Abstract:
Traffic density is estimated around a host vehicle moving on a roadway. An object detection system remotely senses and identifies the positions of nearby vehicles. A controller a) predicts a path of a host lane being driven by the host vehicle, b) bins the nearby vehicles into a plurality of lanes including the host lane and one or more adjacent lanes flanking the predicted path, c) determines a host lane distance in response to a position of a farthest vehicle that is binned to the host lane, d) determines an adjacent lane distance in response to a difference between a closest position in an adjacent lane that is within the field of view and a position of a farthest vehicle binned to the adjacent lane, and e) indicates a traffic density in response to a ratio between a count of the binned vehicles and a sum of the distances.

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     Not Applicable. 
     STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH 
     Not Applicable. 
     BACKGROUND OF THE INVENTION 
     The present invention relates in general to monitoring traffic surrounding a motor vehicle, and, more specifically, to a method and apparatus for classifying on-board and in real time a traffic density within which a host vehicle is moving. 
     For a variety of automotive systems and functions, it can be useful to have available an estimate of local traffic density (including estimations of traffic density in the direct forward path of the vehicle, in adjacent lanes, and an aggregate or overall traffic density in the vicinity of the vehicle). For example, the warning thresholds (e.g., distances or buffer zones) for a collision warning system may be adjusted depending on whether traffic density is light, medium, or heavy. In addition, a driver alertness monitoring system may use different thresholds according to the traffic density. 
     Conventionally, traffic density estimations have been obtained in various ways. In one automated technique, a rough estimate of traffic density is found by tracking cell phones passing through designated roadway locations (e.g., a central monitor obtains GPS or cell tower-based coordinates of individual phones, maps them onto roadway segments, calculates a vehicle density, and communicates the result to the vehicles). Other automated techniques for counting the number of vehicles present at a road segment can also be used. These approaches give only a general idea of how many vehicles are within a fixed area (i.e., not specific to the immediate area around any particular vehicle). They have other disadvantages including that the update rate is slow, the vehicle must have wireless communication in order to access the information, and infrastructure must be provided for performing the calculations outside of the host vehicle. 
     In another approach, drivers or other observers may visually characterize the amount of traffic in an area. This is subject to the same disadvantages, and may be less accurate. In yet another approach, a Vehicle-to-Infrastructure system may be used to characterize the traffic density. This is subject to high costs of implementing hardware on both the vehicles and the roadside. Additionally, a sufficient market penetration would be needed in order for this to be feasible. 
     SUMMARY OF THE INVENTION 
     In one aspect of the invention, a method is provided for an electronic controller in a host vehicle to determine a traffic density. A sensor remotely senses objects within a field of view around the host vehicle. Positions are identified of nearby vehicles within the sensed objects. A path of a host lane being driven by the host vehicle is predicted. The electronic controller bins the nearby vehicles into a plurality of lanes including the host lane and one or more adjacent lanes flanking the predicted path. The electronic controller determines a host lane distance in response to a position of a farthest vehicle that is binned to the host lane, and then determines an adjacent lane distance in response to a difference between a closest position in an adjacent lane that is within the field of view and a position of a farthest vehicle binned to the adjacent lane. The electronic controller indicates a traffic density in response to a ratio between a count of the binned vehicles and a sum of the distances. 
     In a preferred embodiment, the vehicle locations on the surrounding roadway are estimated through the use of an on-board forward looking sensor. Additional vehicle sensors such as side looking blind spot sensors or rear looking sensors can also be used. 
     The relative positions of nearby vehicles (laterally and longitudinally) are acquired from the forward looking sensor. This can be either directly in Cartesian form or calculated from polar coordinates. All of the target vehicles that are detected by the forward looking sensor are then be binned into “lanes” based on their offset from the predicted path of the host vehicle. The predicted path may be determined from a yaw rate sensor or GPS Map data, for example. Based on a typical lane width, the host lane is considered to occupy an area +/− one-half of the lane width around the predicted path. An adjacent lane to the right of the host measured from the host&#39;s center line goes from +½ lane width to +1½ lane width, while an adjacent lane to the left measured from the host&#39;s center line goes from −½ lane width to −1½ lane width. This calculation can be carried out to any desired number of total lanes of interest. 
     With the vehicles all binned to lanes, a count is then performed to determine the total number of vehicles that are seen in each lane. For the host vehicle&#39;s lane, the count should include the host vehicle. To complete a density calculation, a value for the monitored distance within each lane is needed. For the host&#39;s lane, this is done by determining which vehicle is the farthest forward in the host&#39;s lane. The length of the host vehicle and an estimate of the most forward vehicle&#39;s length are preferably added to the longitudinal relative position measured from the front of the host vehicle to the rear of the most forward in lane vehicle to yield a longitudinal distance in which vehicles are seen for the host&#39;s lane. If no forward vehicles are seen, then the distance may default to the maximum reliable detection distance of the sensor. 
     For the adjacent lanes, a distance is preferably determined in response to the field of view from the location of the forward looking sensor to determine the closest point to the host vehicle that a vehicle in the adjacent lane could be detected. This detection distance is then subtracted from the longitudinal relative position of the most forward vehicle in the adjacent lane (preferably again adding a length estimate for the detected vehicle and defaulting to a maximum detection distance if no vehicles are found). The ratio of each respective count to the respective detection distance gives the traffic density for the respective lane. An overall density is obtained from the ratio of the total count to the summed distances. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  shows a host vehicle on a roadway with surrounding traffic. 
         FIG. 2  is a block diagram of one embodiment of vehicle apparatus according to the present invention. 
         FIGS. 3A and 3B  show a vehicle&#39;s predicted path and potential lane positions corresponding to the predicted path. 
         FIG. 4  is a diagram showing nearby vehicles binned to respective lanes with their ranges from the host vehicle or from the position in an adjacent lane where the vehicle would enter the sensor field of view. 
         FIG. 5  is a flowchart of one preferred embodiment of the invention. 
         FIG. 6  is a flowchart of a method for validating adjacent lanes. 
         FIG. 7  is a plot of an estimated traffic density during one example of a portion of a driving cycle. 
     
    
    
     DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS 
     Referring now to  FIG. 1 , a divided roadway  10  is being traversed by a host vehicle  11  moving along a host lane  12  which is flanked by a right adjacent lane  13  and a left adjacent lane  14 . A second left adjacent lane  15  carries opposing traffic. Host vehicle  11  is equipped with a forward-looking remote object recognition and tracking system which may be comprised of a commercial, off-the-shelf remote sensing system such as the ESR electronically scanning radar system available from Delphi Automotive LLP or the forward looking safety system available from TRW Automotive Holdings Corporation. The systems may employ radar sensors and/or optical camera or video systems to sense remote objects within a field of view around the host vehicle and to track distinct objects over time. As a result of the tracking, the systems report a list of objects comprising an identification of each type of object, its relative position, and its current movement. As shown in  FIG. 1 , the object detection system may have a field of view  16  which in this preferred embodiment corresponds to a forward-looking system. 
       FIG. 2  shows host vehicle  11  with components for implementing the present invention. A radar transceiver  20  is coupled with a radar antenna  21  to transmit scanning radar signals  22  and then receiving reflected signals from a nearby object  23  (such as an adjacent vehicle). Remote objects may also be optically detected (e.g., in visible light) using a camera system  24 . Transceiver  20  and camera  24  are coupled to an object detection and tracking module  25  of a conventional design to provide an integrated remote object detection system which provides a list of tracked objects to a traffic density control module  26 . For each object being tracked, the list may include various parameters including but not limited to a relative position, type of object (e.g., car or large truck), relative velocity, and/or absolute velocity. 
     In operation, traffic density controller  26  identifies a predicted path of the host vehicle in one of several ways. For example, an optically-based lane detection system  27  coupled to camera  24  may employ pattern recognition to detect lane markers or other features to locate the roadway lanes. Thus, the paths of the host lane and adjacent lanes may be fed directly to controller  26 . Alternatively, a vehicle yaw sensor  28  may be coupled to controller  26  for providing lateral acceleration information to be used by controller  26  to predict the lane path. In another alternative, a GPS navigation/mapping system  30  may be coupled to controller  26  for identifying lane locations based on using detected geographic coordinates of host vehicle  11  as a pointer onto a roadway map. 
     Based upon vehicle counts and lane distances as determined below, controller  26  generates traffic density indications for the purpose of providing them to other appropriate controllers (not shown) and/or functions that modify their performance in accordance with the traffic density. The indications may be communicated within the vehicle over a multiplex bus  31 . Based on the indicated traffic density, the other systems may adjust thresholds or other aspects of their system operation to account for the actual traffic conditions determined in the immediate vicinity of the host vehicle on-board and in real-time. 
     As shown in  FIG. 3A , host vehicle  11  has a predicted path  33  which may be used to infer the upcoming area traversed by a host lane. When using a yaw sensor in order to predict the vehicle path based on lateral acceleration, a sufficiently low or substantially zero lateral acceleration leads to a prediction of a straight lane path. Larger lateral accelerations lead to prediction of an increasingly curved lane path. As shown in  FIG. 3B , the predicted course of the host lane is centered on predicted path  33  and extends by ½ of a predetermined lane width W to either side. Based on the predicted course of the host lane, a plurality of adjacent lane paths are defined including a left adjacent path L 1 , a right adjacent lane path R 1 , and a second right lateral adjacent lane path R 2  flanking the host lane in a parallel manner. 
     Once the host and adjacent lanes have been laid out relative to the position of the host vehicle, each tracked vehicle can be binned according to the areas covered by the lanes.  FIG. 4  shows an example of binned vehicles relative to a host vehicle  35  in a host lane  36 . Although four vehicles are shown in host lane  36 , an actual vehicle count of three is obtained (i.e., vehicles  35 ,  43 , and  44  are counted). A vehicle  45  which is within a maximum detection distance of the object detection system is not counted because it is not detected (e.g., vehicle  44  is a large truck and blocks the potential view of vehicle  45 ). For a left adjacent lane  37 , a lane count of one would result because of the presence of a vehicle  38 . In a right adjacent lane  40 , a vehicle count of two is obtained due to the presence of vehicles  41  and  42 . 
     With the count information obtained, the next step is to derive the roadway distances over which the counted vehicles are distributed. Within the field of view of the remote sensors, there is a maximum detection distance for sensing any vehicles that may be present. Whenever vehicles are present, however, the view out to the maximum distance may be blocked by a detected vehicle. In the example of  FIG. 4 , the vehicles counted in host lane  36  include vehicle  43  detected at a range R 1  and vehicle  44  detected at a range R 2 . Undetected vehicle  45  which is present in lane  36  does not contribute to the count, and the corresponding portion of host lane  36  should not contribute to the density calculation. Therefore, the distance within each respective lane to be used in the density calculation corresponds with a farthest vehicle that is binned to that lane. In host lane  36 , the farthest vehicle is vehicle  44  so that the host lane distance is comprised of range R 2  between host vehicle  35  and vehicle  44 . Preferably, the distance used for calculating density also comprises the addition of a length L H  of the host vehicle and a length L 1  for vehicle  44 . 
     In an adjacent lane to the side of host vehicle  35 , the appropriate distance to be used as a basis for the density calculation usually does not begin at a point even with the host vehicle because the field of view for the sensing system is unlikely to correspond with the exact front of host vehicle  35 . When using just a forward-looking detector, a vehicle in an adjacent lane must be at least slightly ahead of host vehicle  35  in order to be detected. Locations  46  and  47  in the adjacent lanes correspond to a closest position in those adjacent lanes that is within the field of view of the sensors. These locations can be measured in advance during the design of the vehicle. 
     For an object detection system with other types of sensors, the beginning position for the distance measurement can be at other positions relative to the host vehicle. For detectors with side-looking sensors or rear-looking sensors, the starting position for determining adjacent lane distances could even be behind host vehicle  35  or could be defined according to a farthest detected adjacent vehicle behind the host vehicle. 
     For right adjacent lane  40 , the adjacent lane distance to be used in the traffic density calculation comprises a range R 5  between position  47  and a farthest vehicle  42  in lane  40  plus a length L 3  corresponding to the type of vehicle identified by the object tracking system (e.g., a representative car or truck length). Similarly, a distance for adjacent lane  37  comprises a range R 3  between position  46  and vehicle  38  plus an incremental length L 2  of vehicle  38  (either estimated or measured). 
       FIG. 5  shows one preferred method of the invention wherein remote sensing of objects around a host vehicle is performed in step  50 . In the remote object detection system, the sensed vehicles are identified by type, location, and speeds for tracking over time in step  51 . In step  52 , the traffic density controller predicts a path of the host lane. Using the predicted path of the host lane and the corresponding positions of adjacent lanes which flank the host lane, all detected vehicles are binned into the lanes in step  53 . 
     In step  54 , the furthest ahead vehicle is found for each lane having a vehicle present. For the host lane, this distance along with the host length and furthest vehicle length is used to derive the distance over which vehicles in the lane are distributed. For the adjacent lanes, it is the furthest vehicle and length in combination with the closest detectable point in the lane that is used. If no vehicles are present in a lane, then the associated distance defaults to a maximum detection distance of the sensors along the predicted path of the respective lane. This predetermined maximum detection distance may be a fixed value stored in the controller or could be calculated based on environmental factors such as the height of the horizon. In step  55 , a density is calculated for each lane equal to the respective vehicle count divided by the distance determined for each respective lane. In step  56 , an overall density equal to the total count divided by the sum of distances is determined. 
     The raw traffic density values obtained in steps  55  and  56  can be directly used, or the raw values may be normalized or classified in step  57 . Normalizing may preferably be comprised of transforming the values onto a scale between 0 and 1, determined as a percentage of a predetermined heavy traffic density threshold. For example, a raw value for an overall traffic density would be divided by the threshold and then clipped to a maximum value of 1. The predetermined heavy threshold may be empirically derived based on the prevalent traffic conditions in the market where the vehicle is to be sold and used. 
     Alternatively, classifying the raw traffic density values may be comprised of defining light, medium, and heavy traffic thresholds. Depending on the range in which the raw traffic density values fall, the corresponding level of light, medium, or heavy traffic density could be determined and reported to the other vehicle systems. Thus, the traffic density value or values, whether raw, normalized, or classified, are indicated to the appropriate functions or systems that need them in step  58 . 
     Preferably the method of the invention may be performed using only valid lanes that can be verified to exist around the host vehicle as shown in  FIG. 6 . For example, if the area corresponding to a potential adjacent lane is instead a shoulder of the road, then it would typically not be used in a density calculation. However, in some circumstances it may be desirable to monitor an object density in a shoulder region or other area to be used in identifying potential escape routes if potential collisions are detected. 
     To identify valid lanes, the method in  FIG. 6  begins by identifying a potential lane to be checked in step  60  (e.g., from a predetermined range of two adjacent lanes on each side of the host vehicle). A check is made whether any vehicle is in the identified lane in step  61 . If a moving vehicle is detecting in that lane, then the lane is considered valid for a predetermined period of time (e.g., 60 seconds) in step  62 . Then the method returns to step  60  identifying a next potential lane check. 
     If no vehicles are detected in the currently-examined lane in step  61 , then the method proceeds in step  63  wherein the present overall traffic density is used to determine a time value Y. In situations where a higher traffic density exists, the likelihood of an empty lane is reduced. In conditions of a light traffic density, the possibility of a valid lane being empty of vehicles for a longer period of time increases. Therefore, a time value Y is selected with a magnitude that reflects an average wait time during which it would be expected that a vehicle would again appear in the empty lane. In step  64 , a check is made to determine whether the potential lane being checked has been empty for the last Y seconds. If not, then the lane is still considered valid and a return is made to step  60 . If the lane has been empty for Y seconds, then it is not considered a valid lane in step  65 . The invalid lane may typically be excluded from the density calculations until a vehicle is detected in that potential lane. 
       FIG. 7  shows exemplary traffic density values obtained during a driving cycle in various traffic densities. The densities have been normalized in a range of 0 to 1 based on a heavy traffic threshold  70 . If it is desired to classifying the traffic densities into ranges, then a light traffic range  71  or a medium traffic range  72  can be reported to the other vehicle systems instead of the normalized value based on appropriate thresholds. 
     A further embodiment of the invention may include detecting a lane change maneuver of the host vehicle from an initial lane to a final lane, and then indicating a host lane traffic density as an aggregate of individual traffic lane densities for the initial lane and the final lane during the lane change maneuver. Yet another embodiment may include comparing a closing speed of a vehicle detected in an adjacent lane to a host speed of the host vehicle, and if the closing speed is greater than the host speed then the adjacent lane is indicated as an opposing lane.