Radar observation system, radar observation device, radar observation method, and radar observation program
Patent Information
- Application Number
- PCT/JP2026/001204
- Authority / Receiving Office
- WO · WO
- Patent Type
- Applications
- Current Assignee / Owner
- Priority Date
- 2025-03-25
- Filing Date
- 2026-01-16
- Publication Date
- 2026-10-01
Smart Images
Figure JP2026001204_01102026_PF_FP_ABST
Abstract
Description
Radar observation system, radar observation apparatus, radar observation method, radar observation program Cross-reference to related applications
[0001] This application is based on Japanese Patent Application No. 2025-50298 filed in Japan on March 25, 2025, and the entire content of the base application is incorporated herein by reference.
[0002] The present disclosure relates to a radar observation technique for observing a target by a radar sensor of a host vehicle.
[0003] The radar observation technique disclosed in Patent Document 1 analyzes the azimuth of a reception signal reflected from a target with respect to a transmission signal from a radar sensor, thereby discriminating a false azimuth where no target exists.
[0004] Japanese Patent No. 7512979
[0005] In the radar observation technique disclosed in Patent Document 1, a virtual signal for an actual azimuth where a target exists is calculated from a mode matrix of an estimated azimuth based on a reception signal and estimated power, whereby a false azimuth is discriminated in response to an increase in error between the reception signal and the virtual signal. However, only through such azimuth analysis, when the radar sensor becomes dirty, even an actual azimuth may be discriminated as a false azimuth, which has caused a concern that the observation accuracy may be degraded.
[0006] An object of the present disclosure is to provide a radar observation system that ensures observation accuracy. Another object of the present disclosure is to provide a radar observation apparatus that ensures observation accuracy. A further object of the present disclosure is to provide a radar observation method that ensures observation accuracy. A still further object of the present disclosure is to provide a radar observation program that ensures observation accuracy.
[0007] Hereinafter, technical means of the present disclosure for solving the problem will be described.
[0008] A first aspect of this disclosure is a radar observation system having a processor for performing observation processing to generate observation data obtained by a radar sensor of a host vehicle observing a target, wherein the processor is configured to perform the following: distinguish multiple radar observation points in a driving scene of the host vehicle from actual target observation points and ghost observation points representing ghosts other than the target, based on discrimination feature information obtained by analyzing a received signal reflected from the target in response to a transmitted signal from the radar sensor; monitor estimated actual points as radar observation points estimated to be actual target objects in a driving scene, based on actual feature information different from the discrimination feature information obtained by analyzing the received signal; and output observation data determining the contamination of the radar sensor in response to the number of monitored estimated actual points, where the proportion of ghosts among the estimated actual points that are distinguished as ghost observation points rises to the contamination detection range.
[0009] A second aspect of this disclosure is a radar observation device configured to be mounted on a host vehicle and having a processor for performing observation processing to generate observation data of a target observed by a radar sensor of the host vehicle, wherein the processor is configured to perform the following: distinguish multiple radar observation points in the driving scene of the host vehicle from actual target observation points and ghost observation points representing ghosts other than the target, based on discrimination feature information obtained by analyzing a received signal reflected from the target in response to a transmitted signal from the radar sensor; monitor estimated actual target points as radar observation points estimated to be actual target points in the driving scene, based on actual feature information different from the discrimination feature information obtained by analyzing the received signal; and output observation data determining the contamination of the radar sensor in response to the number of monitored estimated actual target points and the proportion of ghosts among the estimated actual target points rising to the contamination detection range.
[0010] A third aspect of this disclosure is a radar observation method executed by a processor to perform observation processing for generating observation data of a target observed by a radar sensor of a host vehicle, comprising: discriminating multiple radar observation points in a driving scene of a host vehicle into physical observation points representing the actual target and ghost observation points representing ghosts other than the target, based on discrimination feature information obtained by analyzing a received signal reflected from the target in response to a transmitted signal from the radar sensor; monitoring physical estimation points as radar observation points estimated to be the actual target in a driving scene, based on physical feature information different from the discrimination feature information obtained by analyzing the received signal; and outputting observation data determining the contamination of the radar sensor in response to the number of monitored physical estimation points, where the ghost ratio of the number of discrimination points identified as ghost observation points rises to the contamination detection range.
[0011] A fourth aspect of this disclosure is a radar observation program stored in a storage medium for performing observation processing, which generates observation data obtained by a radar sensor of a host vehicle observing a target, and includes instructions for causing a processor to perform said observation processing, the program including instructions for performing the following: distinguishing multiple radar observation points in a driving scene of a host vehicle from actual target observation points and ghost observation points representing ghosts other than the target, based on discrimination feature information obtained by analyzing a received signal reflected from the target in response to a transmitted signal from the radar sensor; monitoring estimated actual target points as radar observation points estimated to be actual target points in a driving scene, based on actual feature information different from the discrimination feature information obtained by analyzing the received signal; and outputting observation data determining the contamination of the radar sensor in response to the number of monitored estimated actual target points, where the ghost ratio of the number of discriminated points identified as ghost observation points among the estimated actual target points rises to the contamination detection range.
[0012] According to these first to fourth embodiments, based on discrimination feature information obtained by analyzing the received signal, multiple radar observation points in the host vehicle's driving scene are distinguished into physical observation points representing the target entity and ghost observation points representing ghosts other than the target. On the other hand, based on physical feature information different from the discrimination feature information obtained by analyzing the received signal, physical estimation points are monitored as radar observation points that are estimated to represent the target entity in the driving scene.
[0013] In these first to fourth embodiments, when the laser sensor becomes dirty, the proportion of ghosts—the number of estimated object points identified as ghost observation points—relative to the number of monitored estimated object points increases. Therefore, by using observation data that indicates radar sensor dirt, which is output in response to the rise in this ghost proportion to the dirt detection range, it becomes possible to distinguish the target object not only from ghosts but also from the dirt, thereby ensuring observation accuracy.
[0014] This is a block diagram showing the overall configuration of the first embodiment. This is a schematic diagram showing the driving environment of the host vehicle to which the first embodiment is applied. This is a block diagram showing the functional configuration of the radar observation system according to the first embodiment. This is a flowchart showing the observation flow according to the first embodiment. This is a schematic diagram for explaining the observation flow according to the first embodiment. This is a schematic diagram for explaining the observation flow according to the first embodiment. This is a flowchart showing the observation flow according to the first embodiment. This is a flowchart showing the observation flow according to the second embodiment. This is a schematic diagram for explaining the observation flow according to the second embodiment. This is a flowchart showing the observation flow according to the third embodiment. This is a flowchart showing the observation flow according to the fourth embodiment.
[0015] Hereinafter, several embodiments of this disclosure will be described with reference to the drawings. In each embodiment, the same reference numerals will be used for corresponding components, and redundant explanations may be omitted. Furthermore, if only a part of the configuration is described in each embodiment, the configuration of other embodiments described earlier may be applied to the other parts of that configuration. Moreover, not only the combinations of configurations explicitly stated in the description of each embodiment, but also the configurations of multiple embodiments can be partially combined even if not explicitly stated, as long as there are no particular problems with the combination.
[0016] (First Embodiment) As shown in Figures 1 and 2, the radar observation system 1 of the first embodiment performs observation processing to generate observation data Do, which is obtained by observing the target To with a radar sensor 52 mounted on the host vehicle 2. The host vehicle 2 may be any of the following vehicles that can travel on roads: for example, an automobile, a truck, a bus, or an autonomous driving robot. From the perspective of the host vehicle 2, the host vehicle 2 can be said to be an ego-vehicle.
[0017] The host vehicle 2 is given an automated driving mode, which is categorized into levels according to the degree of operator intervention in dynamic driving tasks. The automated driving mode may be implemented by autonomous driving control, such as conditional driving automation, highly automated driving, or fully automated driving, in which the system performs all dynamic driving tasks when in operation. The automated driving mode may also be implemented by advanced driver assistance control, such as driver assistance or partial driving automation, in which the operator performs some or all of the dynamic driving tasks. The automated driving mode may be implemented by either one of these autonomous driving controls or advanced driver assistance controls, or by a combination of them, or by switching between them. The operator of such a host vehicle 2 is a driver who is on board the host vehicle 2 and capable of performing manual driving operations. However, the operator of the host vehicle 2 may also be a remote operator who can perform manual driving operations or driving commands remotely from an external center outside the host vehicle 2.
[0018] The host vehicle 2 is equipped with the sensor system 5, communication system 6, and map database 7 shown in Figure 1, along with at least a part of the radar observation system 1. Specifically, the sensor system 5 acquires sensing information usable by the radar observation system 1 regarding the external and internal environments of the host vehicle 2. To this end, the sensor system 5 is composed of an internal sensor 50 and an external sensor 51.
[0019] The internal environment sensor 50 generates internal environment information as sensing information from the internal environment, which is the internal environment of the host vehicle 2. The internal environment sensor 50 may be a physical quantity detection type that detects specific kinetic physical quantities in the internal environment of the host vehicle 2. The physical quantity detection type internal environment sensor 50 is at least one of the following, for example, a driving speed sensor, an acceleration sensor, and an inertia sensor.
[0020] The external sensor 51 generates external information as sensing information from the external environment that constitutes the driving environment in which the host vehicle 2 travels. The external sensor 51 may be a target observation type that observes a target To that exists in the external environment of the host vehicle 2. In particular, the host vehicle 2 is equipped with a radar sensor 52 as a target observation type external sensor 51. The radar sensor 52 is, for example, a millimeter-wave radar that observes a specific direction (forward in this embodiment) in the external environment of the host vehicle 2 by emitting radio waves. The radar sensor 52 has a transmitting / receiving antenna unit 520, a transmitting / receiving processing unit 521, and a radome 522.
[0021] The transmitting and receiving antenna unit 520 is mainly composed of an antenna array, such as a microstrip antenna. The transmitting and receiving processing unit 521 is composed of an IC chip, such as a DSP (Digital Signal Processor), combined with an RF (Radio Frequency) circuit. The transmitting antenna in the transmitting and receiving antenna unit 520 converts the transmission signal modulated by the transmitting and receiving processing unit 521 into radio waves and transmits them to the outside world of the host vehicle 2. The receiving antenna in the transmitting and receiving antenna unit 520 receives the reflected wave from the transmission signal from the target To in the outside world and converts it into a received signal. The transmitting and receiving processing unit 521 mixes the converted received signal with the transmission signal and then performs an FFT (Fast Fourier Transform) analysis to extract position information, direction information, and velocity information as observation information.
[0022] The radome 522 is formed in the shape of a case that covers the transmitting / receiving antenna unit 520 and the transmitting / receiving processing unit 521, and is made of, for example, resin. The radome 522 has a transparent outer surface 522a that is exposed to the outside world, allowing the transmitted signal sent as radio waves from the transmitting / receiving antenna unit 520 and the received signal received from the outside world as reflected waves by the transmitting / receiving antenna unit 520 to pass through.
[0023] Communication system 6 acquires usable communication information from radar observation system 1 via wireless communication. Communication system 6 may be a positioning type that receives positioning signals from GNSS (Global Navigation Satellite System) satellites located outside the host vehicle 2. A positioning type communication system 6 is, for example, a GNSS receiver. Communication system 6 may also be a V2X type that transmits and receives communication signals with a V2X system located outside the host vehicle 2. A V2X type communication system 6 is, for example, at least one of DSRC (Dedicated Short Range Communications) communication devices and cellular V2X (C-V2X) communication devices. Communication system 6 may also be a terminal communication type that transmits and receives communication signals with a terminal located inside the host vehicle 2. A terminal communication type communication system 6 is, for example, at least one of Bluetooth® devices, Wi-Fi® devices and infrared communication devices.
[0024] The map database 7 stores map information available to the radar observation system 1. The map database 7 is composed of at least one type of non-transitory tangible storage medium, such as a semiconductor memory, magnetic medium, and optical medium. The map database 7 may also be a database for a locator that estimates self-state quantities, including the self-position of the host vehicle 2. The map database 7 may also be a database for a navigation unit that navigates the travel route of the host vehicle 2. The map database 7 may be composed of a combination of multiple types of these databases.
[0025] The map database 7 acquires and stores the latest map information, for example, through communication with an external center via a V2X type communication system 6. Here, the map information is digitized in two or three dimensions as information representing the driving environment in which the host vehicle 2 is traveling. In particular, for three-dimensional map data, high-precision digital map data is preferable. The map information may include road information representing at least one type, such as the location, shape, and road surface condition of the road itself. The map information may also include marking information representing at least one type, such as the location and shape of signs and lane markings attached to the road. The map information may also include structural information representing at least one type, such as the location and shape of buildings and traffic lights facing the road.
[0026] As shown in Figure 1, the radar observation system 1 is configured to include at least one dedicated computer. The radar observation system 1 is connected to the sensor system 5, the communication system 6, and the map database 7 via at least one of the following: a LAN (Local Area Network) line, an internal bus, a wire harness, and a wireless communication line. If the radar observation system 1 consists of multiple dedicated computers, the connections between those dedicated computers are similar.
[0027] The dedicated computer constituting the radar observation system 1 may be an electronic control unit (ECU) that controls the operation of the host vehicle 2. The dedicated computer constituting the radar observation system 1 may be a locator ECU that estimates the self-state quantities of the host vehicle 2. The dedicated computer constituting the radar observation system 1 may be a navigation ECU that navigates the travel route of the host vehicle 2 based on map information from the map database 7. The dedicated computer constituting the radar observation system 1 may be an actuator ECU that controls the travel actuators of the host vehicle 2. The dedicated computer constituting the radar observation system 1 may be a human-machine interface (HMI) control unit (HCU) that controls information presentation within the host vehicle 2. The dedicated computer constituting the radar observation system 1 may be a communication ECU that controls the communication system 6 in the host vehicle 2. The dedicated computer constituting the radar observation system 1 may be shared with the microcomputer that constructs the transmit / receive processing unit 521. The dedicated computer constituting the radar observation system 1 may be a computer other than the host vehicle 2 that constructs, for example, an external center or mobile terminal that can communicate via a V2X type communication system 6.
[0028] The dedicated computer constituting the radar observation system 1 has at least one memory 10 and one processor 12. The memory 10 is at least one type of non-transitory tangible storage medium, such as a semiconductor memory, magnetic medium, and optical medium, which non-temporarily stores programs and data that can be read by the computer. Here, storage may be an accumulation where data is retained even when the host vehicle 2 is turned off, or it may be a temporary storage where data is erased when the host vehicle 2 is turned off. The processor 12 includes at least one type as a core, such as a CPU (Central Processing Unit), GPU (Graphics Processing Unit), RISC (Reduced Instruction Set Computer)-CPU, DFP (Data Flow Processor), and GSP (Graph Streaming Processor).
[0029] In the radar observation system 1, the processor 12 executes multiple instructions included in the radar observation program stored in the memory 10 to generate observation data Do of target To observed by the radar sensor 52 of the host vehicle 2. This allows the radar observation system 1 to construct multiple functional blocks for generating observation data Do of target To observed by the radar sensor 52 of the host vehicle 2. As shown in Figure 3, the multiple functional blocks constructed in the radar observation system 1 include a data acquisition block 100, an observation point discrimination block 110, an estimated point monitoring block 120, and a data output block 130.
[0030] The radar observation method for generating observation data Do, obtained by the radar observation system 1 from the observation of target To by the radar sensor 52 of the host vehicle 2, through the combined action of these blocks 100, 110, 120, and 130, is executed according to the observation flow shown in Figure 4. This observation flow may be executed repeatedly for each observation frame while the host vehicle 2 is starting up. This observation flow may include at least observation frames of the driving scene in which the host vehicle 2 is driving inside a tunnel during startup. This observation flow may include at least driving scenes in which the host vehicle 2 is subjected to radar interference from other vehicles driving around it during startup. In this observation flow, each "S" represents a step executed by multiple instructions included in the radar observation program.
[0031] In S10 of Figure 4, the data acquisition block 100 stores in memory 10 the position information, direction information, and velocity information as observation information for the radar observation point Po corresponding to each received signal extracted by the radar sensor 52 in the current observation frame.
[0032] In S20, following S10, the observation point discrimination block 110 distinguishes between two types of radar observation points Po observed in the current driving scene of the host vehicle 2: physical observation points Pot representing the actual target To, and ghost observation points Pog representing ghosts other than the target To. At this time, the distinction between physical observation points Pot and ghost observation points Pog is based on discrimination feature information Itg obtained by analyzing the received signal, which is a received signal reflected from the target To in response to the transmitted signal from the radar sensor 52 and includes the ghost signal when a ghost appears.
[0033] Specifically in S20, azimuth analysis is performed by analyzing azimuth information for each radar observation point Po and corresponding received signal at the radar sensor 52. This analysis yields discrepancy information between the physical observation point Pot and the ghost observation point Pog, which is obtained as discriminant feature information Itg. At this time, a virtual signal is calculated from the mode matrix of the estimated azimuth based on the received signal and the estimated power, assuming the actual azimuth where the target To exists. The error value between these received signals and the virtual signal provides the discriminant feature information Itg.
[0034] Therefore, in S20, radar observation point Po corresponding to a received signal whose error value as deviation information constituting the discrimination feature information Itg is large enough to exceed the discrimination threshold is classified as a ghost observation point Pog. Conversely, radar observation point Po of a received signal whose error value as deviation information constituting the discrimination feature information Itg is small enough to be below the discrimination threshold is classified as a physical observation point Pot in S20. Furthermore, in S20, radar observation point Po of a received signal whose error value as deviation information constituting the discrimination feature information Itg substantially matches the discrimination threshold may be classified as either a ghost observation point Pog or a physical observation point Pot, according to the pre-set classification.
[0035] In S30 following S20 in Figure 4, the estimation point monitoring block 120 monitors the entity estimation point Poe as the radar observation point Po which is estimated to be the actual entity of target To in the current driving scene of the host vehicle 2. At this time, the monitoring by estimation of the entity estimation point Poe is based on entity feature information Ie, which is different from the discrimination feature information Itg in S20, obtained by analyzing the received signal that is reflected from target To and includes the ghost signal when a ghost appears.
[0036] Specifically, in S30, as an analysis of each radar observation point Po corresponding to the received signal from the radar sensor 52, each radar observation point Po is grouped into at least one cluster Cp as shown in Figure 5 by a clustering analysis different from the azimuth analysis in S20. Furthermore, in S30, clustering information representing the grouped cluster Cp of each radar observation point Po is acquired as entity feature information Ie.
[0037] Here, within the same cluster Cp, a ghost observation point Pog caused by a triangular path due to a stationary structure Tos such as a wall, as shown in Figure 6, is clustered further back than the target To's physical observation point Pot, i.e., further from the host vehicle 2, and appears at a speed close to that of the physical observation point Pot. In other words, within the same specific cluster Cp, as shown in Figure 6, the physical observation point Pot is ideally clustered closer to the host vehicle 2 than the ghost observation point Pog, i.e., at the nearest position to the host vehicle 2, and appears at a speed close to that of the ghost observation point Pog caused by the triangular path. However, if the transparent outer surface 522a of the radome 522 of the radar sensor 52 is dirty, the radar observation point Po in Figure 6, which should be identified as a physical observation point Pot according to S20 above, is more likely to be misidentified as a ghost observation point Pog, as shown in Figure 7.
[0038] Therefore, in S30, within the same specific cluster Cp grouped by clustering analysis, the radar observation point Po closest to the host vehicle 2 is monitored as an estimated physical point Poe based on physical feature information Ie, as shown in Figures 6 and 7. As a result, if the radar observation point Po identified as a physical observation point Pot in S20, as shown in Figure 6, is monitored as an estimated physical point Poe, it becomes possible to determine that there is no contamination of the radar sensor 52 in the area where the received signal corresponding to that radar observation point Po is transmitted. On the other hand, if the radar observation point Po identified as a ghost observation point Pog in S20, as shown in Figure 7, is monitored as an estimated physical point Poe, it becomes possible to determine that there is contamination of the radar sensor 52 in the area where the received signal corresponding to that radar observation point Po is transmitted.
[0039] In S40, following S30 in Figure 4, the data output block 130 determines whether the ghost ratio Rg has risen to the contamination detection range. The ghost ratio Rg used as the criterion for this determination is represented by equation 1, which is based on the contamination detection principle described above. That is, the ghost ratio Rg is defined as the ratio of the number of real-world estimated points Poe that were identified as ghost observation points Pog in S20 to the number of real-world estimated points Poe monitored in S30, namely the number of discriminants Neg. Therefore, assuming that the minimum number of real-world estimated points Poe that are misidentified as ghost observation points Pog when the transparent outer surface 522a of the radome 522 of the radar sensor 52 is contaminated is the number of discriminants Neg in equation 1, the ghost ratio Rg is set as the determination threshold in S40. Consequently, the determination in S40 that the ghost ratio Rg has risen to the contamination detection range is affirmative if it rises above that determination threshold.
[0040] If an affirmative judgment is made in S40 of Figure 4, S50 is executed in response to the affirmative judgment. In S50, the data output block 130 determines that there is dirt on the radar sensor 52. Therefore, in S50, the data output block 130 further generates and outputs observation data Do, which includes the dirt information that the radar sensor 52 has been determined to have dirt on it. At this time, the observation data Do may be generated including at least the former of the observation information for each radar observation point Po and the type discrimination information, together with the dirt information of the radar sensor 52.
[0041] On the other hand, if a negative judgment is made in S40, S60 is executed in response to that negative judgment. In S60, the data output block 130 determines that there is no contamination on the radar sensor 52. Therefore, in S60, the data output block 130 further generates and outputs observation data Do so that it includes contamination information indicating that there is no contamination on the radar sensor 52. At this time, the observation data Do is generated including both observation information for each radar observation point Po and type discrimination information.
[0042] In both S50 and S60, the output of the generated observation data Do may be storage in the memory 10 that constitutes the radar observation system 1. The output of the generated observation data Do may be transmission to a computer in the host vehicle 2, such as a driving control ECU. The output of the generated observation data Do may be transmission to a computer outside the host vehicle 2, which constitutes an external center or a mobile terminal or the like capable of wireless communication with the host vehicle 2, for example. As described above, when the execution of both S50 and S60 is completed, the current execution of the observation flow ends.
[0043] (Effects) The operational effects of the first embodiment described above will be described below.
[0044] According to the first embodiment, based on the discriminant feature information Itg obtained by analyzing a received signal, a plurality of radar observation points Po in the driving scene of the host vehicle 2 are discriminated into entity observation points Pot representing the entity of the target To and ghost observation points Pog representing ghosts other than the target To. On the other hand, based on entity feature information Ie different from the discriminant feature information Itg obtained by analyzing the received signal, an entity estimation point Poe is monitored as a radar observation point Po estimated to be the entity of the target To in the driving scene.
[0045] In such a first embodiment, when the radar sensor 52 becomes dirty, the ghost ratio Rg, which is the ratio of the discriminated number Neg of the ghost observation points Pog among the entity estimation points Poe to the monitored number Ne of the monitored entity estimation points Poe, increases. Therefore, according to the observation data Do obtained by determining that the radar sensor 52 is dirty, which is output in response to the ghost ratio Rg increasing to the dirt determination range, it becomes possible to distinguish the entity of the target To not only from ghosts but also from the dirt, thereby securing observation accuracy.
[0046] According to the first embodiment, an estimated entity point Poe is monitored based on clustering information as entity feature information Ie obtained by grouping each radar observation point Po for each received signal through clustering analysis. Accordingly, contamination of the radar sensor 52 can be determined in accordance with the ghost ratio Rg that correlates to the monitored number Ne of estimated entity points Poe, which depends on the entity probability within the grouped cluster Cp. Therefore, with the observation data Do for which contamination is determined according to such ghost ratio Rg, it becomes possible to distinguish the entity of the target To from ghosts and contamination, thereby improving observation accuracy.
[0047] Here, particularly according to the first embodiment, within the same specific cluster Cp grouped by clustering analysis, an estimated entity point Poe which is the radar observation point Po closest to the host vehicle 2 is monitored. Accordingly, contamination of the radar sensor 52 can be determined in accordance with the ghost ratio Rg that correlates to the monitored number Ne of estimated entity points Poe, which has the highest entity probability within the specific cluster Cp. Therefore, with the observation data Do for which contamination is determined according to such ghost ratio Rg, it becomes possible to guarantee securing high observation accuracy by distinguishing the entity of the target To from ghosts and contamination.
[0048] According to the first embodiment, radar observation points Po are discriminated based on deviation information as discrimination feature information Itg, which indicates deviation between an entity observation point Pot and a ghost observation point Pog obtained through azimuth analysis for each received signal. Accordingly, contamination of the radar sensor 52 can be determined in accordance with the ghost ratio Rg that correlates to the discriminated number Neg of ghost observation points Pog reflecting azimuth for each received signal. Therefore, with the observation data Do for which contamination is determined according to such ghost ratio Rg, it becomes possible to distinguish the entity of the target To from ghosts and contamination, thereby improving observation accuracy.
[0049] According to the first embodiment, discrimination of radar observation points Po and monitoring of estimated entity points Poe are preferably performed particularly in a traveling scene where the host vehicle 2 travels inside a tunnel. According to this configuration, even in a tunnel where the occurrence rate of ghosts increases, it becomes possible to secure observation accuracy by distinguishing the entity of the target To not only from the ghosts but also from contamination.
[0050] According to the first embodiment, the identification of the radar observation point Po and the monitoring of the entity estimation point Poe should also be performed in driving scenes where the host vehicle 2 is subjected to radar interference from other vehicles traveling around it. This makes it possible to ensure observation accuracy by distinguishing the actual target To not only from the ghost but also from dirt, even under radar interference conditions where the occurrence rate of ghosts tends to increase.
[0051] (Second Embodiment) The second embodiment is a modification of the first embodiment. As shown in Figure 8, in the observation flow of the second embodiment, S2030 is executed instead of S30 in the first embodiment. In S2030, the estimation point monitoring block 120 monitors the physical estimation point Poe of the nearest position from the host vehicle 2 based on the assumption of a triangular path, and also monitors the physical estimation point Poe while excluding ghosts caused by mirror paths as shown in Figure 9.
[0052] Specifically, in S2030, as shown in Figure 9, entity feature information Ie regarding stationary structures Tos present around the host vehicle 2 as a type of target To is acquired based on at least one of the map information from the map database 7 and sensing information from external sensors 51 other than the radar sensor 52. Therefore, in S2030, radar observation points Po observed on the side closer to the host vehicle 2 than the stationary structures Tos present around the host vehicle 2, i.e., closer to the host vehicle 2 than the stationary structures Tos, are additionally monitored as entity estimation points Poe based on entity feature information Ie which is different from the discrimination feature information Itg. As a result, ghost observation points Pog caused by mirror paths, as shown in Figure 9, are excluded from the number of monitored entity estimation points Poe Ne.
[0053] Thus, according to the second embodiment, the entity estimation point Poe is monitored not only based on entity feature information Ie, which is clustering information, but also based on entity feature information Ie that is at least one of map information and sensing information other than the radar sensor. As a result, the contamination of the radar sensor 52 can be determined according to the ghost ratio Rg, which correlates with the number of monitored entity estimation points Ne, excluding ghost observation points Pog. Therefore, by using observation data Do in which contamination has been determined according to such ghost ratio Rg, it becomes possible to distinguish the entity of target To from ghosts and contamination and ensure high observation accuracy.
[0054] (Third Embodiment) The third embodiment is a modification of the first embodiment. As shown in Figure 10, in the observation flow of the third embodiment, S3030 is executed instead of S30 in the first embodiment. In S3030, the estimation point monitoring block 120 monitors the physical estimation point Poe at the nearest position from the host vehicle 2 based on the assumption of a triangular path, and also monitors the physical estimation point Poe based on signal power information which is physical feature information Ie that is different from the discriminant feature information Itg. At this time, the signal power information which constitutes the physical feature information Ie is obtained by analyzing the reflected power (including the estimated power from the reflection cross-section) for each received signal from each radar observation point Po, as a power analysis different from the azimuth analysis in S20.
[0055] Thus, according to the third embodiment, the entity estimation point Poe is monitored not only based on clustering information as entity feature information Ie, but also based on signal power information as entity feature information Ie obtained by power analysis for each received signal from the radar observation point Po. As a result, contamination of the radar sensor 52 can be determined according to the ghost ratio Rg correlated with the number of monitored entity estimation points Ne, where an increase in power indicates a high degree of entity accuracy. Therefore, by using observation data Do in which contamination has been determined according to such ghost ratio Rg, it becomes possible to distinguish the entity of target To from ghosts and contamination and ensure high observation accuracy.
[0056] (Fourth Embodiment) The fourth embodiment is a modification of the first embodiment. As shown in Figure 11, in the observation flow of the fourth embodiment, S4030 is executed instead of S30 in the first embodiment. In S4030, the estimation point monitoring block 120 monitors the physical estimation point Poe at the nearest position from the host vehicle 2 based on the assumption of a triangular path, in addition to the processing of S30. It also monitors the physical estimation point Poe based on tracking information which is physical feature information Ie that is different from the discrimination feature information Itg. At this time, the tracking information which constitutes the physical feature information Ie is obtained by performing a tracking analysis different from the azimuth analysis of S20, by continuously performing a tracking analysis on each radar observation point Po corresponding to each received signal from S4030 of the past observation frame.
[0057] Thus, according to the fourth embodiment, the entity estimation point Poe is monitored not only based on clustering information as entity feature information Ie, but also based on tracking information as entity feature information Ie obtained by tracking analysis of each radar observation point Po for each received signal. As a result, continuous observation by tracking indicates a high degree of entity accuracy, and the contamination of the radar sensor 52 can be determined according to the ghost ratio Rg correlated with the number of monitored entity estimation points Ne. Therefore, by using observation data Do in which contamination has been determined according to such ghost ratio Rg, it becomes possible to distinguish the entity of target To from ghosts and contamination and ensure high observation accuracy.
[0058] (Other Embodiments) Although several embodiments have been described so far, this disclosure is not to be construed as being limited to those embodiments, and can be applied to various embodiments and combinations without departing from the spirit of this disclosure.
[0059] In the modified embodiments of the first to fourth embodiments, the dedicated computer constituting the radar observation system 1 may have at least one of the digital and analog circuits that make up, for example, the transmit / receive processing unit 521 or other in-vehicle units, as a processor. Here, the digital circuit is at least one of the following: ASIC (Application Specific Integrated Circuit), FPGA (Field Programmable Gate Array), SOC (System on a Chip), PGA (Programmable Gate Array), and CPLD (Complex Programmable Logic Device). Furthermore, such a digital circuit may have a memory that stores a program.
[0060] In the modified versions of the first to fourth embodiments, S20 may be executed following S30, S2030, S3030, and S4030. In the modified version of the second embodiment, in S2030, monitoring of the entity estimation point Poe based on entity feature information Ie, which is at least one of the map information and sensing information other than radar sensor information, may be performed regardless of the presence or absence of a stationary structure Tos. In the modified versions of the second to fourth embodiments, the processing of S30 may be omitted in S2030, S3030, and S4030. In the modified versions of the second to fourth embodiments, at least two of the processing of S2030, S3030, and S4030 may be executed simultaneously.
[0061] The first to fourth embodiments and modifications described above may be implemented in the form of a processing unit (e.g., a processing ECU) or a semiconductor device (e.g., a semiconductor chip) as a radar observation device configured to be mounted on a host vehicle 2 and having at least one processor 12 and one memory 10.
[0062] (Addendum) This specification discloses several technical ideas and several combinations thereof, as listed below. The symbols in parentheses in this addendum indicate the correspondence with the specific means described in the embodiments described in detail earlier, and do not limit the technical scope of this disclosure.
[0063] (Technical Concept 1) A radar observation system having a processor (12) for performing observation processing, which generates observation data (Do) of a target (To) observed by a radar sensor (52) of a host vehicle (2), wherein the processor distinguishes a plurality of radar observation points (Po) in the driving scene of the host vehicle from actual observation points (Pot) representing the actual target and ghost observation points (Pog) representing ghosts other than the target, based on discrimination feature information (Itg) obtained by analyzing the received signal reflected from the target in response to the transmitted signal from the radar sensor, and monitors an estimated actual point (Poe) as the radar observation point estimated to be the actual target in the driving scene, based on actual feature information (Ie) obtained by analyzing the received signal which is different from the discrimination feature information. A radar observation system configured to output observation data in which the contamination of the radar sensor is determined in response to the number of monitored entity estimation points (Ne) being determined to be ghost observation points (Rg) rising to the contamination determination range.
[0064] (Technical Concept 2) The radar observation system according to Technical Concept 1, wherein the monitoring of the entity estimation points includes monitoring the entity estimation points based on clustering information as entity characteristic information, which is obtained by grouping each of the radar observation points for each received signal using clustering analysis.
[0065] (Technical Concept 3) The radar observation system according to Technical Concept 2, wherein the monitoring of the entity estimation point includes monitoring the entity estimation point, which is the nearest radar observation point from the host vehicle, within the same specific cluster (Cp) grouped by the clustering analysis.
[0066] (Technical Concept 4) The radar observation system according to Technical Concept 2, wherein the monitoring of the entity estimation point is also based on the entity characteristic information, which is at least one of the map information and sensing information other than the radar sensor.
[0067] (Technical Concept 5) A radar observation system according to any one of Technical Concepts 1 to 4, which includes monitoring the entity estimation points based on signal power information as entity characteristic information obtained by power analysis for each received signal from each radar observation point.
[0068] (Technical Idea 6) A radar observation system according to any one of Technical Ideas 1 to 5, which includes monitoring the entity estimation point based on tracking information as entity characteristic information obtained by tracking analysis of each of the radar observation points for each received signal.
[0069] (Technical Idea 7) A radar observation system according to any one of Technical Ideas 1 to 6, which includes determining the radar observation point based on the deviation information as discrimination feature information, which is the deviation between the physical observation point and the ghost observation point due to the azimuth analysis of each received signal.
[0070] (Technical Concept 8) The radar observation system described in any one of Technical Concepts 1 to 7 is performed during the driving scene in which the host vehicle is driving inside a tunnel, and the identification of the radar observation point and the monitoring of the entity estimation point are performed.
[0071] (Technical Concept 9) The radar observation system according to any one of Technical Concepts 1 to 8, wherein the identification of the radar observation point and the monitoring of the entity estimation point are performed in the driving scene in which the host vehicle is subjected to radar interference from other vehicles driving around it.
[0072] Furthermore, the technical concepts 1 to 9 described above may also be understood within the respective technical concepts of the methods and programs.
Claims
1. A radar observation system having a processor (12) for performing observation processing, which generates observation data (Do) of a target (To) observed by a radar sensor (52) of a host vehicle (2), wherein the processor distinguishes a plurality of radar observation points (Po) in the driving scene of the host vehicle from actual observation points (Pot) representing the actual target and ghost observation points (Pog) representing ghosts other than the target, based on discrimination feature information (Itg) obtained by analyzing the received signal reflected from the target in response to the transmitted signal from the radar sensor, and monitors an estimated actual point (Poe) as the radar observation point estimated to be the actual target in the driving scene, based on actual feature information (Ie) obtained by analyzing the received signal which is different from the discrimination feature information. A radar observation system configured to output observation data in which the contamination of the radar sensor is determined in response to the number of monitored entity estimation points (Ne) being determined to be ghost observation points (Rg) rising to the contamination determination range.
2. The radar observation system according to claim 1, wherein the monitoring of the entity estimation points includes monitoring the entity estimation points based on clustering information as entity characteristic information, which is obtained by grouping each of the radar observation points for each received signal by clustering analysis.
3. The radar observation system according to claim 2, wherein the monitoring of the entity estimation points includes monitoring the entity estimation points that are the nearest radar observation points from the host vehicle within the same specific cluster (Cp) grouped by the clustering analysis.
4. The radar observation system according to claim 2, wherein the monitoring of the entity estimation point is also based on the entity characteristic information, which is at least one of the map information and sensing information other than the radar sensor.
5. The radar observation system according to any one of claims 1 to 4, wherein the monitoring of the entity estimation points is performed based on signal power information as entity characteristic information, which is power-analyzed for each of the received signals from each of the radar observation points.
6. The radar observation system according to any one of claims 1 to 4, wherein the monitoring of the entity estimation point includes monitoring the entity observation point based on tracking information as entity characteristic information obtained by tracking analysis of each of the radar observation points for each received signal.
7. The radar observation system according to any one of claims 1 to 4, wherein the determination of the radar observation point is made based on the deviation information as determination feature information, which is the deviation between the physical observation point and the ghost observation point as determined by the azimuth analysis of each received signal.
8. The radar observation system according to any one of claims 1 to 4, wherein the determination of the radar observation point and the monitoring of the physical location are performed during the driving scene in which the host vehicle is driving inside the tunnel.
9. The radar observation system according to any one of claims 1 to 4, wherein the determination of the radar observation point and the monitoring of the entity estimation point are performed in the driving scene in which the host vehicle is subjected to radar interference from other vehicles driving around it.
10. A radar observation device configured to be mounted on a host vehicle (2), having a processor (12) for performing observation processing, which generates observation data (Do) of a target (To) observed by a radar sensor (52) of the host vehicle, wherein the processor distinguishes between multiple radar observation points (Po) in the driving scene of the host vehicle and ghost observation points (Pog) representing ghosts other than the target, based on discrimination feature information (Itg) obtained by analyzing the received signal reflected from the target in response to the transmitted signal from the radar sensor, and monitors an estimated entity point (Poe) as the radar observation point estimated to be the actual target in the driving scene, based on entity feature information (Ie) obtained by analyzing the received signal that is different from the discrimination feature information. A radar observation device configured to output observation data in which contamination of the radar sensor is determined in response to the number of monitored entity estimation points (Ne) being determined to be ghost observation points (Rg) rising to the contamination determination range.
11. A radar observation method executed by a processor (12) to perform observation processing, wherein the radar sensor (52) of a host vehicle (2) observes a target (To) and generates observation data (Do), the method comprising: distinguishing a plurality of radar observation points (Po) in the driving scene of the host vehicle from actual observation points (Pot) representing the actual target and ghost observation points (Pog) representing ghosts other than the target, based on discrimination feature information (Itg) obtained by analyzing the received signal from the radar sensor; and monitoring an estimated actual point (Poe) as the radar observation point estimated to be the actual target in the driving scene, based on actual feature information (Ie) obtained by analyzing the received signal that is different from the discrimination feature information. A radar observation method comprising outputting observation data in which the contamination of the radar sensor is determined in response to the number of monitored entity estimation points (Ne) being determined to be ghost observation points (Neg) rising to the contamination determination range.
12. A radar observation program that is stored in a storage medium (10) to perform observation processing, which generates observation data (Do) obtained by a radar sensor (52) of a host vehicle (2) observing a target (To), and includes instructions for causing a processor (12) to execute said observation processing, wherein the program distinguishes a plurality of radar observation points (Po) in the driving scene of the host vehicle from actual observation points (Pot) representing the actual target and ghost observation points (Pog) representing ghosts other than the target, based on discrimination feature information (Itg) obtained by analyzing the received signal from the radar sensor, and monitors an estimated actual point (Poe) as the radar observation point that is estimated to be the actual target in the driving scene, based on actual feature information (Ie) obtained by analyzing the received signal that is different from the discrimination feature information. A radar observation program including the command to output observation data in which the radar sensor is found to be dirty, in response to the number of monitored entity estimation points (Ne) being identified as ghost observation points (Neg) rising to the dirt detection range.