JP7168353B2 - Radar device and target data allocation method - Google Patents

Description

The present invention relates to a radar device and target data allocation method.

2. Description of the Related Art Conventionally, there has been known a radar device that transmits radio waves around a vehicle and detects a target based on reflected waves of the transmitted radio waves that are reflected by the target. Some radar devices generate target data by performing time-series filtering based on instantaneous data representing reflection points of the target (see, for example, Patent Document 1).

In addition, in such time-series filtering, instantaneous data is assigned to prediction data predicted from past target data within an assignment range set based on the running state including the running speed and steering angle of the own vehicle. .

JP 2015-210157 A

However, in the prior art, if the allocation range is set based on the running state of the vehicle, the instantaneous data derived from the same target may deviate from the allocation range, making it impossible to properly maintain the continuity of the target data. For example, when the own vehicle runs on a bank-shaped road surface, a deviation occurs between the travel trajectory estimated from the steering angle of the own vehicle and the actual travel trajectory of the own vehicle. is not possible, and the prediction accuracy of the prediction data may decrease.

SUMMARY OF THE INVENTION It is an object of the present invention to provide a radar apparatus and a method of allocating target data that can appropriately maintain the continuity of target data.

In order to solve the above-described problems and achieve the object, a radar device according to an embodiment includes a generator and a filter processor. The generator generates instantaneous data corresponding to the target based on a reflected wave of the transmitted wave reflected by the target. The filtering unit allocates the instantaneous data generated by the generating unit to prediction data predicted from past target data corresponding to the target. Further, the filter processing unit assigns the instantaneous data to the prediction data based on an assignment range set according to the bank angle of the road surface on which the vehicle is traveling.

According to the present invention, continuity of target data can be appropriately maintained.

FIG. 1A is a schematic diagram showing an example of mounting a radar device. FIG. 1B is a diagram showing an outline of a target data allocation method. FIG. 2 is a block diagram of the radar device. FIG. 3 is a process explanatory diagram from pre-processing of the signal processing unit to peak extraction processing in the signal processing unit. FIG. 4A is a process explanatory diagram of the angle estimation process. FIG. 4B is a process explanatory diagram (part 1) of the pairing process. FIG. 4C is a process explanatory diagram (part 2) of the pairing process. FIG. 5 is a diagram showing a specific example of variation given to particles. FIG. 6 is a flow chart showing a processing procedure executed by the radar device. FIG. 7 is a block diagram of a filter processing unit according to a modification. FIG. 8 is a diagram showing a specific example of thresholds for bank angles. FIG. 9 is a diagram showing a specific example of processing by the weighting unit. FIG. 10 is a diagram illustrating an example of allocation ranges.

Embodiments of a radar device and a target data allocation method disclosed in the present application will be described in detail below with reference to the accompanying drawings. It should be noted that the present invention is not limited by this embodiment. In the following description, a case where the radar system is of the FM-CW (Frequency Modulated Continuous Wave) system will be described as an example, but the radar system may be of the FCM (Fast-Chirp Modulation) system.

First, the outline of the target data allocation method according to the embodiment will be described with reference to FIGS. 1A and 1B. FIG. 1A is a diagram showing an example of mounting a radar device. FIG. 1B is a diagram showing an outline of a target data allocation method. FIG. 1A shows an own vehicle MC equipped with a radar device 1 according to the embodiment, and another vehicle LC located in front of the own vehicle MC.

As shown in FIG. 1A, the radar device 1 is mounted, for example, in the front grille of the own vehicle MC, and detects targets (for example, other vehicles LC, etc.) existing in the traveling direction of the own vehicle MC. Note that the radar device 1 may be mounted at other locations such as a windshield, a rear grille, and left and right side portions (for example, left and right door mirrors).

Further, as shown in FIG. 1A, the radar device 1 generates instantaneous data 100 corresponding to the target based on the reflected waves of the radio waves transmitted around the host vehicle MC reflected by the other vehicle LC. The instantaneous data 100 includes information such as the relative speed toward the own vehicle MC, the distance between the own vehicle MC and the instantaneous data 100, and the direction of the instantaneous data 100. FIG.

The radar device 1 also performs time-series filtering on the generated instantaneous data 100 to generate target data corresponding to the target as a filter value. This makes it possible to follow (track) the target data.

Such time-series filtering is a process of ensuring continuity of target data by assigning current instantaneous data 100 to prediction data predicted from previous target data. More specifically, in time-series filtering, prediction data corresponding to current target data is predicted based on information such as position and speed included in previous target data.

Then, the current instantaneous data 100 within a predetermined range from the predicted data is assigned. Note that the vector connecting the previous target data to the predicted data is hereinafter referred to as "predicted movement vector Vr".

By the way, the predicted movement vector Vr includes the predicted movement amount of the own vehicle MC and the predicted movement amount of the other vehicle LC. Therefore, the size of the allocation range is set based on the assumed error of each movement prediction. In other words, the assumed error mentioned here quantitatively determines the amount of deviation between the predicted amount and the actual value when the movement prediction is incorrect. The predictive quantity may be, for example, the position as described above, or it may be the velocity. That is, for example, even if the host vehicle MC and the other vehicle LC make a sharp turn or accelerate, a range in which tracking is possible is set as the allocation range. In other words, the allocation range is set based on the assumed error, taking into account various design requirements such as the probability that the prediction will actually go wrong and the tolerance for the occurrence of the error.

Further, the radar device 1 can calculate the amount of movement of the own vehicle MC based on the actual steering angle and running speed of the own vehicle MC. Therefore, the radar device 1 does not need to predict elements caused by the movement of the own vehicle MC, that is, the assumed error may be reduced (or eliminated). In other words, the allocation range can be set primarily based on the assumed error of the factors caused by the movement of the other vehicle LC. This makes it possible to narrow the allocation range. It should be noted that a narrow allocation range means that the prediction accuracy is high, and the possibility of erroneous allocation when allocating instantaneous data is low, so it is generally preferable.

However, for example, when the host vehicle MC travels on a bank-shaped road, if the travel amount of the host vehicle MC is obtained based on the steering angle of the host vehicle MC, the travel amount and the actual travel amount of the host vehicle MC may differ. Misalignment occurs.

That is, for example, assuming that the host vehicle MC is traveling straight on a bank-shaped road, the steering angle will indicate a value (a value other than straight travel) according to the bank angle. Therefore, in such a case, the radar device 1 may recognize that the own vehicle MC is turning even though the own vehicle MC is actually traveling straight.

In such a case, an error occurs between the movement amount of the own vehicle MC calculated by the radar device 1 and the actual movement amount of the own vehicle MC. Therefore, if the movement of the own vehicle MC is assumed to be correct and the allocation range is set without considering the estimated error in the movement prediction of the own vehicle MC, the instantaneous data to be allocated may appear outside the allocation range. As a result, instantaneous data cannot be assigned to prediction data, and the continuity of target data cannot be properly maintained.

Therefore, in the target data allocation method according to the embodiment, the continuity of the target data is appropriately maintained by setting the allocation range according to the bank angle. Specifically, in the target data allocation method according to the embodiment, as shown in FIG. 1B, when it is determined that there is no bank, a first allocation range Ra1 centered on the prediction data 60 is set, and it is determined that there is a bank. If so, a second allocation range Ra2 wider than the first allocation range Ra1 is set.

For example, the first allocation range Ra1 is a range corresponding to the estimated error in the movement prediction of the other vehicle LC, and the second allocation range Ra2 is the first allocation range Ra1 in consideration of the assumed error in the movement prediction of the own vehicle MC. It is the range determined by

That is, in the target object data allocation method according to the embodiment, when traveling on a bank-shaped road, the first allocation range Ra1 is switched to the second allocation range Ra2, which also takes into consideration the assumed error in the movement prediction of the own vehicle MC. Therefore, the current instantaneous data 100 can be contained within the second allocation range Ra2.

Therefore, according to the target data allocation method according to the embodiment, it is possible to appropriately allocate the appropriate instantaneous data 100 that is continuous with the previous target data, so that the continuity of the target data can be appropriately maintained. .

Next, with reference to FIG. 2, the configuration of the radar device 1 according to the embodiment will be described in detail. FIG. 2 is a block diagram showing the configuration of the radar device 1 according to the embodiment. Note that FIG. 2 shows functional blocks mainly for components necessary for explaining the features of the present embodiment, and some general components are omitted.

As shown in FIG. 2 , the radar device 1 includes a transmitter 10 , a receiver 20 and a processor 30 . The radar device 1 is also connected to a vehicle control device 2 that controls the behavior of the own vehicle MC.

The vehicle control device 2 performs vehicle control such as PCS (Pre-crash Safety System) and AEB (Advanced Emergency Braking System) based on the target detection result by the radar device 1 .

The transmitter 10 includes a signal generator 11 , an oscillator 12 and a transmission antenna 13 . The signal generation unit 11 generates a modulation signal for transmitting millimeter waves frequency-modulated with a triangular wave under the control of the transmission/reception control unit 31, which will be described later. The oscillator 12 generates a transmission signal based on the modulated signal generated by the signal generator 11 and outputs it to the transmission antenna 13 . Incidentally, as shown in FIG. 2, the transmission signal generated by the oscillator 12 is also distributed to the mixer 22, which will be described later.

The transmission antenna 13 converts a transmission signal from the oscillator 12 into a transmission wave, and outputs the transmission wave to the outside of the own vehicle MC. A transmission wave output from the transmission antenna 13 is a continuous wave frequency-modulated with a triangular wave. A transmission wave transmitted from the transmission antenna 13 to the outside of the own vehicle MC, for example, to the front is reflected by a target such as another vehicle LC and becomes a reflected wave.

The receiving section 20 includes a plurality of receiving antennas 21 forming an array antenna, a plurality of mixers 22 and a plurality of A/D converting sections 23 . Mixer 22 and A/D converter 23 are provided for each receiving antenna 21 .

Each receiving antenna 21 receives a reflected wave from a target as a received wave, converts the received wave into a received signal, and outputs the received signal to the mixer 22 . Although the number of receiving antennas 21 shown in FIG. 2 is four, the number may be three or less or five or more.

A received signal output from the receiving antenna 21 is input to the mixer 22 after being amplified by an amplifier (for example, a low noise amplifier) not shown. The mixer 22 mixes the distributed transmission signal and part of the received signal input from the receiving antenna 21 to remove unnecessary signal components, generates a beat signal, and outputs the beat signal to the A/D converter 23 . .

The beat signal has a beat frequency that is the difference between the frequency of the transmission signal (hereinafter referred to as "transmission frequency") and the frequency of the reception signal (hereinafter referred to as "reception frequency"). The beat signal generated by the mixer 22 is output to the processing section 30 after being converted into a digital signal by the A/D conversion section 23 after the synchronization section (not shown) synchronizes the timing between the receiving antennas.

The processing unit 30 includes a transmission/reception control unit 31 , a signal processing unit 32 and a storage unit 36 . The signal processor 32 includes a generator 33 and a filter processor 35 .

The storage unit 36 stores history data 36a. The history data 36 a is information including the history of the target data 50 and the history of the instantaneous data 100 in a series of signal processing executed by the signal processing unit 32 .

The processing unit 30 is a microcomputer including, for example, a CPU (Central Processing Unit), ROM (Read Only Memory) and RAM (Random Access Memory) corresponding to the storage unit 36, registers, and other input/output ports. It controls the entire device 1 .

The CPU of such a microcomputer functions as a transmission/reception control section 31 and a signal processing section 32 by reading out and executing programs stored in the ROM. The transmission/reception control unit 31 and the signal processing unit 32 can also be configured entirely by hardware such as ASIC (Application Specific Integrated Circuit) or FPGA (Field Programmable Gate Array).

The transmission/reception control section 31 controls the transmission section 10 including the signal generation section 11 and the reception section 20 . The signal processing unit 32 periodically executes a series of signal processing. Next, each component of the signal processing unit 32 will be described.

The generator 33 generates instantaneous data 100 . Specifically, the generator 33 generates the instantaneous data 100 by performing frequency analysis processing, peak extraction processing, and instantaneous data generation processing.

In the frequency analysis process, the beat signal input from each A/D converter 23 is subjected to Fast Fourier Transform (FFT) processing (hereinafter referred to as "FFT processing"). The result of such FFT processing is the frequency spectrum of the beat signal, which is the power value (signal level) for each frequency of the beat signal (for each frequency bin set at frequency intervals corresponding to the frequency resolution).

In the peak extraction process, a peak frequency is extracted as a result of the FFT process by the frequency analysis process. In the peak extraction process, peak frequencies are extracted for each of the "UP section" and the "DN section" of the beat signal, which will be described later.

In the instantaneous data generation process, an angle estimation process is executed to calculate the arrival angle and the power value of the reflected wave corresponding to each of the peak frequencies extracted in the peak extraction process. It should be noted that, at the time of execution of the angle estimation process, the arrival angle is an angle at which it is estimated that the target exists, so it may be referred to as an "estimated angle" below.

Also, in the instantaneous data generation process, a pairing process is executed to determine the correct combination of the peak frequencies of the "UP section" and the "DN section" based on the calculated estimated angle and power value.

Further, in the instantaneous data generation process, the distance of each target from the own vehicle MC and the relative speed toward the own vehicle MC are calculated from the determined combination result. In the instantaneous data processing, the calculated estimated angle, distance, and relative velocity of each target are output to the filter processing unit 35 as instantaneous data 100 for the latest cycle (latest scan), and the history data 36a is stored in the storage unit 36. remember as

3 to 4C show the flow of processing from the pre-processing of the signal processing unit 32 up to this point in the signal processing unit 32 for the sake of easy understanding of the explanation. FIG. 3 is a process explanatory diagram from pre-processing of the signal processing unit 32 to peak extraction processing in the generation unit 33 .

Also, FIG. 4A is a process explanatory diagram of the angle estimation process. 4B and 4C are process explanatory diagrams of the pairing process. Note that FIG. 3 is divided into three regions by two thick downward white arrows. In the following, such regions are described in order as an upper stage, a middle stage, and a lower stage.

As shown in the upper part of FIG. 3, the transmission signal fs(t) is transmitted from the transmission antenna 13 as a transmission wave, is reflected by the target, arrives as a reflected wave, and is received by the reception antenna 21 as the reception signal fr(t ) is received as

At this time, as shown in the upper part of FIG. 3, the received signal fr(t) is delayed by the time difference T with respect to the transmitted signal fs(t) according to the distance between the host vehicle MC and the target. Due to this time difference T and the Doppler effect based on the relative velocities of the host vehicle MC and the target, the beat signal has a frequency fup in an "UP section" where the frequency rises and a frequency fdn in the "DN section" where the frequency drops. is obtained as a repeated signal (see the middle part of FIG. 3).

The lower part of FIG. 3 schematically shows the results of the FFT processing of the beat signal in the frequency analysis processing for each of the "UP section" side and the "DN section" side.

As shown in the lower part of FIG. 3, after the FFT processing, waveforms in the frequency domains on the "UP section" side and the "DN section" side are obtained. In the peak extraction process, the peak frequency of the waveform is extracted.

For example, in the case of the example shown in the lower part of FIG. 3, a peak extraction threshold is used, and on the "UP section" side, peaks Pu1 to Pu3 are determined as peaks, and peak frequencies fu1 to fu3 are extracted.

Also, on the "DN section" side, the peaks Pd1 to Pd3 are determined as peaks by the same peak extraction threshold, and the peak frequencies fd1 to fd3 are extracted.

Here, the frequency component of each peak frequency extracted by the peak extraction process may be mixed with reflected waves from a plurality of targets. Therefore, in the instantaneous data generation process, an angle estimation process for performing azimuth calculation is performed for each peak frequency, and the presence of a target corresponding to each peak frequency is analyzed.

Note that the azimuth calculation in the instantaneous data generation process can be performed using a known direction-of-arrival estimation method such as ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques).

FIG. 4A schematically shows an azimuth calculation result of instantaneous data generation processing. In the instantaneous data generation process, the estimated angle of each target (each reflection point) corresponding to each of the peaks Pu1 to Pu3 is calculated from each of the peaks Pu1 to Pu3 of the azimuth calculation result. Also, the magnitude of each of the peaks Pu1 to Pu3 is the power value. In the instantaneous data generation processing, as shown in FIG. 4B, such angle estimation processing is performed for each of the "UP section" side and the "DN section" side.

Then, in the instantaneous data generation process, a pairing process is performed in which peaks having similar estimated angles and power values are combined in the azimuth calculation result. Further, from the result of the combination, in the instantaneous data generation process, the distance of each target (each reflection point) corresponding to the combination of each peak and the relative speed toward the own vehicle MC are calculated.

The distance can be calculated based on the relationship “distance∝(fup+fdn)”. The relative velocity can be calculated based on the relationship "velocity∝(fup-fdn)". As a result, as shown in FIG. 4C, a pairing processing result showing instantaneous data 100 of the estimated angle, distance and relative speed of each reflection point RP with respect to the own vehicle MC is obtained.

Returning to the description of FIG. 2, the detection unit 34 will be described. The detection unit 34 detects the bank angle of the road surface on which the host vehicle MC travels. For example, the detection unit 34 detects a bank angle based on sensor values such as an acceleration sensor (not shown) and a roll sensor (not shown) provided in the host vehicle MC, and filters a bank angle signal corresponding to the bank angle. Output to the processing unit 35 .

Note that the detection unit 34 may detect the bank angle based on the values of other sensors such as a gyro sensor, or may detect the bank angle based on an image of the road surface captured by the own vehicle MC. may be detected. Although the case where the detection unit 34 is provided inside the radar device 1 is shown here, the detection unit 34 may be provided outside the radar device 1 .

Next, the filter processing unit 35 will be described. As shown in FIG. 2, the filtering unit 35 includes a prediction unit 35a, an allocation unit 35b, a weighting unit 35c, a resampling unit 35d, and a target object data generation unit 35e.

In this embodiment, the filter processing unit 35 applies a particle filter for performing analysis using a distribution of a predetermined number of particles to the instantaneous data 100 generated by the generating unit 33, thereby filtering objects corresponding to the instantaneous data 100. target data 50 is generated.

The particle filter makes multiple hypotheses for the true state of the target and analyzes them. A hypothesis is a hypothetical value for the state of a target, such as position or velocity. For example, in the position space, hypotheses are scattered with a given distribution and look like a single moving particle, so here we refer to hypotheses as particles.

In addition, particle swarm data in which a predetermined number of particles are collectively used as one hypothesis is also used. For example, the particle group data is the average value of the state of particles, etc., and can be said to be one of the most probable hypotheses in the distribution of a predetermined number of particles. That is, the particle swarm data corresponds to the prediction data 60. FIG. Therefore, the particle swarm data is also referred to as prediction data 60 below.

The prediction unit 35a performs particle group data and particle prediction processing. Specifically, the prediction unit 35a assumes that the latest cycle is time t, and predicts the state of the particles and particle group data at time t based on the particles and particle group data at time t−1 of the previous cycle. For example, there is a method of predicting by a motion model and a measurement period ΔT based on the state of particles and particle group data such as velocity and position.

Specifically, in the prediction process, the prediction unit 35a gives information on the predicted movement vector Vr in the measurement period ΔT to the particle and particle group data at time t−1, so that from the particle at time t−1 to the time Generate particle and particle swarm data at t. The predicted movement vector Vr is, for example, a difference in position and a difference in speed. For prediction, there is a method using a motion model according to the so-called Newton's equation of motion using velocity, acceleration, and measurement period ΔT.

Next, the allocation unit 35b will be described. The allocation unit 35b performs processing for allocating the instantaneous data 100 in the latest period to the latest particle group data, which is the prediction result of the prediction unit 35a. Specifically, the allocation unit 35b performs an instantaneous Allocate data 100.

That is, when there is a bank, the allocation unit 35b allocates the instantaneous data 100 within the second allocation range Ra2 based on the assumed error in the movement prediction of the host vehicle MC based on the bank angle to the prediction data 60. FIG. On the other hand, when there is no bank, the allocation unit 35b allocates to the prediction data 60 the instantaneous data 100 within the first allocation range Ra1 in which the assumed error based on the movement prediction of the own vehicle MC is reduced. The determination of the allocation range may be performed independently of or together with the determination of whether the particle swarm data and the instantaneous data are allocated. When performing together, there is a technique such as giving a penalty value to the instantaneous data 100 outside the allocation range.

In this way, the allocation unit 35b allocates the instantaneous data 100 to the particle group data based on the allocation range Ra set according to the bank angle. It becomes possible to allocate particle group data to the instantaneous data 100.

In other words, by allocating the appropriate instantaneous data 100 to the prediction data 60, it is possible to appropriately maintain the continuity of the target data 50. FIG. If there is instantaneous data 100 that does not exist within the allocation range Ra of any target data 50, the allocation unit 35b treats the instantaneous data 100 as a new target.

After performing predetermined processing on the new target, particle group data is generated, and particles are added at the same time. Predetermined processing can also span measurement cycles. For example, for a new target, we simply evaluated the continuity for several cycles and confirmed that it did not occur sporadically due to noise or the like. Particle group data can be generated later and particles can be added. It should be noted that, as described above, the particles can also be given a variation according to the bank angle at this point.

At this time, as described above, the predicted movement vector Vr given to the particles and particle group data includes the predicted movement amount of the host vehicle MC and the predicted movement amount of the other vehicle LC. By obtaining the rudder angle and running speed of the MC, it is possible to calculate the actual amount of movement instead of prediction.

Note that the particles form a distribution with a predetermined variation. The predetermined variation is given at the time of particle application based on, for example, a probability distribution such as a normal distribution, or a characteristic probability distribution that can be taken in each state such as velocity. Variation, also known as noise, is usually not specially adjusted after it is initially set. In this embodiment, the variation is adjusted according to the bank angle. The allocation unit 35b adjusts the variation based on the assumed error of the predicted movement amount of the own vehicle MC, that is, the noise component.

The allocator 35b sets the variation of the particles according to the bank angle when sampling the particles. FIG. 5 is a diagram showing a specific example of variation given to particles.

As shown in FIG. 5, when there is no bank, that is, when the bank angle is equal to or less than a predetermined value or zero, the assigning unit 35b gives the particle DP a noise component Ng based on the assumed error of the other vehicle LC. .

Further, when there is a bank, that is, when the bank angle is equal to or greater than a predetermined value, the allocation unit 35b gives the particle DP the noise component Nr. Here, the noise component Nr includes a noise component Nb corresponding to the bank angle in addition to the noise component Ng.

Moreover, the noise component given to each particle DP includes not only the lateral position component, which is the vehicle width direction of the own vehicle MC, but also the longitudinal position component, which is the traveling direction of the own vehicle MC. That is, when there is a bank, the particles DP are distributed over a wider range not only in the horizontal position but also in the vertical position than when there is no bank.

Therefore, as shown in the lower diagram of FIG. 5, when there is a bank, the particles DP are distributed over a wider range than when there is no bank. That is, the distribution d2 of the particles DP with the bank is wider than the distribution of the particles DP without the bank. Therefore, in weighting, which will be described later, there is a high probability that particles close to instantaneous data exist. When all the particles are farther than the instantaneous data 100, the weights of all the particles become close to each other, making it difficult for the particles to rearrange due to resampling, which will be described later, which is a factor in lowering the followability.

As the bank angle increases, the intensity of the noise component Nb may be increased, or the intensity of the noise component may be fixed at all times. Further, when there is a bank, the intensity of only the horizontal position component or only the vertical position component may be increased with respect to the noise component Nb.

Returning to the description of FIG. 2, the weighting unit 35c weights each particle DP based on the current instantaneous data 100 in the allocation relationship by the allocation unit 35b.

For example, the weighting unit 35c increases the weight of particles DP that are similar to the instantaneous data 100 of this time and decreases the weight of particles that are not similar from the instantaneous data 100 of this time, among the particles DP of this time. It should be noted that the degree of similarity referred to here indicates, for example, an evaluation value of a cost function described based on a positional difference, a speed difference, or the like.

The resampling unit 35d rearranges (resamples) the particles DP based on the current weight of each particle DP. Specifically, the resampling unit 35d moves the particle DP with the smaller weight closer to the instantaneous data 100 (with the larger weight).

More specifically, the resampling unit 35d rearranges particles DP whose weight is less than a predetermined threshold to particles DP whose weight is equal to or greater than a predetermined threshold. As a result, the current particle swarm data generated by prediction can be corrected with instantaneous data that may be closer to the true value. As a result, the target data 50 generated by the target data generation unit 35e, which will be described later, can be made more accurate.

The target data generator 35e generates the target data 50 based on the current particles rearranged by the resampling unit 35d. For example, the target data generation unit 35e generates the probability density function from the particle distribution and generates the target data 50 based on the center of gravity thereof, or simply based on the average of the particles. may be generated. Note that the particle group data is updated with the target data 50 .

Also, the target data generation unit 35e treats the instantaneous data 100 to which no particle has been assigned as a new target, and outputs it as the target data 50 as it is. That is, the target data generator 35e outputs instantaneous data 100=target data 50 in the case of a new target.

Next, a processing procedure executed by the radar device 1 according to the embodiment will be described with reference to FIG. FIG. 6 is a flowchart showing a processing procedure executed by the radar device 1. As shown in FIG. Note that the processing procedure described below is repeatedly executed by the signal processing unit 32 .

As shown in FIG. 6, the generation unit 33 first generates instantaneous data 100 corresponding to the target based on the reflected wave of the transmitted radio wave reflected by the target (step S101). Subsequently, the detector 34 detects the bank angle (step S102). The radar device 1 may perform the processing of step S101 and the processing of step S102 in parallel, or may perform the processing of step S101 after the processing of step S102.

Subsequently, the prediction unit 35a performs a prediction process of predicting the current particle based on the previous particle and a prediction process of predicting the prediction data 60 continuing from the previous target data 50 (step S103).

Subsequently, the allocation unit 35b allocates the current instantaneous data 100 to the current particle DP in the allocation range Ra corresponding to the bank angle (step S104). Subsequently, the allocation unit 35b gives the particles DP variations based on the bank angle (step S105).

Subsequently, the assignment unit 35b determines whether or not there is a new target based on the presence or absence of instantaneous data 100 to which particles have not been assigned this time (step S106). If the instantaneous data 100 is a new target (step S106, Yes), the allocation unit 35b sets predetermined particles (for example, particles in an initial state) to the instantaneous data 100 corresponding to the new target. (step S110).

If the instantaneous data 100 is not a new target (step S106, No), the weighting unit 35c weights each particle based on the instantaneous data 100 (step S107).

Subsequently, the resampling unit 35d resamples the current particle based on the weighting by the weighting unit 35c (step S108), and the target data generation unit 35e calculates the probability density function of the resampled current particle as Then, the target data 50 is generated based on the probability density function (step S109), and the process ends.

As described above, the radar device 1 according to the embodiment includes the generator 33 and the filter processor 35 . The generation unit 33 generates instantaneous data 100 corresponding to the target based on the reflected wave of the transmitted wave reflected by the target (for example, another vehicle LC). The filtering unit 35 assigns the instantaneous data 100 generated by the generating unit 33 to prediction data 60 predicted from past target data 50 corresponding to the target.

Further, the filter processing unit 35 allocates the instantaneous data 100 to the prediction data 60 based on the allocation range Ra set according to the bank angle of the road surface on which the host vehicle MC travels. Therefore, according to the radar device 1 according to the embodiment, since an appropriate allocation range Ra can be set according to the bank angle, the continuity of the target data 50 can be appropriately maintained.

By the way, in the above-described embodiment, the case where the time-series filtering is the particle filter has been described, but the present invention is not limited to this. The filter processing unit 35-2 according to the modification will be described below with reference to FIGS. 7 to 9. FIG.

FIG. 7 is a block diagram of the filter processing section 35-2 according to the modification. As shown in FIG. 7, the filter processing unit 35-2 according to the modification includes a prediction unit 35a-2, an allocation unit 35b-2, a weighting unit 35c-2, and a target object data generation unit 35e-2. Prepare.

The prediction unit 35a-2 predicts prediction data 60 corresponding to the current target data 50 from the previous target data 50. FIG. Further, when the bank angle exceeds the threshold value, the prediction unit 35a-2 determines that there is a bank, and switches the allocation range Ra.

FIG. 8 is a diagram showing a specific example of thresholds for bank angles. As shown in FIG. 8, for example, the threshold for the bank angle has a first threshold Th1 and a second threshold Th2, and a hysteresis region Rh is provided between the first threshold Th1 and the second threshold Th2.

For example, the prediction unit 35a-2 determines that there is a bank when the bank angle exceeds the first threshold Th1, and switches the bank flag to ON. Further, the prediction unit 35a-2 switches the bank flag off when the bank angle falls below a second threshold Th2 which is lower than the first threshold Th1.

That is, by providing the hysteresis region Rh between the first threshold value Th1 and the second threshold value Th2, it becomes difficult to switch the bank flag from off to on, and the bank flag is easily switched from on to off.

This is to suppress erroneous determination of the presence or absence of the bank based on the noise component, which may be included in the bank angle due to vibration or the like when the own vehicle MC travels over the step. .

Therefore, by providing the hysteresis region Rh, it is possible to determine that there is a bank only when the host vehicle MC travels on a bank-shaped road surface. Note that the first threshold Th1 and the second threshold Th2 shown in FIG. 8 can also be applied to the radar device 1 according to the above-described embodiment.

Returning to the description of FIG. 7, the allocation unit 35b-2 will be described. The allocation unit 35b-2 allocates the instantaneous data 100 to the prediction data 60 based on the allocation range Ra set according to the bank angle.

The allocation unit 35 b - 2 allocates the instantaneous data 100 closest to the predicted data 60 in terms of relative velocity and position to the predicted data 60 . Here, as shown in FIG. 1, the allocation range Ra is the first allocation range Ra1 when there is a bank, and the second allocation range Ra2 when there is no bank.

In addition, the first allocation range Ra1 is a range including the estimated error of the movement prediction of the other vehicle LC, while the second allocation range Ra2 is a range including the estimated error of the movement prediction of the own vehicle MC in the first allocation range Ra1. is the range plus

That is, the allocating unit 35b-2 can allocate a wider range of instantaneous data 100 to the prediction data 60 when it is determined that there is a bank. As a result, even if the prediction data 60 and the instantaneous data 100 deviate due to the influence of the bank, the original instantaneous data 100 can be assigned to the prediction data 60 . That is, it becomes possible to appropriately maintain the continuity of the target data 50 .

The weighting unit 35c-2 levels the prediction data 60 and the instantaneous data 100 assigned to the prediction data 60 by the assignment unit 35b-2 by using an exponential moving average, and adjusts the weighting in the exponential moving average according to the bank angle. do.

FIG. 9 is a diagram showing a specific example of weighting in the exponential moving average. As shown in FIG. 9, for example, the weighting unit 35c-2 increases the weight of the instantaneous data 100 and decreases the weight of the prediction data 60 when it is determined that there is a bank.

As a result, the target data 50 is generated so as to be closer to the instantaneous data 100 than the predicted data 60 is. In other words, when it is assumed that the prediction accuracy of the prediction data 60 is reduced due to the influence of the bank, by reflecting the instantaneous data 100 in the target data 50 at a higher rate, the error in the prediction data 60 caused by the influence of the bank can be reduced. can be corrected.

In this case, the weights of the instantaneous data 100 and the prediction data 60 may be set such that the greater the bank angle, the greater the weight of the instantaneous data 100, or the bank is determined to be present. The weight in each case may always be a constant value.

Further, the weighting unit 35c-2 may set the weight corresponding to the bank angle by referring to a table prepared in advance, or may set the weight input from an external control device. good.

Returning to the description of FIG. 7, the target object data generator 35e-2 will be described. The target data generation unit 35e-2 generates target data 50 based on the instantaneous data 100 and the prediction data 60 weighted by the weighting unit 35c-2.

That is, the target data generator 35e-2 generates the target data 50 by increasing the weight of the instantaneous data 100 and decreasing the weight of the prediction data 60 when it is determined that there is a bank.

As a result, it is possible to generate the target data 50 in which bank errors are suppressed, and to improve the likelihood of the prediction data 60 predicted from the target data 50 .

Therefore, in the next process, the predicted data 60 and the instantaneous data 100 corresponding to the predicted data 60 will take relatively close values, and variations between the predicted data 60 and the instantaneous data 100 can be suppressed.

By the way, although the second allocation range Ra2 described above is a wider range than the first allocation range Ra1, it is not limited to this. The second allocation range Ra2 may be approximately the same size as the first allocation range Ra1.

FIG. 10 is a diagram showing an example of allocation range Ra. As shown in FIG. 10, the second allocation range Ra2 may be set by rotating the first allocation range Ra1 according to the bank angle, for example.

For example, the radar device 1 stores a table in which the deviation amount θ between the prediction data 60 corresponding to the bank angle and the reflection point RP (see FIG. 4C) is calculated in advance, and uses the deviation amount θ according to the bank angle. A second allocation range Ra2 is set by correcting the first allocation range Ra1 with the

That is, by correcting the predicted movement vector Vr with the deviation amount θ, the corrected predicted movement vector Vrb is derived, and based on the predicted movement vector Vrb, the predicted data 60a based on the predicted movement vector Vr is based on the predicted movement vector Vrc. Correction is made to the prediction data 60b. This means that movement prediction is also based on the bank angle.

Then, for the prediction data 60b, for example, a second allocation range Ra2 having approximately the same size as the first allocation range Ra1 is set. This makes it possible to suppress deterioration in followability due to bank effects without setting the second allocation range Ra2 wider than the first allocation range Ra1.

Note that, for example, when a Kalman filter or an extended Kalman filter is used as the time series filter, the covariance matrix may be extended according to the bank angle. That is, as the bank angle increases, the allocation range Ra may be extended by extending the covariance matrix.

Further effects and modifications can be easily derived by those skilled in the art. Therefore, the broader aspects of the invention are not limited to the specific details and representative embodiments so shown and described. Accordingly, various changes may be made without departing from the spirit or scope of the general inventive concept defined by the appended claims and equivalents thereof.

1 radar device 33 generation unit 34 detection unit 35 filter processing unit 35a prediction unit 35b allocation unit 35c weighting unit 35d resampling unit 35e target data generation unit 50 target data 60 prediction data 100 instantaneous data Ra allocation range Ra1 first allocation range Ra2 second allocation range

Claims (7)

a generating unit that generates instantaneous data corresponding to the target based on the reflected wave of the transmitted wave reflected by the target;
a filter processing unit that assigns the instantaneous data generated by the generation unit to prediction data predicted from past target data corresponding to the target ;
with
The filter processing unit is
configured to assign the instantaneous data to the prediction data based on an assignment range set according to the bank angle of the road surface on which the vehicle travels,
radar equipment. The filter processing unit is
A first allocation range determined based on an assumed error in movement prediction of the target and a second allocation range determined based on an assumption error in movement prediction of both the target and the own vehicle according to the bank angle. configured to switch between
The radar device according to claim 1. The filter processing unit is
When the bank angle exceeds a first threshold, the first allocation range is switched to the second allocation range, and when the bank angle falls below a second threshold smaller than the first threshold, the second allocation range is switched. configured to switch to the first allocation range;
The radar device according to claim 2. The filter processing unit is
A shift amount between the prediction data corresponding to the bank angle and the reflection point of the transmission wave is stored in advance, and the first allocation range is corrected using the shift amount according to the bank angle to correct the first allocation range. 2, configured to set allocation ranges;
The radar device according to claim 2 or 3. The filter processing unit is
The target data is generated by smoothing the prediction data and the instantaneous data assigned to the prediction data by an exponential moving average, and weighting in the exponential moving average is set according to the bank angle. configured to
The radar device according to any one of claims 1 to 4 . The filter processing unit is
The target data is generated by applying a particle filter based on variations of a plurality of particles each having a predetermined state, and the variations are set according to the bank angle ,
The radar device according to claim 1 . a generation step of generating instantaneous data corresponding to the target based on the reflected wave of the transmitted wave reflected by the target;
a filtering step of assigning the instantaneous data generated by the generating step to prediction data predicted from past target data corresponding to the target ;
including
In the filtering step ,
assigning the instantaneous data to the prediction data based on an assignment range set according to the bank angle of the road surface on which the vehicle travels;
Target data allocation method.

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