Patent Publication Number: US-11049393-B2

Title: Systems and methods for vehicle to improve an orientation estimation of a traffic participant

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application is a national phase filing under 35 U.S.C. 371 of International Patent Application No. PCT/EP2018/077684, which claims priority to U.S. Provisional Patent Application No. 62/572,390, filed Oct. 13, 2017, the application of which is incorporated herein by reference in its entirety. 
    
    
     FIELD 
     Embodiments relate to systems and methods for estimating an orientation of a traffic participant. 
     SUMMARY 
     Many modern vehicles require a means for not only detecting other vehicles in the general vicinity, but also determining where other vehicles may be navigating to. For instance, an autonomous or partially autonomous vehicle (sometimes referred to as a “host vehicle” may desire to track the heading or in some case the orientation of a separate vehicle (sometimes referred to as a “target vehicle”) in order to determine if a collision will occur. To track the orientation of the separate vehicle, the autonomous vehicle utilizes a sensor to determine a distance between the two vehicles, estimate an orientation of the separate vehicle, and estimate a rate of change of the orientation of the separate vehicle. 
     Embodiments described herein relate to systems and methods for estimating an orientation of a separate vehicle and, more broadly, a traffic participant (which could include a separate vehicle, objects, and animals that may exist in traffic). 
     One embodiment provides a system for estimating an orientation of a traffic participant. The system comprises a sensor configured to detect a traffic participant; and an electronic controller configured to receive a signal from the sensor, compare a location of the vehicle to a map of expected orientation of traffic participants to estimate an orientation of the traffic participant, perform a calculation based upon the signal from the sensor to estimate an orientation of the traffic participant if an expected orientation is not determined by the comparison of the location of the vehicle to the map of expected orientation of traffic participants, and generate a notification based upon the estimated orientation of the traffic participant. 
     In another embodiment, a method for estimating an orientation of a traffic participant is described. The method comprises generating, with a sensor, a signal, receiving, with an electronic controller, the generated signal, comparing, with the electronic controller, a location of a vehicle to a map of expected orientation of traffic participants to estimate an orientation of the traffic participant, performing, with the electronic controller, a calculation based upon the generated signal from the sensor to estimate an orientation of the traffic participant if an expected orientation is not determined by the comparison of the location of the vehicle to the map of expected orientation of traffic participants, and generating, with the electronic controller, a notification based upon the estimated orientation of the traffic participant. 
     Other aspects, features, and embodiments will become apparent by consideration of the detailed description and accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram of a vehicle with a system for estimating an orientation of a traffic participant according to one embodiment. 
         FIG. 2  is an illustrative example of an electronic controller according to one embodiment. 
         FIG. 3  is a flowchart illustrating a method of estimating an orientation of a traffic participant according to one embodiment. 
         FIG. 4  illustrates a detailed map according to one embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     Before any embodiments are explained in detail, it is to be understood that this disclosure is not intended to be limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the following drawings. Embodiments are capable of other configurations and of being practiced or of being carried out in various ways. 
     A plurality of hardware and software based devices, as well as a plurality of different structural components may be used to implement various embodiments. In addition, embodiments may include hardware, software, and electronic components or modules that, for purposes of discussion, may be illustrated and described as if the majority of the components were implemented solely in hardware. However, one of ordinary skill in the art, and based on a reading of this detailed description, would recognize that, in at least one embodiment, the electronic based aspects of the invention may be implemented in software (for example, stored on non-transitory computer-readable medium) executable by one or more processors. For example, “control units” and “controllers” described in the specification can include one or more electronic processors, one or more memory modules including non-transitory computer-readable medium, one or more input/output interfaces, one or more application specific integrated circuits (ASICs), and various connections (for example, a system bus) connecting the various components. 
     For the purposes of this document, the term “traffic participant” is a vehicle, a pedestrian or other entity that actively interacts with a flow of traffic. For example, a vehicle driving in an adjacent lane is a traffic participant. In another example, a bicycle is a traffic participant. 
     For the purposes of this document, the term “orientation” is a direction of travel of a traffic participant. The orientation may include an angle of travel of the traffic participant, a heading or bearing, and the like. The orientation of the traffic participant may be in relation to a vehicle equipped with a system as described below. 
       FIG. 1  illustrates an example of a vehicle  100  equipped with a system  105  for estimating an orientation of a traffic participant according to one embodiment. The system  105  comprises a sensor  110  and an electronic controller  115  and has one or more wheels  120 - 123 . 
     The sensor  110  is configured to detect a traffic participant (such as traffic participant  130 ). In one embodiment, the sensor  110  is a laser sensor, such as a Lidar sensor. The sensor  110  is configured to detect a distance  135  to the traffic participant, an angle  140  of the traffic participant relative to the vehicle  100 , a number of returns from the traffic participant to the sensor  110 , and the like. The sensor  110  is, in one embodiment, an array of sensors positioned at various points on the vehicle  100  in order to detect a plurality of traffic participants in all directions around the vehicle  100 . 
     The electronic controller  115  is communicatively coupled to the sensor  110  via various wired or wireless connections. In one embodiment, the electronic controller  115  is connected to the sensor  110  via a dedicated wire connection. In other embodiments, the electronic controller  115  is communicatively coupled to the sensor  110  via a shared communication link such as a vehicle communication bus (for example, a controller area network (CAN) bus) or a wireless vehicle network. 
     In some embodiments, the system  105  also includes a notification indicator  120 . The notification indicator  120  is configured to provide a notification to an operator of the vehicle  100 . In one embodiment, the notification indicator  120  is a display screen that displays the notification for the operator of the vehicle  100 . In one example, the notification includes the location, estimated orientation, and other information of a traffic participant. The notification may also include information for more than one traffic participant. The notification indicator  120  is communicatively coupled to the electronic controller  115  in similar ways to the sensor  110  as discussed above. 
     In some embodiments, the system  105  also includes a wireless transceiver  125 . The electronic controller  115  is configured to send and receive data with a remote location or a remote device via the wireless transceiver  125 . The wireless transceiver  125  is communicatively coupled to the electronic controller  115  in similar ways to other components discussed above. 
       FIG. 2  is an illustrative example of an electronic controller, such as the electronic controller  115 . The electronic controller  115  includes a plurality of electrical and electronic components that provide power, operation control, and protection to the components and modules within the electronic controller  115 . In the example illustrated, the electronic controller  115  includes an electronic processor  205  (such as a programmable electronic microprocessor, microcontroller, or similar device), a memory  210  (for example, non-transitory, machine-readable memory), and an input-output interface  215 . The electronic processor  205  is communicatively connected to the memory  210  and the input-output interface  215 . The electronic processor  205 , in coordination with the memory  210  and the input-output interface  215 , is configured to implement, among other things, the methods described herein. 
     The memory  210  stores a detailed map  220 . The detailed map  220  includes information about the direction of travel that other traffic participants other than the vehicle  100  would be expected to be present in. An embodiment of the detailed map  220  is discussed below. 
     The electronic controller  115 , in some embodiments, may be implemented in several independent controllers (for example, programmable electronic control units) each configured to perform specific functions or sub-functions. Additionally, the electronic controller  115  may contain sub-modules that include additional electronic processors, memory, or application-specific integrated circuits (ASICs) for handling input-output functions, processing of signals, and application of the methods listed below. In other embodiments, the electronic controller  115  includes additional, fewer, or different components. 
       FIG. 3  is a flowchart illustrating a method  300  of estimating an orientation of a traffic participant according to one embodiment. 
     The sensor  110  generates a signal (at block  305 ). In one embodiment, the sensor  110  detects a traffic participant along with information about the traffic participant, such as a distance from the vehicle  100  to the traffic participant, an angle of the traffic participant relative to the vehicle  100 , and the like. In other embodiments, the sensor  110  is also configured to detect a number of returns that the sensor  110  receives from a traffic participant. For example, the sensor  110  may detect a traffic participant multiple times and determine an angle of the traffic participant relative to the vehicle  100 , a distance from the vehicle  100 , and the like based upon an average of the multiple detections. The sensor  110  then generates the signal in response to detecting the traffic participant. The signal, in some embodiments, includes the information (such as the distance from the vehicle  100  to the traffic participant) detected by the sensor  110 . 
     The sensor  110  then sends the generated signal to the electronic controller  115 , which receives the generated signal (at block  310 ). The electronic controller  115  receives the generated signal through the input-output interface  215 . In some embodiments, the information contained within the generated signal is stored in the memory  210  before being processed by the electronic processor  205  of the electronic controller  115 . 
     The electronic controller  115  compares a location of the vehicle  100  with the detailed map  220  of expected orientation of traffic participants to estimate an orientation of the traffic participant (at block  315 ). An example of the detailed map  220  is illustrated in  FIG. 4 . 
     The detailed map  220  contains information about how many lanes (such as lanes  400  and  405 ) are on a particular road  410  and also indicates the expected direction of travel of traffic participants located in the lanes  400  and  405  (such as an expected direction of travel for a one way lane, an expected direction of travel for a particular lane of a multi-lane highway, and the like). For example, arrow  412  illustrates an expected direction of travel for traffic at the location of the arrow  412 . 
     In some embodiments, the detailed map  220  is partially or wholly stored on a remote memory and not the memory  210 . In these embodiments, the electronic processor  205  is configured to communicate with the remote memory to access the detailed map  220 . It is to be understood that  FIG. 4  illustrates a graphical representation of information that is stored in the memory  220 . 
     Using the detailed map  220 , the electronic controller  115  uses the signal sent from the sensor  110  to determine where a traffic participant is located on the detailed map  220  and, based upon the location of the traffic participant, estimate the orientation of the traffic participant using the expected direction of travel of the traffic participant, a determined lane the traffic participant is in, and the like. For example, in the detailed map  220 , a vehicle detected at location  415  would have an expected orientation of 180° with respect to the vehicle  100 , whereas a vehicle detected at location  420  would have an expected orientation of 30° with respect to the vehicle  100 . As shown in  FIG. 4 , different locations (represented by arrows such as locations  412 ,  415 , and  420 ) have different expected directions of travel (for example, at location  420 , a different expected direction of travel is expected for a vehicle going around a curve than a vehicle traveling on a straight portion of the road  410 , such as at location  412 ). 
     The vehicle  100  must also be able to be accurately located on the detailed map  220  (for example, at location  430 ). In some embodiments, the electronic controller  115  receives a location of the vehicle  100  via the wireless transceiver  125 . The electronic controller  115  is configured to use the location of the vehicle  100  in order to determine the location of the traffic participant based upon a distance received by the sensor and an angle received by the sensor. The electronic controller  115  then uses this location to estimate an orientation of the traffic participant based upon the expected direction of travel for the determined location on the detailed map  220 . 
     There are some scenarios in which the detailed map  220  will not be able to give an accurate estimation of orientation for a traffic participant. For example, in some circumstances, the traffic participant or the vehicle  100  is in a location that does not have an expected direction of travel, such as a parking lot or other large, open space. In another circumstance, the traffic participant or the vehicle  100  is at a location that has multiple expected directions of travel, such as an intersection, a roundabout, and the like. 
     If the detailed map  220  cannot give an accurate estimation of orientation, or if the detailed map  220  is not available, the electronic controller  115  performs a calculation based upon the signal from the sensor to estimate an orientation of the traffic participant (at block  320 ). The calculation is performed by the electronic processor  205  of the electronic controller  115 . 
     The calculation, in some embodiments, assumes that all traffic is driving parallel to the vehicle  100 . Using this assumption, the expected orientation of the traffic participant can be calculated. A second assumption is made in assuming the motion of the traffic participant is parabolic, and can be modeled as follows:
 
 y= 0.5* K*x   2   (Equation 1)
 
     where K is a road curvature (K) derived from 1/radius, x is a longitudinal distance, and y is as lateral distance. 
     The change in lateral distance over the change in longitudinal distance is equivalent to the tangent of the orientation angle, as shown in Equation 2, below. 
     
       
         
           
             
               
                 
                   
                     dy 
                     dx 
                   
                   = 
                   
                     
                       K 
                       * 
                       x 
                     
                     = 
                     
                       tan 
                       ⁡ 
                       
                         ( 
                         orientation 
                         ) 
                       
                     
                   
                 
               
               
                 
                   ( 
                   
                     Equation 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     2 
                   
                   ) 
                 
               
             
           
         
       
     
     The orientation can then be derived as follows:
 
orientation= a  tan( K*x )  (Equation 3)
 
     In this way, an orientation of the traffic participant can be determined. The electronic controller  115  is configured in some embodiments to store the orientation in the memory  210  and associate the orientation with the traffic participant. The electronic controller  115 , in also embodiments, is able to store a plurality of traffic participants with associated orientations in the memory  210 . 
     The calculation may also include determining a change in orientation over time, referred to hereinafter as a yaw rate. For example, the signal received from the sensor  110  may include a plurality of different times where the sensor  110  detected the traffic participant, each time including a different distance value. The electronic controller  115  is configured to perform Equation 3 for each received distance and determine a total change in orientation over a total change in time, as exemplified by Equation 4 below.
 
yaw rate= d (orientation)/ dt   (Equation 4)
 
     The yaw rate may be associated with the traffic participant and stored in the memory  210  in some embodiments. The calculation that utilizes the detailed map  220  and the assumptions as stated above is hereinafter referred to as a yaw-constrained model. 
     In other embodiments, the electronic controller  115  is configured to track the traffic participant by performing the calculation multiple times and associating the result with the same traffic participant every time. The electronic controller  115  may associate an identity with each traffic participant being tracked in order to differentiate between different target traffic participants detected by the sensor  110 . 
     In other embodiments, the calculation also includes utilizing a non-yaw-constrained model of determining orientation. In these embodiments, the non-yaw-constrained model makes up for the deficiencies of the yaw-constrained model. For example, in some embodiments, a traffic participant may be changing lanes, which utilizing the yaw-constrained model may give an inaccurate estimation of orientation of the traffic participant. Therefore, non-yaw-constrained models of tracking orientation may be utilized in conjunction with the yaw-constrained models to track the traffic participants. 
     The non-yaw-constrained models suffer their own deficiencies. For example, non-yaw-constrained models see high variance and inaccuracy in poor measuring conditions, such as poor angular accuracy of the sensor  110  in relation to the traffic participant, large distances between the sensor  110  and the traffic participant, too few returns of the signal of the sensor  110 , and the like. 
     Some embodiments, therefore, combine a yaw-constrained model and a non-yaw-constrained model. To combine the yaw-constrained model and a non-yaw-constrained model, the electronic controller  115  is configured to determine what a probability that the yaw-constrained model is within an uncertainty of the non-yaw-constrained model is. Mathematically, and assuming a Gaussian distribution, the probability that the yaw-constrained model is within an uncertainty of the non-yaw-constrained model is: 
     
       
         
           
             
                 
             
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               Likelihood 
               ⁢ 
               
                   
               
               ⁢ 
               of 
               ⁢ 
               
                   
               
               ⁢ 
               yaw 
               ⁢ 
               
                   
               
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               constrained 
             
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                 ⋀ 
               
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                                 ⁢ 
                                 
                                     
                                 
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                                 ⁢ 
                                 
                                     
                                 
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                     2 
                   
                   
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     Because orientation is an angle, the difference needs to be the shortest path around a circle and needs to handle the −pi to pi boundary correctly. The likelihood_yaw constrained value gives the contribution factor of the yaw-constrained model. The non-yaw-constrained model&#39;s contribution factor would be 1−likelihood_yaw constrained. Other likelihood functions could also be utilized here. 
     An effect of mixing the yaw-constrained model and the non-yaw-constrained model is two-fold. First, if the non-yaw-constrained model has poor orientation estimation, then it would have a large variance. Even if the non-yaw-constrained estimation deviates considerably from the yaw-constrained model, the yaw-constrained model would be dominant and correct these tracks. Second, if the non-yaw-constrained model has good orientation estimation, it would have a small variance. If the non-yaw-constrained estimation deviates considerably, then the yaw-constrained model would essentially not contribute to the estimation. Therefore, for well-measured traffic participants, there is no dependency to follow the expected direction from the detailed map  220  or from the assumptions of all traffic participants driving parallel to the vehicle  100 . 
     The electronic controller  115 , after determining an estimated orientation of the traffic participant, generates a notification using the estimated orientation (at block  325 ). In some embodiments, the electronic controller  115  generates an identifier for the traffic participant and generates a notification using both the identifier and the estimated orientation of the traffic participant. In other embodiments, the notification may also include a position on the detailed map  220 . 
     In some embodiments, the electronic controller  115  is also configured to send the notification to the notification indicator  120 . For example, the notification indicator  120  may be a display screen, which then displays the detailed map  220  with a location of the vehicle  100  and a location and estimated orientation of the traffic participant displayed in real-time on the detailed map  220 . 
     In some embodiments, the electronic controller  115  is configured to send the notification to a remote location or device via the wireless transceiver  125 . For example, the vehicle  100  may be an autonomous vehicle. In this case, the vehicle  100  may be remotely monitored. The electronic controller  115  therefore creates the notification and sends the notification via the wireless transceiver  125  to the remote location so that a computer system or human operator can monitor the vehicle  100  and the traffic participants tracked by the system  105 . 
     Various features, advantages, and embodiments are set forth in the following claims.