:
 (i) generating a MicroDoppler spectrogram of the secondary radar signal;
 (ii) determining, based on the MicroDoppler spectrogram, an observed bulk speed of the moving pedestrian, wherein the observed bulk speed corresponds to a speed component that is related to the pedestrian'"'"'s torso and is oriented along a line of sight extending from the moving pedestrian to the radar sensor system;
 (iii) determining, based on the MicroDoppler spectrogram, at least one gait cycle parameter of the moving pedestrian; and
 (iv) determining, based on the determined observed bulk speed and the determined gait cycle parameter, an illumination angle between a moving direction of the moving pedestrian and the line of sight.
The Doppler frequency shift mainly results from the movement of the observed object as a whole, i.e. in case of an observed pedestrian from the movement of the pedestrian'"'"'s torso. Beside this shift resulting from the main body movement, there usually are sidebands relating to moving parts of the object. For example, swinging arms or legs of a pedestrian can cause additional Doppler shifts. Such additional shifts are discernible in a MicroDoppler spectrogram. The generation of MicroDoppler spectrograms is disclosed, for example, in the book of Chen V. C., “The MicroDoppler Effect in Radar”, Artech House, 2011, or in the paper of Yan et al., “MicroDoppler Based Classifying Features for Automotive Radar VRU Target Classification”, 25^{th }International Technical Conference on the Enhanced Safety of Vehicles (ESV), Jun. 58, 2017, Detroit, Mich., United States.
The superposition of Doppler shifts from each individual component is called a “MicroDopplersignature”. MicroDopplersignatures can be analyzed to classify detected objects. The use of Doppler spectrograms for a discrimination of pedestrians is disclosed, for example, in the paper of Gürbüz S. Z. et al., “Detection and Identification of Human Targets in Radar Data”, SPIE 6567, Signal Processing, Sensor Fusion and Target Recognition XVI, 656701, May 2007.
By means of a method in accordance of the invention, it is possible to determine the illumination angle in a quick and easy manner merely by analyzing the MicroDoppler spectrogram. In the field of radarbased object recognition, the illumination angle is an important quantity to be used in various processing and evaluation steps. The knowledge of the illumination angle enables a particularly reliable detection of crossing pedestrians. In particular, the speed of a pedestrian in the moving direction can be calculated if the illumination angle is known.
In principle, the illumination angle can be estimated by means of an anglefinding process. Such a process, however, requires a multiple channel radar system, which is rather costly. In contrast, a method in accordance with the invention is not dependent on the presence of a multiple channel radar sensor and may be implemented directly in the rangeDoppler domain. The invention thus enables the fabrication of lowcost pedestrian recognition systems.
Advantageous embodiments of the invention can be seen from the dependent claims, from the following description and from the appended figures.
Preferably, the speed of the moving pedestrian in the moving direction is determined based on the determined observed bulk speed and the determined illumination angle, in particular using the formula:
v=v^{ob}/cos(θ)
wherein v is the speed of the moving pedestrian in the moving direction, v^{ob }is the observed bulk speed and θ is the illumination angle. In particular for an advanced driver assistance system, it is advantageous to know the speed of a detected pedestrian in the moving direction. For example, the potential danger of a traffic situation, in particular an imminent collision with the pedestrian, can be estimated if the speed of the pedestrian in the moving direction is known.
The MicroDoppler spectrogram may be generated by means of a timefrequency analysis, in particular by means of a ShortTimeFourierTransform (STFT) or a WignerVilleDistribution technique (WVD technique). Dependent on the application, the secondary radar signal or a signal derived from the secondary radar signal may be subjected to a timefrequency analysis.
The observed bulk speed can be determined by means of a percentilebased method or a curvefitting method. Specifically, it is possible to calculate the cumulative amplitude distribution for each time slice and to determine the observed bulk speed to correspond to a percentile of about 50% of the cumulative amplitude distribution function. According to an embodiment of the invention, a percentilebased method or a curvefitting method as disclosed in the paper of Gürbüz S. Z. et al., “Operational assessment and adaptive selection of microDoppler features”, IET Radar Sonar Navig., Vol. 9, Iss. 9, pp. 11961204, 2015, may be used to determine the observed bulk speed.
Preferably, the at least one gait cycle parameter is an average duration of the pedestrian'"'"'s gait cycle, an average gait cycle repetition frequency or a number of time bins related to one gait cycle. One gait cycle usually corresponds to two footsteps.
According to an embodiment of the invention, the at least one gait cycle parameter is determined by means of a Fourier analysis. A Fourier analysis enables the extraction of periodic components related to specific movements of body parts from the MicroDoppler spectrogram.
Specifically, a Fast Fourier Transform (FFT) may be applied to an upper envelope and/or to a lower envelope of the MicroDoppler spectrogram to estimate the gait cycle repetition frequency. The upper envelope and/or the lower envelope can be determined by means of a percentilebased method or a curvefitting method as mentioned above. Specifically, it is possible to calculate the cumulative amplitude distribution for each time slice and to determine the upper envelope to correspond to a percentile of about 95% of the cumulative amplitude distribution function and/or to determine the lower envelope to correspond to a percentile of about 5% of the cumulative amplitude distribution function.
According to a further embodiment of the invention, the step (iv) includes relating the determined gait cycle parameter to the determined observed speed and to an estimated height of the moving pedestrian'"'"'s thigh. The relationship to be used can be based on a kinematic human model.
The height of the moving pedestrian'"'"'s thigh may be estimated based on a relationship between the height of the moving pedestrian'"'"'s thigh and the overall height of the pedestrian, in particular based on the following relationship:
H_{t}≈0.53·H_{ped }
wherein H_{t }is the height of the moving pedestrian'"'"'s thigh and H_{ped }is the overall height of the pedestrian. This simple relationship has proved to provide sufficiently precise results.
In accordance with an embodiment of the invention, the overall height of the moving pedestrian is estimated based on a statistical average height of pedestrians. It has shown that the typical height of pedestrians is between 1.2 m and 1.8 m. Therefore, H_{ped }may be set to 1.5 m. This enables a simple calculation of the illumination angle.
In step (iv), the illumination angle may be estimated by means of a recursive state estimator, in particular an Extended Kalman Filter (EKF). By means of a recursive state estimator, it is possible to achieve a particularly high precision. The recursive state estimator may use a state vector including the illumination angle and the gait cycle parameter as well as a measurement vector including the observed bulk speed and the gait cycle parameter.
The invention also relates to a system for the recognition of a moving pedestrian comprising a radar sensor system for transmitting a primary radar signal into an observation space and for receiving a secondary radar signal reflected by the moving pedestrian and an electronic processing device for processing the secondary radar signal.
According to the invention, the electronic processing device is configured for carrying out a method as disclosed above.
Dependent on the application, the electronic processing device may be united with the radar sensor system or configured as a separate unit. The electronic processing device may comprise a computer.
Preferably, the radar sensor system is configured to be mounted at or in a motor vehicle.
The invention further relates to a computer program product including executable program code which, when executed, carries out a method as disclosed above.
The disclosure with reference to the inventive method is also true for the inventive system and the inventive computer program product.
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 nonlimiting example only and with reference to the accompanying drawings.
Subsequently, the present invention is explained in more detail based on an exemplary embodiment with reference to the accompanying figures, wherein:
Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the various described embodiments. However, it will be apparent to one of ordinary skill in the art that the various described embodiments may be practiced without these specific details. In other instances, wellknown methods, procedures, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.
‘One or more’ includes a function being performed by one element, a function being performed by more than one element, e.g., in a distributed fashion, several functions being performed by one element, several functions being performed by several elements, or any combination of the above.
It will also be understood that, although the terms first, second, etc. are, in some instances, used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first contact could be termed a second contact, and, similarly, a second contact could be termed a first contact, without departing from the scope of the various described embodiments. The first contact and the second contact are both contacts, but they are not the same contact.
The terminology used in the description of the various described embodiments herein is for describing embodiments only and is not intended to be limiting. As used in the description of the various described embodiments and the appended claims, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses all possible combinations of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises,” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
As used herein, the term “if” is, optionally, construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context. Similarly, the phrase “if it is determined” or “if [a stated condition or event] is detected” is, optionally, construed to mean “upon determining” or “in response to determining” or “upon detecting [the stated condition or event]” or “in response to detecting [the stated condition or event],” depending on the context.
In
In operation, the motor vehicle 11 is moving in a driving direction 15 on a lane 17. A pedestrian 19 crossing the lane 17 is moving in a moving direction 21. The moving direction 21 is at least essentially perpendicular to the driving direction 15. In principle, the moving direction 21 may be inclined to the driving direction 15.
The radar sensor system 13 is configured for transmitting a primary radar signal into the traffic space 23 in front of the motor vehicle 11 and for detecting pedestrians 19 present in the traffic space 23 on the basis of a secondary radar signal reflected by the pedestrians 19. The line 25 which extends from the pedestrian 19 to the active region of the radar sensor system 13 is called “line of sight”. The observed bulk speed v^{ob }of the pedestrian 19, i. e. the speed component related to the pedestrian'"'"'s torso 27 and oriented along the line of sight 25, can be determined in a known manner using the Doppler effect. Specifically, it is known that the relationship between the observed bulk speed v^{ob }and the pedestrian'"'"'s speed v in the moving direction 21 is given as:
v=v^{ob}/cos(θ) (1)
wherein θ is the illumination angle, i. e. the angle between the moving direction 21 and the line of sight 25.
According to the invention, the illumination angle θ and the speed v are determined on the basis of a MicroDoppler spectrogram analysis by means of the electronic processing device, as is explained in greater detail below.
As shown in
The generation of the MicroDoppler spectrogram 30 is carried out by means of a ShortTimeFourierTransform (STFT) or a WignerVilleDistribution technique (WVD technique). An exemplary MicroDoppler spectrogram 30 is shown in
To determine the course 40 of the observed bulk speed v^{ob}, the upper envelope 41 and the lower envelope 42, the cumulative amplitude distribution function is determined for each time slice. The course 40 of the observed bulk speed v^{ob }is assigned to a percentile of about 50% of the cumulative amplitude distribution function. The upper envelope 41 is assigned to a percentile of about 95% of the cumulative amplitude distribution function, whereas the lower envelope 42 is assigned to a percentile of about 5% of the cumulative amplitude distribution function.
By applying a Fast Fourier Transform (FFT) onto the upper envelope 41 and the lower envelope 42, the repetition frequency of the gait cycle is estimated. The duration of the gait cycle T_{gaitcycle }is the reciprocal of the repetition frequency of the gait cycle.
According to a kinematic model disclosed in the book of Chen V. C., “The MicroDoppler Effect in Radar”, Artech House, 2011, the duration of the gait cycle T_{gaitcycle }can be expressed as:
wherein H_{t }is the height of the pedestrian'"'"'s thigh. Generally, the height of the pedestrian'"'"'s thigh H_{t }can be expressed as:
H_{t}≈0.53*H_{ped} (3)
wherein H_{ped }is the pedestrian'"'"'s height.
Supposing that the time resolution of the MicroDoppler spectrogram 30 is ρ_{Timebin}, there are N_{gaitcycle }timebins contained in one gaitcycle:
wherein N_{footstep }is the number of timebins contained in a segment corresponding to one footstep. In equation (4), the factor 2 accounts for the above mentioned fact that usually one gaitcycle comprises two footsteps.
A combination of equations (1)(4) delivers:
vN_{footstep}^{2}=KH_{ped} (5)
v^{ob}N_{footstep}^{2}=KH_{ped }cos(θ) (6)
wherein K is a constant for a given spectrogram:
From equation (6), it follows:
If H_{ped }is known, the illumination angle θ can be determined from equation (8). One variant for calculating the illumination angle θ is to use equation (8) and to set H_{ped }as the statistical average pedestrian height H_{ped_avg }for the dedicated application scenario. The angle estimation error induced by the unknown pedestrian height is:
In
Another variant of estimating the illumination angle θ is to use a recursive state estimator such as an Extended Kalman Filter (EKF). The use of such a recursive state estimator enables a more precise estimation of the illumination angle θ than the above mentioned variant using an average pedestrian height.
In an algorithm based on an Extended Kalman Filter, the state space model of θ and the measurement model of v^{ob }can be given as:
wherein the random variables w_{n−1 }and q_{n }represent the process noise and the measurement noise. The subscripts n, n−1 indicate the current state and the previous state, respectively. The dot refers to the derivative of θ.
Using equation (10) and equation (11), the illumination angle theta θ can be estimated.
After determining the illumination angle θ, the speed v of the pedestrian 19 in the moving direction 21 can be determined by rearranging formula (1):
v=v^{ob}/cos(θ) (12)
Since H_{ped }is a component of the state vector of the EKF as given in equation (10), it can also be estimated by the EKF. An example of such an estimation is shown in
v=KH_{ped}/N_{footstep}^{2} (13)
The invention enables a reliable recognition of moving pedestrians by means of a radar sensor system without the necessity to use complex and expensive hardware. 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.