Patent Publication Number: US-2021183240-A1

Title: Smart intersection with criticality determination

Description:
BACKGROUND 
     In an ideal world, traffic participants would always follow the law. However, in the real world, and especially in urban environments, frequently pedestrians jaywalk and vehicles run red lights. This can increase their likelihood of being involved in a collision. 
     The advent of smart vehicles and smart phones has made it possible to digitize communication with traffic participants and further attempt to avoid these accidents. Predictive movement data and accident prevention warnings can be communicated to a smart vehicle&#39;s processor or to a pedestrian&#39;s smartphone. However, traffic congestion and bandwidth issues may make it impracticable to communicate on behalf of or with each traffic participant individually. In such cases, it is desirable to determine the criticality of each traffic participant and prioritize communication based on those who are most at risk. 
     SUMMARY 
     A method of communicating with traffic participants according to an example of this disclosure includes storing data of traffic participant tendencies at an intersection. The method further includes sensing real-time movement characteristics of all traffic participants in the proximity of the intersection. The method further includes determining that it is impracticable to communicate all movement data with all traffic participants in the proximity of the intersection and then calculating a criticality level of one or more traffic participants in the proximity of the intersection based on their movement characteristics and the traffic participant tendencies at the intersection. The method further includes developing a limited communication strategy for the one or more traffic participants based on their criticality level; and then communicating accident prevention information to one or more of the traffic participants according to the limited communication strategy through a communication means. 
     In a further example of the foregoing, the limited communication strategy includes communicating accident prevention information for traffic participants with higher criticality to all traffic participants in the proximity of the intersection. 
     In a further example of any of the foregoing, the limited communication strategy includes only communicating accident prevention information for a traffic participant if their criticality level is above a predetermined level. 
     In a further example of any of the foregoing, the limited communication strategy includes communicating accident prevention information for as many traffic participants as possible up to a bandwidth limit of the communication means, prioritizing traffic participants with a higher criticality. 
     In a further example of any of the foregoing, the limited communication strategy includes only communicating accident prevention information to traffic participants with a higher criticality level. 
     In a further example of any of the foregoing, it is impracticable to communicate all movement data with all traffic participants if either there are more traffic participants than a predetermined limit in the proximity of the intersection or if communicating all movement data to all traffic participants would exceed a bandwidth limit of the communication means. 
     In a further example of any of the foregoing, wherein the movement characteristics of the one or more traffic participants includes their speed, acceleration, location, and relative movement direction. 
     In a further example of any of the foregoing, the method further includes grouping traffic participant movement outcomes with their movement characteristics as they approach the intersection in conjunction with current traffic signals of the intersection and the time of day to determine traffic participant tendencies at the intersection, prior to the storing data step. 
     In a further example of any of the foregoing, traffic participant tendencies includes the tendencies of traffic participants to ignore traffic signals. 
     In a further example of any of the foregoing, traffic participant tendencies includes the tendencies of pedestrians to jaywalk at certain hours of the day. 
     In a further example of any of the foregoing, traffic participant tendencies include the tendencies of vehicles to cross through an intersection with a given traffic light phase signal at certain hours of the day. 
     In a further example of any of the foregoing, the one or more traffic participants includes all traffic participants in the proximity of an intersection. 
     In a further example of any of the foregoing, the accident prevention information is at least one of real-time movement characteristics of traffic participants, predicted movement outcomes of traffic participants, details of potential accidents, and warning messages. 
     A system according to an example of this disclosure includes one or more sensors detecting the movement characteristics of one or more traffic participants in the proximity of an intersection. The sensors communicate data to a control. A communication means is also in communication with the control. The control stores data of movement outcome tendencies for traffic participants at the intersection and predicts probabilistic movement outcomes for each of the one or more traffic participants by a comparison to the data of movement outcome tendencies. Further, the control calculates a criticality level for each of the one or more traffic participants by comparing their probabilistic movement outcomes with one another and instructs the communication means to communicate accident prevention information to the one or more traffic participants based on the criticality level of the one or more traffic participants. 
     In a further example of the foregoing, the control learns movement outcome tendencies by grouping traffic participant movement outcomes with their movement characteristics as they approach the intersection in conjunction with current traffic signals of the intersection and the time of day. 
     In a further example of any of the foregoing, the control incorporates a machine-learning component to learn movement outcome tendencies. 
     In a further example of any of the foregoing, movement characteristics includes traffic participant&#39;s speed, acceleration, location, and relative movement direction, and movement outcome tendencies include the probability that a traffic participant will ignore a traffic signal at the intersection. 
     In a further example of any of the foregoing, the accident prevention information is at least one of real-time movement characteristics of traffic participants, predicted movement outcomes of traffic participants, details of potential accidents, and warning messages. 
     In a further example of any of the foregoing, the communication means comprises a data transceiver broadcasting to a traffic participants cell phone or to a smart vehicle processor. 
     In a further example of any of the foregoing, the communication means comprises at least one of a visual display and an audible speaker system. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates an intersection with a smart infrastructure component incorporating criticality factoring. 
         FIG. 2  illustrates a criticality-factoring algorithm of a smart infrastructure component. 
         FIG. 3  illustrates a method of communicating with traffic participants incorporating criticality factoring. 
     
    
    
     DETAILED DESCRIPTION 
       FIG. 1  illustrates an intersection  10  incorporating a smart infrastructure component  12 .  FIG. 1  illustrates a typical four-way vehicle intersection wherein two perpendicular roads intersect and the ingress and egress of vehicles through the intersection is regulated by phased traffic lights  14  (green, yellow, red). The illustrated intersection further includes crosswalks and crosswalk signals  16  (walk, don&#39;t walk), which regulate the movement of sidewalk pedestrians. Throughout this application, the term “traffic participant”  18  includes vehicles, pedestrians, and bicyclists. 
     At a well-programmed traffic intersection, such as intersection  10 , while a traffic phase  14  or signal  16  instructs traffic participants  18  to proceed in a first direction A, traffic participants moving in a second perpendicular direction B are instructed to stop. If all traffic participants follow these signals then “T-bone” and pedestrian crossing collisions at intersections will be avoided. However, frequently pedestrians jaywalk and vehicles run red lights. Accordingly, smart infrastructure component  12  determines the probability of traffic participants  18  ignoring traffic laws and communicates warnings for traffic participants  18  presented with a collision risk created by that conduct. 
     Smart infrastructure component  12  includes sensors  20  capable of obtaining the real-time movement characteristics of all traffic participants in the proximity of intersection. The movement characteristics of a traffic participant include their speed, acceleration, location, and relative movement direction. In one embodiment, the relevant proximity of the intersection is defined as within a fifty foot radius of the intersection. In other embodiments, it is defined as far out as within a one-hundred or two-hundred foot radius of the intersection depending on the field of view of the sensors. The sensors  20  are also capable of detecting and identifying the occurrence of an adverse traffic event, such as a collision. The sensors  20  may consist of radars, LiDARs, ultrasonic, vision based sensors, or any other appropriate sensor. 
     The smart infrastructure component  12  is preferably attached to a static structure  22  of the intersection  10 . For example, smart infrastructure component may be mounted on a structure supporting traffic lights  14  or crosswalk signals  16 , as illustrated in  FIG. 1 . Alternatively, smart infrastructure component  12  may be mounted on a stand-alone structure. 
     The smart infrastructure component further includes a controller  24 . Controller  24  includes a data module  26  in communication with a bandwidth detection module  28 , a signal, phase and timing (or “SPAT”) module  30 , a machine-learning module  32 , and a comparison module  34 . The data module  26  is in communication with the sensors  20  to access data or information relating to the real-time movement characteristics of traffic participants  18  in proximity of the intersection  10 . The bandwidth detection module  28  determines if it is practicable to communicate all traffic data to all traffic participant  18  in the proximity of the intersection  10 . The SPAT module  30  determines the current phase (green, yellow, red) of traffic lights  14 , the signal (walk, don&#39;t walk) of the pedestrian crossing signal  16 , as well as the time of day. The machine-learning module  32  learns the tendencies of traffic participants  18  at the intersection  10 . The comparison module  34  compares the real-time movement characteristics of traffic participants  18  (communicated by the data module  26 ) to corresponding tendencies (communicated by the machine-learning module  32 ) to predict future movement of traffic participants  18  and determine their criticality. As illustrated in  FIG. 1 , the control  24  may be positioned locally as part of smart infrastructure component  12  and be specific to intersection  10 . In another embodiment, control  24  may be located remotely at a centralized hub communicating and controlling multiple smart infrastructure components at multiple intersections. 
     The smart infrastructure component further includes a communication means  36  instructed by the controller  24  to communicate specified information with traffic participants  18  when appropriate. Communication means  36  is preferably a data transmitter and broadcasts to either a pedestrian&#39;s phone or a smart vehicles processor through one of Wi-Fi, Bluetooth, cellular, DSRC, or any other appropriate data communication method. Alternatively, communication means  36  may communicate with traffic participants through a visual display  36 ′ or an audible speaker system  36 ″ located at intersection  10 . 
     In one embodiment, communication means  36  communicates accident prevention information in the form of at least one of real-time movement characteristics, predictive movement outcomes, and potential accidents to the processor of smart vehicles in the proximity of the intersection. Smart vehicles with autonomous capabilities may be able to use this data to avoid accidents without driver intervention. For example, a smart vehicle may slow down, stop, or perform an evasive maneuver to avoid a collision course predicted by the smart infrastructure component  12  or by on-board computation of the vehicles processor. In other embodiments, the communication means  36  simply provides a warning to the traffic participant  18  predicted to be involved in an adverse traffic event with or without details. 
     In one example, the smart infrastructure component  12  first operates in a learning mode prior to any communications with traffic participants  18 . In this mode, the machine-learning module  32  determines a traffic pattern or tendency of traffic participants  18  at the intersection  10  by grouping traffic participant  18  movement outcomes with movement data obtained from the data module  26  and information provided by the SPAT module  30  over a period of time. In other words, the machine-learning module  32  learns the probability that a traffic participant  18  will continue through the intersection  10  (movement outcome) given their speed, acceleration, location, and relative direction as they approach the intersection (data module  26 ), in conjunction with the current traffic light phase  14 , crosswalk signal  16 , and time of day (SPAT module  30 ). 
     For example, the machine-learning module  32  may learn that a pedestrian approaching intersection  10  at a jog in direction A is likely to ignore the crosswalk “don&#39;t walk” signal  16  from the hours of 11 a.m. to noon, but is likely to obey from 5 p.m. to 6 p.m. As another example, the machine-learning module  32  may learn that a vehicle heading in direction B and accelerating towards a red light during rush hour is likely to ignore the traffic light  14  and continue through the intersection  10 . 
     Machine-learning module  32  may comprise a neural network or any other appropriate known machine-learning algorithm. Preferably, the machine-learning module  32  learns under an unsupervised learning technique as described above; making groupings out of the information provided by the data module  26  and SPAT module  30 . However, the machine-learning module  32  may also learn under a supervised learning technique wherein movement outcome probabilities are manually observed and fed into the machine-learning module  32  as data sets. 
     When the machine-learning module  32  is capable of predicting the movement outcomes of traffic participants  18  to an acceptable degree of accuracy, then smart infrastructure component  12  may be set to operate in a warning mode. In this mode, the comparison module compares real-time movement data of all traffic participants  18  in the proximity of the intersection (communicated by the data module  26 ) with the predicted tendencies of individual traffic participants  18  (communicated by the machine-learning module  32 ). In this manner, the comparison module  34  analyzes the current location and probabilistic movement of all traffic participants  18  in the proximity of an intersection  10  and determines the likelihood of any number of those traffic participants  18  colliding with one another. For example, if a vehicle is approaching a green light in direction A, a pedestrian is approaching the intersection in direction B, and the machine-learning module  32  indicates that previously pedestrians with similar movement characteristics have frequently jaywalked at the present time of day, then the comparison module  34  will determine there is a risk of collision. 
     In a preferred embodiment, the machine-learning module  32  continues to refine the accuracy of its movement outcome predictions while operating in the warning mode. As the smart infrastructure component  12  operates and receives more and more traffic participant data, the machine-learning module  32  can continue to pair movement outcomes with information from the data module  26  and SPAT module  30 , and continuously improve the accuracy of predictions. 
     Ideally, when any risk is present, the controller  24  would instruct communication means  36  to relay all traffic information to all relevant traffic participants  18  in the proximity of intersection  10 . However, in many cases the risk of collision for a certain traffic participant  18  is miniscule. Moreover, there are situations in which the bandwidth detection module  28  may determine that communicating or broadcasting all traffic data to all traffic participants  18  is impracticable, such as if there are more traffic participants  18  in the proximity of the intersection than a predetermined limit or if communications to all traffic participants  18  would exceed a bandwidth limit of the communication means  36 . Accordingly, in such situations, the comparison module  34  performs a criticality determination for all traffic participants and determines a limited communication strategy based on the criticality levels. 
     In one embodiment, the limited communication strategy includes communicating the movement data or a warning message on behalf of traffic participants  18  with higher criticality levels to all traffic participants  18  in the proximity of the intersection  10 , and not communicating on behalf of traffic participants  18  with lower criticality levels. In other words, under this limited communication strategy, the communication means  36  means delivers a reduced list of more relevant data to accident avoidance to all traffic participants  18  when bandwidth limitations make it impossible or impractical to communicate or broadcast on behalf of all traffic participants. 
     In another embodiment, the limited communication strategy includes only communicating the movement data of other traffic participants  18  or a warning message to a subset of traffic participants  18  with higher criticality levels. In this manner, the communication means  36  provides a complete list of data to a reduced number of traffic participants  18  in the proximity of the intersection  10 . 
     Moreover, the limited communication strategy may fall somewhere in between the two foregoing strategies. The communication means  36  may communicate a reduced list of the most relevant traffic data to only a subset of traffic participants  18  based on the criticality determination. 
       FIG. 2  illustrates a simplified algorithm  100  performed by the controller  24  while operating in the warning mode. At step  101  the bandwidth detection module  28  of the controller  24  determines if it is practicable to communicate all traffic data with all traffic participants  18  in the proximity of intersection  10 . If it is, then the controller  24  will instruct the communication means  36  to communicate with each traffic participant  18  (or simply broadcast) at step  114 . If not, then the criticality determination for each individual traffic participant  18  is initiated at step  102 . 
     At step  104  the data module  26  and SPAT module  30  work in conjunction to determine if a particular traffic participant  18  is approaching a phase or signal  14 ,  16  instructing them to go or stop. At step  106  the data module  26  determines if there is a traffic participant  18  approaching from a perpendicular direction with movement characteristics indicating a potential collision. At step  108  the comparison module  34  compares the movement characteristics of the traffic participant  18  from the data module  26  to the traffic tendencies learned by the machine-learning module  32  and determines the probability that either the subject traffic participant  18  or a perpendicular traffic participant  18  will ignore a traffic stop  14 ,  16 . If there is simply no perpendicular traffic participant  18  at the intersection or no traffic participants  18  are likely to ignore a traffic stop  14 ,  16 , then the criticality of the subject traffic participant  18  is determined to be lower at step  110 . Conversely, if there is another traffic participant  18  on a collision course with the subject traffic participant  18 , and either traffic participant is likely to ignore a traffic stop  14 ,  16 , then the criticality of the subject traffic participant is determined to be higher at step  112 . If the traffic participants has a higher criticality, at step  114  the controller prioritizes communicating on behalf of or with that traffic participant  18  through communication means  36 . 
     It should be understood, that algorithm  100  is a simplified algorithm intended to be illustrative. The criticality determination does not involve binary choices, but rather involves an evaluation of combined probabilities. The various movement characteristics and SPAT factors each serve to increase or decrease the probability that individual traffic participants will continue through a traffic stop  14 , 16 , creating a spectrum of risk or criticality. The controller  24  may instruct the communication means to only communicate on behalf of or with a traffic participant  18  when a certain criticality level is reached, or it may communicate on behalf of or with as many traffic participants  18  as possible, prioritizing those with a higher criticality level. 
     Further, a criticality determination similar to algorithm  100  is performed for each traffic participant in the proximity of the intersection  10  continuously. A criticality calculation may be performed multiple times on a single traffic participant  18  at multiple stages as they approach and move through intersection  10 . 
       FIG. 3  illustrates a method  200  of collision prevention according to the present invention. Step  202  includes storing data of traffic participant  18  tendencies at an intersection  10 . Step  204  includes sensing real-time movement characteristics of all traffic participants in the proximity of the intersection. Step  206  includes determining that it is impracticable to communicate all movement data with all present traffic participants  18  in the proximity of the intersection  10 . Step  208  includes calculating the criticality of one or more traffic participants  18  in the proximity of the intersection  10  based on their movement characteristics and the stored traffic participant  18  tendencies of the intersection. Step  210  includes developing a limited communication strategy for the one or more traffic participants  18  based on their criticality level. Finally, step  210  includes communicating accident prevention information to one or more of the traffic participants  18  according to the limited communication strategy through a communication means  36 . 
     It should be recognized that the preceding description is exemplary rather than limiting in nature. The invention can be practiced other than exactly as described. While a typical four-way, traffic-light vehicle intersection  10  has been illustrated in  FIG. 1 , and an associated algorithm  100  provided, it should be understood that the invention could be applied to other types of intersections, such as intersections with stop signs, intersections without pedestrian crossings, roundabouts, highway interchanges, T-intersections, or any other type of intersection. A worker would recognized that certain modifications and variations in light of the above teachings will fall within the scope of the appended claims. Accordingly, the claims should be studied to determine the true scope and content of the legal protection given to this disclosure.