Patent Publication Number: US-2022215673-A1

Title: Device, system, and method for generating occupancy grid map

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is a continuation application of International Application No. PCT/JP2020/028376 filed Jul. 22, 2020 which designated the U.S. and claims priority to Japanese Patent Application No. 2019-177053 filed with the Japan Patent Office on Sep. 27, 2019, the contents of each of which are incorporated herein by reference. 
    
    
     BACKGROUND 
     Technical Field 
     This disclosure relates to a technique for generating an occupancy grid map. 
     Related Art 
     Occupancy Grid Map (OGM) is known as one of methods to recognize travelable regions (free space) where vehicles can travel. According to the OGM, a region is divided into a grid, and each cell of the grid has an occupancy probability assigned that an object (three-dimensional object) occupies the cell. The occupancy probability for each cell is updated in chronological order. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       In the accompanying drawings: 
         FIG. 1  is a schematic diagram of an OGM generation system; 
         FIG. 2  is a schematic diagram of an OGM generation device; 
         FIG. 3  is an illustration of updating weights; 
         FIG. 4  is a flowchart of operations of the OGM generation device; 
         FIG. 5A  is an illustration of the accuracy of a PD map and updating weights for the PD map with high accuracy; and 
         FIG. 5B  is an illustration of the accuracy of a PD map and updating weights for the PD map with low accuracy. 
     
    
    
     DESCRIPTION OF SPECIFIC EMBODIMENTS 
     As to the above known occupancy grid map as disclosed in JP 2019-46147 A, it is difficult to set how much to update the occupancy probability for each cell in each frame (referred to as an updating width) when updating the above known occupancy grid map in time series. If the updating width is large, the probability changes significantly from frame to frame, resulting in a large occupancy probability even for clutter. On the other hand, if the updating width is too small, it will take time to identify a target. 
     In view of the above, it is desired to have an occupancy grid map generation device that can appropriately control an updating width for the occupancy probability. 
     One aspect of the present disclosure provides an occupancy grid map generation device for generating an occupancy grid map that indicates, for each of a plurality of cells of a grid into which a region is divided, an occupancy probability that an object occupies the cell. In the occupancy grid map generation device, a data acquisition unit is configured to acquire, from a detection device that detects an object therearound, detection result data of the object. An occupancy probability updating unit is configured to, based the detection result data, successively update the occupancy probability for each of the plurality of cells calculated at a previous time to calculate a latest occupancy probability. A map data acquisition unit is configured to acquire map data therearound. An updating weight calculation unit is configured to acquire data about presence or absence of an object in each of the plurality of cells therearound based on the map data, and determine a weight of the detection result data used by the occupancy probability updating unit to update the occupancy probability for the cell, based on a degree of match between the data about presence or absence of an object in the cell and the detection result data. 
     The present disclosure enables proper control of the updating width of the occupancy probability using the map data. 
     An occupancy grid map generation device (hereinafter referred to as an OGM generation device)  10  according to one embodiment of the present disclosure will now be described with reference to the accompanying drawings. In the present embodiment, the OGM generation device  10  is mounted to each of a plurality of vehicles, forming an occupancy grid map generation system (hereinafter referred to as an OGM generation system)  1 . The present embodiment is not limited thereto. The OGM generation device  10  may be used as a stand-alone unit and does not necessarily have to be mounted to a vehicle. 
       FIG. 1  illustrates a configuration of the OGM generation system  1 . The OGM generation system  1  includes the OGM generation devices  10  mounted to respective ones of a plurality of probe cars C and a probe data map server (hereinafter referred to as a PD map server)  30 . 
     Each probe car C transmits detection data of objects around the probe car C and data of the current location of the probe car C to the PD map server  30 . The PD map server  30  generates a probe data map (hereinafter referred to as a PD map) based on the detection data collected from the plurality of probe cars C. The PD map server  30  generates, in response to a request from each probe car C, distribute the PD map around the probe car C. 
       FIG. 2  illustrates the configuration of each OGM generation device  10 . The OGM generation device  10  is a generation device configured to generate an occupancy grid map that indicates, for each of a plurality of cells of a grid into which a region is divided, the occupancy probability that an object occupies the cell. 
     The OGM generation device  10  is connected to a camera  20 , a millimeter-wave radar  21 , and other sensors  22  mounted to the vehicle that is the probe car C. Images captured by the camera  20  are processed by a camera electronic control unit (ECU)  11  to detect objects in the surroundings. Data acquired by the millimeter-wave radar  21  is processed by a millimeter-wave ECU  12  to detect objects in the surroundings. Other sensors  22  are the other devices that detect objects in the surroundings, and data acquired by the other sensors  22  is processed by an others ECU  13 . The camera  20 , the millimeter-wave radar  21 , and the other sensors  22  correspond to devices configured to detect objects in the surroundings. Detection result data of objects acquired by the camera ECU  11 , the millimeter-wave ECU  12 , and the others ECU  13  is input to an occupancy probability updating unit  14 . In the following description, vehicle-mounted sensors, such as the cameras  20 , the millimeter-wave radar  21 , and the other sensors  22 , are referred to as autonomous sensors. Although the camera  20  and the millimeter-wave radar  21  are listed as an example, the types of sensors are not limited to these sensors. Various types of sensors are may be used as the other sensors  22 . One or more of the camera ECU  11 , the millimeter-wave 
     ECU  12 , and the others ECU  13  correspond to a data acquisition unit. 
     The occupancy probability updating unit  14  has a function of updating the occupancy probability for each cell in the OGM based on the detection result data of objects in the surroundings. The occupancy probability is a probability that a three-dimensional object is present in the cell. 
     The occupancy probability updating unit  14  updates the OGM at predefined timings, where the occupancy probability updating unit  14  successively updates the OGM generated at the previous timing, if any, by updating the occupancy probability for each cell in that OGM. That is, the occupancy probability updating unit  14  does not replace the occupancy probability for each cell based on the detection result data from the camera  20  and the millimeter-wave radar  21 , but updates the occupancy probability for each cell in the previously generated OGM based on the detection result data. In a condition where no OGM has been generated, such as at startup, the occupancy probability updating unit  14  calculates the occupancy probability for each cell in the OGM based on the detection result data. This is because there is not any OGM generated at the previous timing. 
     An updating weight calculation unit  15  has a function of calculating a weight of the detection result data used to update the occupancy probabilities. When weighting the likelihood of the detection points acquired based on the detection result data, the weight of the detection result data is increased. When weighting the occupancy probabilities for the OGM at the previous time, the weight of the detection result data is decreased. Therefore, the weight calculated by the updating weight calculation unit  15  allows an updating width when the occupancy probability updating unit  14  updates the occupancy probabilities to be controlled. The updating weight calculation unit  15  uses the PD map data to calculate the updating weight. 
     A current location calculation unit  16  has a function of detecting the current location of the own vehicle. The current location calculation unit  16  is, for example, a GPS receiver. A PD map acquisition unit  17  transmits data of the current location calculated by the current location calculation unit  16  to the PD map server  30 , and requests download of the PD map data around the current location. Upon receipt of the PD map data from the PD map server  30 , the PD map acquisition unit  17  forwards the received PD map to the updating weight calculation unit  15 . The PD map acquisition unit  17  corresponds to a map data acquisition unit. The current location calculation unit  16  corresponds to a current location detection unit. 
     The PD map data is generated using the detection result data collected from the plurality of probe cars C and includes data about objects around the probe car C. However, the accuracy of the PD map data depends on an amount and newness of probe data collected and is not necessarily constant. 
     The updating weight calculation unit  15  acquires data about the presence or absence of an object in each cell based on the PD map acquired from the PD map server  30 . Since the PD map is formed of data of latitude and longitude and object attribute, the updating weight calculation unit  15  converts the acquired PD map into a grid map as viewed from the own vehicle using data of the location of the own vehicle. This provides data about the presence or absence of an object in each of cells of a grid into which a region proximate to the own vehicle is divided (i.e., each cell proximate to the own vehicle). The updating weight calculation unit  15  then calculates a weight of the detection result data when updating the occupancy probability for each cell based on a degree of match between the data about the presence or absence of an object in each cell and the detection result data from the autonomous sensor. For the PD map having information in the form of a grid map, the data about the presence or absence of an object in each cell indicated by the PD map can be used to calculate the updating weight for each cell. 
       FIG. 3  illustrates the updating weights. In  FIG. 3 , “YES” and “NO” for the “PD MAP” respectively indicate the presence and absence of an object in the PD map data, and “YES” and “NO” for “AUTONOMOUS SENSORS” respectively indicate the presence and absence of an object based on the detection result data from the autonomous sensors. 
     As illustrated in  FIG. 3 , in a case where the presence or absence of an object is consistent between the PD map and the detection result from the autonomous sensors, the updating weight is set to “LARGE”. Conversely, in a case where the presence or absence of an object is not consistent between the PD map and the detection result from the autonomous sensors, the updating weight is set to “MEDIUM” or “SMALL”. That is, the updating weight is set larger when the presence or absence of an object is consistent than when it is not. Values of the updating weights “LARGE,” “MEDIUM,” and “SMALL” may be set experimentally as appropriate. 
     In a case where the presence or absence of an object is not consistent between the PD map and the detection result from the autonomous sensors, the updating weight is changed depending on whether the autonomous sensors determine that an object is present or absent. That is, in a case where the PD map indicates “NO” and the detection result from the autonomous sensors indicates “YES”, an object (e.g., a vehicle) may have appeared in a cell (e.g., a road) where nothing exists, and the detection result data from the autonomous sensors may not be wrong. Therefore, the updating weight based on the detection result data is set to “MEDIUM”. Conversely, in a case where the PD map indicates “YES” and the detection result from the autonomous sensors indicates “NO”, the detection result data from the autonomous sensors may be wrong. 
     Therefore, the updating weight is set to “SMALL”. When the state where the PD map indicates “NO” and the detection result from the autonomous sensors indicates “YES” continues for a certain number of consecutive frames, an object is likely present. Therefore, the updating weight may be set to “LARGE”. 
     The updating weight calculation unit  15  forwards the updating weight data to the occupancy probability updating unit  14 . When updating the occupancy probability for each cell based on the detection result data, the occupancy probability updating unit  14  uses the updating weights received from the updating weight calculation unit  15 . 
       FIG. 4  illustrates the operations of the OGM generation device  10 . The OGM generation device  10  acquires detection result data from the autonomous sensors (at S 10 ). The OGM generation device  10  then acquires detection points of objects based on the detection result data and estimates a likelihood of the detection points of each object (at S 11 ). 
     The OGM generation device  10  acquires PD map data around the current location from the PD map server  30  (at S 12 ), and based on the PD map data and the detection result data, calculates weights of the detection result data used when updating the occupancy probability for each cell in the OGM (at S 13 ). Based on the detection points from the autonomous sensors, the OGM generation device  10  updates the OGM using the weights calculated by the updating weight calculation unit  15  (at S 14 ). 
     The configuration of the OGM generation device  10  in the present embodiment has been described above. An example of hardware of the OGM generation device  10  is a computer including a CPU, a RAM, a ROM, a hard disk, a display, a communication interface, and the like. A program including modules to implement the above functions of the blocks  11 - 17  illustrated in  FIG. 10  is stored in the RAM or ROM. The above OGM generation device is implemented by the CPU executing the program. Such a program is also included in the scope of the present disclosure. 
     The configurations and operations of the OGM generation system  1  and the OGM generation device  1  have been described. 
     The OGM generation device  10  according to the above-described embodiment uses information about the presence or absence of an object based on the PD map data to calculate the updating weights that reflect the detection result from the autonomous sensors, which allows the OGM to be updated with appropriate updating weights. When the detection result data from the autonomous sensors matches the PD map data, the updating weights are increased. This enables earlier identification of a target, which can reduce undetected objects. Conversely, when the detection result data from the autonomous sensors does not match the PD map data, the updating weights are decreased. This can reduce false detections. 
     The OGM generation device of the present embodiment has been described above, but the OGM generation device of this disclosure is not limited to the specific embodiment described above. The OGM generation device of this disclosure may calculate the updating weights for the OGM based on the accuracy of the PD map. The accuracy of the PD map may be determined from the amount of probe data used to generate the PD map. The accuracy of the PD map generated from a large amount of probe data is high, while the accuracy of the PD map generated from a small amount of probe data can not be high. In the present disclosure, the accuracy of the PD map with the amount of probe data used to generate the PD map equal to or greater than a predefined threshold may be high. The accuracy of the PD map with the amount of probe data used to generate the PD map less than the threshold may be low. 
       FIG. 5A  illustrates the updating weights for the PD map with high accuracy, and  FIG. 5B  illustrates the updating weights for the PD map with low accuracy. In  FIGS. 5A and 5B , the updating weights are indicated on a  5 -point numerical rating scale. As illustrated in  FIGS. 5A and 5B , as the accuracy of the PD map increases, the updating weights for a high degree of match between the PD map and the autonomous sensors may be increased. Although  FIGS. 5A and 5B  illustrates the updating weights for the accurate and inaccurate PD maps, the updating weights may be changed according to the date and time the PD map was updated. That is, the more recent the date and time of the latest update, the greater the updating weights for a high degree of match between the autonomous sensors and the PD map. 
     In the above-described embodiment, the data about the presence or absence of an object based on the PD map is a binary value of “YES” or “NO,” but information about the presence or absence of an object may be a probability value. The updating weights may be calculated based on the degree of match between this probability value and the likelihood of the detection points acquired at step S 11 . 
     In the above-described embodiment, the PD map is used as map data, but the map data does not have to be the PD map. 
     Electronic maps compiled and produced by any company may be used as the map data. 
     In the above-described embodiment, the updating weights are calculated using map data, but they may also be calculated based on data acquired via a vehicle-to-vehicle communication, a roadside-to-vehicle communication or the like. 
     This disclosure is useful to provide a device for generating an occupancy probability map in a region proximate to the own vehicle.