Patent Publication Number: US-10783775-B2

Title: Method and system for using intersecting electronic horizons

Description:
This application is a continuation under 37 C.F.R. § 1.53(b) and 35 U.S.C. § 120 of U.S. patent application Ser. No. 15/710,333 filed Sep. 20, 2017, which is a continuation of U.S. patent application Ser. No. 15/097,457 (now U.S. Pat. No. 9,799,216) filed Apr. 13, 2016, which is a continuation of U.S. patent application Ser. No. 14/251,031 (now U.S. Pat. No. 9,330,564) filed Apr. 11, 2014, which is a continuation of U.S. patent application Ser. No. 12/900,780 (now U.S. Pat. No. 8,717,192) filed Oct. 8, 2010, each of which are incorporated herein by reference in their entirety. 
    
    
     FIELD 
     The present invention relates generally to an electronic horizon, and more particularly, relates to intersecting electronic horizons. 
     BACKGROUND 
     Vehicles, such as automobiles, ambulances, military trucks, and semi-tractors, are designed to operate on networks of roads with other vehicles. An increasing number of vehicles are being built with Advanced Driver Assistance Systems (ADAS). The ADAS in each of those vehicles can use digital map data to provide that vehicle with information about the road network on which the vehicle travels. 
     U.S. Pat. No. 6,405,128 describes methods and systems for providing an electronic horizon in an ADAS architecture. The electronic horizon may identify multiple paths leading from a vehicle&#39;s current position. Each path within the electronic horizon may include one or more intersections through which a driver may maneuver the vehicle. A respective probability may be assigned to each path identified for the electronic horizon. Those probabilities may be based on the most-likely maneuvers a driver may take at each intersection identified for the electronic horizon. Determining the most-likely maneuver and lower-probability maneuvers that a driver may take at each intersection of the electronic horizon may be based on a predetermined ranking of all possible maneuvers that may be made at that intersection, taking into account information regarding the road network, such as turn angles, road function classes, traffic signals, and speed limits or dynamic information, such as direction indicators and driving history. 
     Although U.S. Pat. No. 6,405,128 describes many useful features, there exists room for further improvements. The description that follows provides example embodiments of such improvements. 
     SUMMARY 
     In one respect, an example embodiment may take the form of a method comprising: (i) receiving a first set of vehicle data, wherein the first set of vehicle data includes data that is associated with a first vehicle and a given road segment defined for a road network on which the first vehicle can travel, (ii) receiving a second set of vehicle data, wherein the second set of vehicle data includes data that is associated with a second vehicle and the given road segment defined for the road network, wherein the second vehicle can travel on the road network, (iii) using at least a portion of the first set of vehicle data and at least a portion of the second set of vehicle data to determine a first multi-vehicle probability value that indicates a probability that the first vehicle and the second vehicle will arrive at a common position of the given road segment simultaneously, and (iv) taking a responsive measure if the first multi-vehicle probability value exceeds a threshold probability value. 
     In another respect, an example embodiment may be arranged as a computer-readable data storage device comprising: (i) a first set of vehicle data, wherein the first set of vehicle data includes data that is associated with a first vehicle and a given road segment defined for a road network on which the first vehicle can travel, (ii) a second set of vehicle data, wherein the second set of vehicle data includes data that is associated with a second vehicle and the given road segment defined for the road network, wherein the second vehicle can travel on the road network, (iii) computer-readable program instructions executable by a processor to use at least a portion of the first set of vehicle data and at least a portion of the second set of vehicle data to determine one or more multi-vehicle probabilities, wherein each multi-vehicle probability value indicates a probability of whether the first vehicle and the second vehicle will arrive at a common position of the given road segment simultaneously, and (iv) computer-readable program instructions executable by the processor to determine whether any of the multi-vehicle probabilities exceeds a threshold probability and to trigger a responsive measure to be carried out if any of the multi-vehicle probabilities exceeds the threshold probability. 
     In yet another respect, an example embodiment may take the form of a method comprising (i) receiving a first set of vehicle data, wherein the first set of vehicle data includes data that is associated with at least a first vehicle traveling in a platoon of vehicles on a road network, (ii) receiving a second set of vehicle data, wherein the second set of vehicle data includes data that is associated with a second vehicle destined to enter the platoon of vehicles, and (iii) using at least a portion of the first set of vehicle data and at least a portion of the second set of vehicle data to determine an adjustment for at least one vehicle to make in order for the second vehicle to enter the platoon of vehicles. 
     These as well as other aspects and advantages will become apparent to those of ordinary skill in the art by reading the following detailed description, with reference where appropriate to the accompanying drawings. Further, it should be understood that the embodiments described in this overview and elsewhere are intended to be examples only and do not necessarily limit the scope of the invention. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Example embodiments are described herein with reference to the drawings, in which: 
         FIG. 1  illustrates an example road network; 
         FIG. 2  is a block diagram of an example data storage device; 
         FIG. 3  illustrates another example road network; 
         FIG. 4  is a block diagram of example components of an example vehicle; 
         FIG. 5  is a block diagram of example components of an example road network device (RND); and 
         FIG. 6  is a flow chart depicting a set of functions that may be carried out in accordance with an example embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     I. Introduction 
     An advanced driver assistance system (ADAS) operating within a vehicle may use an electronic horizon to continuously provide the vehicle with updated data about paths along roads onto which the vehicle can travel from the vehicle&#39;s current position. The electronic horizon refers to a collection of roads and intersections leading out from the vehicle&#39;s current position, and the potential driving paths of the vehicle from that current position. Each vehicle of a plurality of vehicles can generate a respective electronic horizon and provide that electronic horizon to another vehicle or device. Each of the electronic horizons can then be stored in a data storage device as a respective set of vehicle data. Additional details regarding electronic horizons are described in U.S. Pat. Nos. 6,450,128 and 6,735,515. The entire disclosures of U.S. Pat. Nos. 6,450,128 and 6,735,515 are incorporated by reference herein. 
     This description provides details of various example embodiments. In one respect, the example embodiments pertain to methods and systems for using intersecting electronic horizons for a plurality of vehicles. The example embodiments include embodiments in which electronic horizons (i.e., sets of vehicle data) or at least portions of the electronic horizons from multiple vehicles are combined. If the electronic horizons include time parameters, the electronic horizons may additionally be referred to as “Time Domain Electronic Horizons.” The combination of electronic horizons or vehicle data may be referred to as an “intersecting electronic horizon,” or additionally as an “intersecting time domain electronic horizon” if the combined electronic horizons include time parameters. 
     In order to combine electronic horizons, vehicle-to-vehicle communications may be established between vehicles to distribute electronic horizons between vehicles. A road network device may notify a given vehicle operating within a given area (e.g., a 1 Km radius surrounding the road network device) of the other vehicles within that given area that have the capability to provide an electronic horizon to the given vehicle. Additionally or alternatively, the road network device may operate as intermediary device that communicates electronic horizon data from one vehicle to another vehicle. Furthermore, as vehicles move from the given area to another area through which a road network passes, a respective road network device for the other area may track the vehicles operating in the other area so that vehicles operating in the other area may be notified of the vehicles that can communicate electronic horizons. 
     An intersecting electronic horizon may include and/or be used to determine a multi-vehicle probability value that indicates a probability of whether two or more vehicles will arrive at a common position of a given road network simultaneously. If the multi-vehicle probability value exceeds a threshold probability value, one or more responsive measures can be taken to reduce the probability that those vehicles will arrive at the common position of a given road network simultaneously. Carrying out the responsive measures can have various benefits, such as collision avoidance and the efficient addition of vehicles to a vehicle platoon. 
     II. Example Architecture 
       FIG. 1  illustrates a simplified road network  100  for describing example embodiments in this detailed description. Road network  100  represents a network of roads, in any country or countries, upon which vehicles can travel.  FIG. 1  illustrates two of those vehicles as vehicles  90  and  95 , respectively.  FIG. 1  also illustrates one road network device (RND)  80  that can be strategically placed, for example, in, on, or near a road network, or in orbit as a satellite. Vehicles that travel on a road network, such as vehicles  90  and  95 , and a plurality of RNDs, including RND  80 , can each include a computer-readable data storage device that contains digital map data and/or a map database that defines a road network, such as road network  100 . For purposes of this description, the term digital map data hereinafter refers to digital map data and/or a map database. 
     The digital map data (e.g., digital map data  220 , shown in  FIG. 2 ) can include information about a road network, road geometry, road conditions, and other information. As an example, the digital map data can include data that defines road network  100 , at least in part, as a plurality of nodes and road segments.  FIG. 1  illustrates road segments  20 ,  21 ,  22 ,  23 ,  24 ,  25 ,  26 ,  27  and  28  and nodes  40 ,  41 ,  42 ,  43 ,  44 ,  45 ,  46 ,  47 ,  48  and  49 . Additional details regarding the digital map data are described in U.S. Pat. Nos. 6,405,128 and 6,735,515. 
     The vehicles that operate and/or that are operable on road network  100  may be arranged to communicate with one another and/or with a plurality of RNDs, such as RND  80 . Since the vehicles that operate on road network  100  may be in motion, the inter-vehicle communications, as well as the vehicle-to-RND and the RND-to-vehicle communications, may include wireless communications, such as radio frequency (RF) communications that occur via an air interface. In this regard, RND  80  may operate as a wireless access point so as to allow a vehicle to access vehicle data from one or more other vehicles and/or to provide vehicle data to one or more other vehicles. 
       FIG. 1  illustrates vehicle-to-RND communications  12  and  14 , RND-to-vehicle communications  11  and  13 , and inter-vehicle communications  15  and  16 . Some or all of the vehicle-to-RND communications  12  and  14 , the RND-to-vehicle communications  11  and  13 , and the inter-vehicle communications  15  and  16  may occur directly between the vehicle and the RND or between the vehicles. Alternatively, some or all of the vehicle-to-RND communications  12  and  14 , the RND-to-vehicle communications  11  and  13 , and the inter-vehicle communications  15  and  16  may occur via one or more intermediary devices of a radio access network, such as a base transceiver station or a wireless access point. 
     RND  80  may be arranged in various configurations. As an example, RND  80  may include a road-side unit (RSU) that is positioned at a location near a road network (e.g., near a street). A location near a road network may, for example, include a location within five meters of the road network. Alternatively, the RSU may be positioned on the road network itself. Being positioned on the road network may include being positioned on a light post, a traffic light, or a traffic guard rail, or being positioned within a paved road of the road network. In accordance with this alternative configuration, the RSU may be referred to as an infrastructure device. 
     As another example, RND  80  may include a device that that is not positioned near the road network. In that regard, RND  80  may be positioned on a satellite orbiting Earth, or at a location on Earth but not near the road network (e.g., a location greater than five meters from the road network). 
     RND  80  may include a device that is operable to control traffic signals and display devices that are operable to visually present alerts to users of road network  100 . As an example, RND  80  may control when a traffic signal for one or more directions of traffic changes to a signal that indicates vehicles heading in certain directions should stop at an intersection of two or more roads and simultaneously control when another traffic signal for vehicles heading in other directions should changes to a signal that indicates those latter vehicles may proceed through the intersection of two or more roads. As another example, RND  80  may control display devices positioned along road network  100  so as to present various visual alerts to users of road network  100 , such as alerts that indicate traffic is congested ahead and/or an estimated time to travel to a given position on road network  100 . Additional details regarding RND  80  are described with reference to  FIG. 5 . 
     Next,  FIG. 2  illustrates an example data storage device  200 . Data storage device  200  may include a computer-readable storage medium readable by a processor. The computer-readable storage medium may include volatile and/or non-volatile storage components, such as optical, magnetic, organic, or other memory or disc storage, which can be integrated in whole or in part with the processor. As an example, data storage device  200  may be located at and/or within a vehicle, such as vehicle  90  or  95 . As another example, data storage device  200  may be located at and/or within an RND, such as RND  80 . 
     Data storage device  200  contains a variety of computer-readable data including vehicle data  210 , digital map data  220  (described above), threshold probability data  230 , computer-readable program instructions  240 , multi-vehicle probability data  250 , and platoon data  260 . Details regarding platoon data  260  are described with respect to  FIG. 3 . 
     Vehicle data  210  may include vehicle data (e.g., electronic horizons) for a plurality of vehicles. In that regard, vehicle data may include any data within an electronic horizon. As illustrated in  FIG. 2 , vehicle data  210  includes vehicle data  211 ,  212 ,  213 ,  214 ,  215 , and  216 . Each of those vehicle data may be associated with a respective vehicle. By way of example, and for purposes of this description, vehicle data  211  is associated with vehicle  90 , vehicle data  212  is associated with vehicle  95 , vehicle data  213  is associated with a vehicle  91  (shown in  FIG. 3 ) and vehicle data  214  is associated with a vehicle  92  (shown in  FIG. 3 ). Vehicle data  215  and  216  may be associated with vehicles not shown in the figures. 
     Table 1 includes an example of vehicle data  211 . The vehicle data may include data that identifies when the vehicle data was generated. By way of example, vehicle data  211  was generated at 9 o&#39;clock in the morning on Jan. 1, 2011. Table 1 includes vehicle data for a single road segment (i.e., road segment  28 ) of road network  100 . In that regard, the vehicle data shown in Table 1 includes only a portion of an electronic horizon that can be determined for vehicle  90 . A person having ordinary skill in the art will understand that the vehicle data (i.e., the electronic horizon) for a given vehicle can include vehicle data for multiple segments of road network  100 . That same person will also understand that vehicle data can be generated repeatedly as time passes (i.e., at different times) and as the vehicle travels on the road network. 
     
       
         
           
               
             
               
                 TABLE 1 
               
             
            
               
                   
               
               
                 Example vehicle data (211) 
               
               
                 Vehicle (90) - Road Segment (28) - Start Point: Node (44),  
               
               
                 End Point: Node (49) 
               
               
                 Data Generation: Date: 1 Jan. 2011 
               
               
                 Time: 09:00.00.00 (hours:minutes:seconds:hundredths of seconds) 
               
               
                 Probability of Vehicle (90) traveling on Road Segment (28): 0.6 
               
            
           
           
               
               
               
               
               
            
               
                   
                   
                 Time Parameter  
                 Time Parameter  
                   
               
               
                   
                   
                 for Delta  
                 for Delta  
                 Probability  
               
               
                   
                   
                 Distance  
                 Distance  
                 of traveling  
               
               
                   
                 Vehicle 
                 150 m 
                 200 m 
                 on link 
               
               
                 Speed 
                 Speed 
                 Location:  
                 Location:  
                 at speed  
               
               
                 candidate 
                 Probability 
                 node (44) 
                 node (49) 
                 candidate 
               
               
                   
               
               
                  8 m/s 
                 0.1 
                 18.75 seconds 
                 25.00 seconds 
                 0.06 
               
               
                 10 m/s 
                 0.2 
                 15.00 seconds 
                 20.00 seconds 
                 0.12 
               
               
                 12 m/s 
                 0.4 
                 12.50 seconds 
                 16.67 seconds 
                 0.24 
               
               
                 14 m/s 
                 0.2 
                 10.71 seconds 
                 14.29 seconds 
                 0.12 
               
               
                 16 m/s 
                 0.1 
                  9.38 seconds 
                 12.50 seconds 
                 0.06 
               
               
                   
               
            
           
         
       
     
     Vehicle data  211  includes a probability value that indicates the probability of vehicle  90  traveling on road segment  28  is 0.6 (i.e., 60%). The probability value of vehicle  90  traveling on each road segment of a road network may, for example, be determined by a data engine and/or a data horizon program (e.g., the data engine and/or data horizon program referred to in U.S. Pat. Nos. 6,405,128 and 6,735,515). Those probability values may be based on the potential paths vehicle  90  may travel, including the most-likely path of vehicle  90 . 
     Vehicle data  211  includes multiple speed candidates representative of average speeds that vehicle  90  may travel if it travels on road segment  28 , and multiple vehicle speed probability values that indicate the probability that vehicle  90  will travel at those speeds. The speed candidates may, for example, be based on various factors, such as a speed limit for traveling on the road segment corresponding to the speed candidate, historical speeds traveled by vehicle  90  (e.g., historical speeds traveled on road segments leading towards road segment  28 , on road segment  28 , and/or road segments leading away from road segment  28 ), traffic pattern information for road segment  28  (e.g., congested, not congested), conditions of road segment  28  (e.g., dry, wet, or icy), and a driving style associated with a driver of vehicle  90  (e.g., rarely exceeds speed limit or usually exceeds speed limits by one of a plurality of threshold speeds). A person having ordinary skill in the art will understand that vehicle data could include a different set of speed candidates and those different speed candidates could be in units other than meters per second. 
     Vehicle data  211  includes time parameters for two delta distances (i.e., 150 meters and 200 meters) from a current position of vehicle  90 . A delta distance represents a distance a vehicle would have to travel to reach a given point within road network  100  from the vehicle&#39;s current position. For purposes of this description, the delta distances 150 m and 200 m are associated with node  44  and node  49 , respectively. A person having ordinary skill in the art will understand that the delta distances listed in Table 1 are merely examples and other delta distances may be used. Moreover, the vehicle data may include delta distances for points within a road segment other than a start point or end point of a road segment. 
     The time parameters of vehicle data  211  represent an expected time value that vehicle  90  will arrive at the point associated with the delta distance. For example, if vehicle  90  travels at an average speed of 12 m/s, vehicle  90  will arrive at node  44  in 12.50 seconds (i.e., 150 m divided by 12 m/s). In that regard, vehicle  90  would arrive at node  44  at the time 09:00.12.50 (i.e., 09:00.00.00 plus 12.50 seconds). 
     Vehicle data  211  also includes probability values that indicate a probability that vehicle  90  will travel on road segment  28  at a given speed candidate. For example, vehicle data  211  includes a probability value that represents the probability of vehicle  90  traveling on road segment  28  at an average speed of 12 m/s is 0.24 (i.e., the probability of vehicle  90  traveling on road segment  28  (i.e., 0.6) times the probability of vehicle  90  traveling at an average speed of 12 m/s on road segment  28  (i.e., 0.4)). 
     One or more time parameters associated with a given road segment may be identified as a respective most-probable time (MPT). In Table 1, the time parameters in the row for speed candidate 12 m/s may be identified as MPTs for road segment  28  and in particular, nodes  44  and  49 , respectively, because vehicle data  211  shows that vehicle  90  will most likely travel on road segment  28  at an average speed 12 m/s. Identification of MPTs for the various road segments in an electronic horizon may be used to reduce the amount of data that gets transmitted to an RND and/or to one or more other vehicles if the vehicle only transmits vehicle data associated with the MPT (e.g., the data in one row of Table 1). Alternatively, vehicles may transmit vehicle data in addition to the vehicle data associated with the MPT. 
     Similarly, other vehicles operating on road network  100  with vehicle  90  may reduce the amount of data they transmit to vehicle  90  and/or to an RND by identifying MPTs for those vehicles. In that way, the data storage and processing burden on vehicle  90  and/or the RND may be reduced because vehicle  90  and/or the RND are receiving less vehicle data. Should vehicle  90  and/or the RND determine that it needs more data from a vehicle traveling on road network  100 , vehicle  90  and/or the RND can request that the vehicle transmit additional vehicle data (e.g., vehicle data in addition to that which is associated with an MPT). 
     Next, Table 2 includes an example of vehicle data  212 . Vehicle data  212  includes data that indicates it was generated at the same time vehicle data  211  was generated. However, vehicle data  211  and  212  are not so limited, as vehicle data  211  and  212  may be generated at different times. Table 2 includes vehicle data for a single road segment (i.e., road segment  28 ) of road network  100 . In that regard, the vehicle data shown in Table 2 includes only a portion of an electronic horizon that can be determined for vehicle  95 . 
     
       
         
           
               
             
               
                 TABLE 2 
               
             
            
               
                   
               
               
                 Example vehicle data (212) 
               
               
                 Vehicle (95) - Road Segment (28) - Start Point: Node (44),  
               
               
                 End Point: Node (49) 
               
               
                 Data Generation: Date: 1 Jan. 2011 
               
               
                 Time: 09:00.00.00 (hours:minutes:seconds:hundredths of seconds) 
               
               
                 Probability of Vehicle (95) traveling on Road Segment (28): 0.8 
               
            
           
           
               
               
               
               
               
            
               
                   
                   
                 Time Parameter  
                 Time Parameter  
                   
               
               
                   
                   
                 for Delta  
                 for Delta  
                 Probability  
               
               
                   
                   
                 Distance  
                 Distance  
                 of traveling  
               
               
                   
                 Vehicle 
                 100 m 
                 150 m 
                 on link  
               
               
                 Speed 
                 Speed 
                 Location:  
                 Location:  
                 at speed  
               
               
                 Candidate 
                 Probability 
                 node (44) 
                 node (49) 
                 candidate 
               
               
                   
               
               
                 4 m/s 
                 0.1 
                 25.00 seconds 
                 37.50 seconds 
                 0.08 
               
               
                 6 m/s 
                 0.2 
                 16.67 seconds 
                 25.00 seconds 
                 0.16 
               
               
                 8 m/s 
                 0.4 
                 12.50 seconds 
                 18.75 seconds 
                 0.32 
               
               
                 10 m/s  
                 0.2 
                 10.00 seconds 
                 15.00 seconds 
                 0.16 
               
               
                 12 m/s  
                 0.1 
                  8.33 seconds 
                 12.50 seconds 
                 0.08 
               
               
                   
               
            
           
         
       
     
     Vehicle data  212  includes a probability value that indicates the probability of vehicle  95  traveling on road segment  28  is 0.8 (i.e., 80%). Similar to the probability value of 0.6 in Table 1, the probability value of vehicle  95  traveling on each road segment of a road network may be determined by a data engine and/or a data horizon program. Vehicle data  212  includes multiple speed candidates representative of average speeds that vehicle  95  may travel if it travels on road segment  28 , and multiple vehicle speed probability values representative of the probability that vehicle  95  will travel at those speeds. 
     Vehicle data  212  includes time parameters for two delta distances (i.e. 100 meters and 150 meters) from a current position of vehicle  95 . For purposes of this description, the delta distances 100 m and 150 m are associated with node  44  and node  49 , respectively. A person having ordinary skill in the art will understand that the delta distances listed in Table 2 are merely examples and other delta distances may be used. Moreover, vehicle data  212  may include delta distances for points within a road segment other than a start point or end point of a road segment. 
     The time parameters of vehicle data  212  represent an expected time value that vehicle  95  will arrive at the point associated with the delta distance. For example, if vehicle  95  travels at an average speed of 4 m/s, vehicle  90  will arrive at node  44  in 25.00 seconds (i.e., 100 m divided by 4 m/s). In that regard, vehicle  90  would arrive at node  44  at the time 09:00.25.00 (i.e., 09:00.00.00 plus 25.00 seconds). 
     Vehicle data  212  also includes probability values that indicate a probability that vehicle  95  will travel on road segment  28  at a given speed candidate. For example, vehicle data  212  includes a probability value that indicates the probability of vehicle  95  traveling on road segment  28  at an average speed of 8 m/s is 0.32 (i.e., the probability of vehicle  95  traveling on road segment  28  (i.e., 0.8) times the probability of vehicle  95  traveling at an average speed of 8 m/s on road segment  28  (i.e., 0.4)). 
     The number of vehicles represented by vehicle data within vehicle data  210  may vary from time to time. For example, the number of vehicles represented by vehicle data within vehicle data  210  may vary as the number of vehicles within an area around the vehicle comprising data storage device  200  changes or the number of vehicles within an area around RND  80  comprising data storage device  200  changes. For example, as the number of vehicles around the vehicle or RND  80  increases, the number of vehicles represented by vehicle data within vehicle data  210  may increase as more vehicles provide their vehicle data to the vehicle or RND  80 . As another example, as the number of vehicles around the vehicle or RND  80  decreases, the number of vehicles represented by vehicle data within vehicle data  210  may decrease as fewer vehicles provide their vehicle data to the vehicle or RND  80 . 
     Computer-readable program instructions (CRPI)  240  include various program instructions executable by a processor. In general, CRPI  240  may include program instructions to carry out the functions described in this description, and CRPI  240  may include program instructions arranged as a data horizon program and a data engine that is operable to determine and obtain from the map database the relevant data about road segments lying ahead of or behind a vehicle. More particular examples of CRPI  240  are described below. 
     For example, CRPI  240  may include program instructions that are executable to determine speed candidates and vehicle speed probabilities associated with those speed candidates. Those program instructions may use a variety of information to make the determinations. For instance, the information used to determine speed candidates and vehicle speed probabilities may include digital map data, such as a respective speed limit for driving on each road segment for which the speed candidate and vehicle speed probability are being determined, and data associated with the factors upon which speed candidates may be based. 
     As another example, CRPI  240  may include program instructions that are executable to obtain at least a portion of vehicle data from multiple vehicles and to use the obtained data to determine multi-vehicle probability values. For instance, CRPI  240  may include program instructions executable by a processor to generate multi-vehicle probability values. Each multi-vehicle probability value may indicate a probability that two or more vehicles will arrive at the same place at the same time. Generating the multi-vehicle probability values may include the processor comparing vehicle data  211  and  212 . While comparing vehicle data  211  and  212 , the processor can determine it is possible that vehicles  90  and  95  will simultaneously arrive at node  44  at the time 9:00:12.50 and it is possible that vehicles  90  and  95  will simultaneously arrive at node  49  at the time 9:00.12.50 or 9:00:25.00. 
     The processor can determine a first multi-vehicle probability value by multiplying the probability value that vehicle  90  will travel on road segment  28  at an average speed of 12 m/s so as to arrive at node  44  at the time 9:00:12.50 by the probability value that vehicle  95  will travel on road segment  28  at an average speed of 8 m/s so as to arrive at node  44  at the time 9:00:12.50 (i.e., 0.24 times 0.32). In that regard, the first multi-vehicle probability value of 0.0768 represents the probability that vehicles  90  and  95  will simultaneously arrive at node  44 . 
     The processor can determine a second multi-vehicle probability value by (i) multiplying the probability value that vehicle  90  will travel on road segment  28  at an average speed of 8 m/s so as to arrive at node  49  at the time 9:00:25.00 by the probability value that vehicle  95  will travel on road segment  28  at an average speed of 6 m/s so as to arrive at node  49  at the time 9:00:25.00 (i.e., 0.06 times 0.16), (ii) multiplying the probability value that vehicle  90  will travel on road segment  28  at an average speed of 16 m/s so as to arrive at node  49  at the time 9:00:12.50 by the probability value that vehicle  95  will travel on road segment  28  at an average speed of 12 m/s so as to arrive at node  49  at the time 9:00:12.50 (i.e., 0.06 times 0.08), and (iii) adding the sums of those two products (i.e., (0.06 times 0.16) plus (0.06 times 0.08)). In that regard, the second multi-vehicle probability value of 0.0144 represents the probability that vehicles  90  and  95  will simultaneously arrive at node  49 . 
     As another example, CRPI  240  may include program instructions executable by a processor to compare a multi-vehicle probability value to a threshold probability value contained in threshold probability data  230 . Threshold probability data  230  includes at least one data value for comparing to a multi-vehicle probability value of multi-vehicle probability data  250 . When threshold probability data  230  includes multiple values, those various values may be selected for comparing to a multi-vehicle probability value based on various factors, such as the condition of roads due to an amount of traffic, weather conditions, and time of day. For instance, the selected threshold data value may be relatively higher when the amount of traffic is relatively low (e.g., not congested), when the road conditions are good (e.g., not icy or snowy), and/or during certain daylight hours. Alternatively, the selected threshold value may be relatively lower when the amount of traffic is relatively high (e.g., congested), when the road conditions are poor (e.g., icy or snowy), and/or during night time hours. 
     Next,  FIG. 3  illustrates another simplified road network  300 . Road network  300  may be part of road network  100  or separate from road network  100 . Similar to road network  100 , road network  300  may be defined by digital map data. In that regard, the digital map data may define a plurality of road segments and a plurality of nodes. Those road segments and nodes may include, as illustrated in  FIG. 3 , road segments  29 ,  30 ,  31 ,  32 ,  33 , and  34  and nodes  50 ,  51 ,  52 ,  53 ,  54 ,  55 , and  56 . 
     A vehicle platoon includes a plurality of vehicles whose actions on a road network are coordinated by communications. Those communications may include RF communications that are carried out using an air interface, as described below.  FIG. 3  depicts a vehicle platoon  60  traveling on road network  300 . Platoon  60  includes vehicles  90 ,  91 , and  92 . Vehicles within platoon  60  can leave the platoon. For example, vehicle  92  can leave platoon  60  by turning onto road segment  34  at node  51 , whereas the other vehicles of platoon  60  continue traveling onto segment  30 . Other vehicles can join platoon  60 . For example, vehicle  95  can join platoon  60  by merging into a gap within platoon  60  when that gap occurs at node  52 . 
     The communications carried out to coordinate vehicular action in the platoon can include data storable as platoon data  260 . As an example, platoon data  260  may include data about each vehicle in platoon  60 , as well as data about vehicles that may join the platoon and/or vehicles that were previously in the platoon. The data about each vehicle may identify a vehicle type (e.g., a 2010 model year Chevrolet Camaro, a Freightliner semi-tractor with 53 foot trailer, and a 2010 model year Range Rover Sport), and the dimensions of those vehicle types (e.g., 4.84 m, 19.80 m, and 4.78 m, respectively). As another example, platoon data  260  may include gap data that indicates the size of a gap in front of or behind a vehicle, and a location of the gap or a location of a vehicle associated with the gap.  FIG. 3  illustrates a lead gap  70 , intermediary gaps  71  and  72 , and a trailing gap  73 . 
     Table 3 includes example platoon data  260 . Table 3 includes data regarding vehicle  95  because vehicle  95  is expected to join platoon  60 . The “current position” in the Platoon Member column indicates an order of the vehicles. A vehicle at position (1) is the lead vehicle of a platoon, a vehicle at (final) is the vehicle at the rear of the platoon, and a vehicle at position (none) is not currently in the platoon. The “entry point” in the Joining Platoon column indicates a position (e.g., location) of road network  300  where a vehicle may join the platoon. The “exit point” in the Leaving Platoon column indicates a position of road network  300  where a vehicle may exit the platoon. 
     
       
         
           
               
             
               
                 TABLE 3 
               
             
            
               
                   
               
               
                 Example platoon data (260) 
               
            
           
           
               
               
               
               
               
               
               
            
               
                   
                   
                 Joining 
                 Leaving 
                   
                 Forward 
                   
               
               
                   
                 Platoon Member 
                 Platoon 
                 Platoon 
                 Vehicle 
                 Gap 
                 Rearward 
               
               
                 Vehicle 
                 (Current Position) 
                 (Entry Point) 
                 (Exit Point) 
                 length 
                 (Ref. No) 
                 Gap 
               
               
                   
               
               
                 90 
                 Yes (1) 
                 No 
                 No 
                 5 m 
                 25 m (70) 
                 20 m (71) 
               
               
                 91 
                 Yes (2) 
                 No 
                 No 
                 5 m 
                 20 m (71) 
                 10 m (72) 
               
               
                 92 
                 Yes (Final) 
                 No 
                 Yes 
                 5 m 
                 10 m (72) 
                 15 m (73) 
               
               
                   
                   
                   
                 (Node 51) 
               
               
                 95 
                 No (None) 
                 Yes 
                 No 
                 5 m 
                 N.A. 
                 N.A. 
               
               
                   
                   
                 (Node 52) 
               
               
                   
               
            
           
         
       
     
     The forward gaps and rearward gaps listed in Table 3 can be identified in various ways. For example, the forward gaps and rearward gaps can be determined via the use of vehicle sensors, such as sonar and radar sensors that are operable to provide signals to a processor for detecting a vehicle or another object in front of or behind a vehicle including the sensors. As another example, the forward gaps and rearward gaps can be determined via the use of location information that identifies the location of two vehicles in the platoon and vehicle dimension data of those two vehicles. Other examples of determining the forward gaps and rearward gaps are also possible. The forward and rearward gaps for vehicle  95  are listed as non-applicable (N.A.) because vehicle  95  has not yet joined platoon  60 . 
     Next, Tables 4 and 5 include additional examples of vehicle data  211  and  212 , respectively, and Tables 6 and 7 include examples of vehicle data  213  and  214 , respectively. As an example, the vehicle data  211 ,  212 ,  213 , and  214  and platoon data  260  may be contained in a data storage device (e.g., data storage device  200 ) within vehicle  95 . In accordance with that example, an RF communications interface within vehicle  95  can receive vehicle data  211  from vehicle  90 , vehicle data  213  from vehicle  91 , and vehicle data  214  from vehicle  92 . That RF communications interface can also receive portions of platoon data  260  from each of vehicles  90 ,  91 , and  92 . Additionally or alternatively, vehicle data  211 ,  212 ,  213 , and  214 , and platoon data  260  may be contained in a data storage device within a vehicle of platoon  60  and/or RND  80 . 
     
       
         
           
               
             
               
                 TABLE 4 
               
             
            
               
                   
               
               
                 Example vehicle data (211) 
               
               
                 Vehicles (90) - Road Segment (31) - Start Point: Node (52),  
               
               
                 End Point: Node (53) 
               
               
                 Data Generation: Date: 1 Jan. 2011 
               
               
                 Time: 10:00.00.00 (hours:minutes:seconds:hundredths of seconds) 
               
               
                 Probability of Vehicle (90) traveling on Road Segment (31): 0.9 
               
            
           
           
               
               
               
               
               
            
               
                   
                   
                 Time Parameter  
                 Time Parameter  
                   
               
               
                   
                   
                 for Delta  
                 for Delta  
                 Probability  
               
               
                   
                   
                 Distance 200 m 
                 Distance 300 m 
                 of traveling  
               
               
                   
                 Vehicle 
                 Location:  
                 Location:  
                 on link at 
               
               
                 Speed 
                 Speed 
                 node (52) 
                 node (53) 
                 speed  
               
               
                 Candidate 
                 Prob. 
                 Vehicle (90) 
                 Vehicle (90) 
                 candidate 
               
               
                   
               
               
                  8 m/s 
                 0.1 
                 25.00 seconds 
                 37.50 seconds 
                 0.09 
               
               
                 10 m/s 
                 0.2 
                 20.00 seconds 
                 30.00 seconds 
                 0.18 
               
               
                 12 m/s 
                 0.5 
                 16.67 seconds 
                 25.00 seconds 
                 0.45 
               
               
                 14 m/s 
                 0.2 
                 14.29 seconds 
                 21.43 seconds 
                 0.18 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 5 
               
             
            
               
                   
               
               
                 Example vehicle data (213) 
               
               
                 Vehicles (91) - Road Segment (31) - Start Point: Node (52),  
               
               
                 End Point: Node (53) 
               
               
                 Data Generation: Date: 1 Jan. 2011 
               
               
                 Time: 10:00.00.00 (hours:minutes:seconds:hundredths of seconds) 
               
               
                 Probability of Vehicle (91) traveling on Road Segment (31): 0.9 
               
            
           
           
               
               
               
               
               
            
               
                   
                   
                 Time Parameter  
                 Time Parameter  
                   
               
               
                   
                   
                 for Delta  
                 for Delta  
                 Probability  
               
               
                   
                   
                 Distance 220 m 
                 Distance 320 m 
                 of traveling  
               
               
                   
                 Vehicle 
                 Location:  
                 Location:  
                 on link at 
               
               
                 Speed 
                 Speed 
                 node (52) 
                 node (53) 
                 speed  
               
               
                 Candidate 
                 Prob. 
                 Vehicle (91) 
                 Vehicle (91) 
                 candidate 
               
               
                   
               
               
                  8 m/s 
                 0.1 
                 27.50 seconds 
                 40.00 seconds 
                 0.09 
               
               
                 10 m/s 
                 0.2 
                 22.00 seconds 
                 32.00 seconds 
                 0.18 
               
               
                 12 m/s 
                 0.5 
                 18.33 seconds 
                 26.67 seconds 
                 0.45 
               
               
                 14 m/s 
                 0.2 
                 15.71 seconds 
                 22.86 seconds 
                 0.18 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 6 
               
             
            
               
                   
               
               
                 Example vehicle data (214) 
               
               
                 Vehicles (92) - Road Segment (31) - Start Point: Node (52),  
               
               
                 End Point: Node (53) 
               
               
                 Data Generation: Date: 1 Jan. 2011 
               
               
                 Time: 10:00.00.00 (hours:minutes:seconds:hundredths of seconds) 
               
               
                 Probability of Vehicle (92) traveling on Road Segment (31): 0.1 
               
            
           
           
               
               
               
               
               
            
               
                   
                   
                 Time Parameter  
                 Time Parameter  
                   
               
               
                   
                   
                 for Delta  
                 for Delta  
                 Probability  
               
               
                   
                   
                 Distance 230 m 
                 Distance 330 m 
                 of traveling  
               
               
                   
                 Vehicle 
                 Location:  
                 Location:  
                 on link at 
               
               
                 Speed 
                 Speed 
                 node (52) 
                 node (53) 
                 speed  
               
               
                 Candidate 
                 Prob. 
                 Vehicle (92) 
                 Vehicle (92) 
                 candidate 
               
               
                   
               
               
                  8 m/s 
                 0.1 
                 28.75 seconds 
                 41.25 seconds 
                 0.01 
               
               
                 10 m/s 
                 0.2 
                 23.00 seconds 
                 33.00 seconds 
                 0.02 
               
               
                 12 m/s 
                 0.5 
                 19.17 seconds 
                 27.50 seconds 
                 0.05 
               
               
                 14 m/s 
                 0.2 
                 16.43 seconds 
                 23.57 seconds 
                 0.02 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 7 
               
             
            
               
                   
               
               
                 Example vehicle data (212) 
               
               
                 Vehicles (95) - Road Segment (31) - Start Point: Node (52),  
               
               
                 End Point: Node (53) 
               
               
                 Data Generation: Date: 1 Jan. 2011 
               
               
                 Time: 10:00.00.00 (hours:minutes:seconds:hundredths of seconds) 
               
               
                 Probability of Vehicle (95) traveling on Road Segment (31): 0.9 
               
            
           
           
               
               
               
               
               
            
               
                   
                   
                 Time Parameter  
                 Time Parameter  
                   
               
               
                   
                   
                 for Delta  
                 for Delta  
                 Probability  
               
               
                   
                   
                 Distance 300 m 
                 Distance 400 m 
                 of traveling  
               
               
                   
                 Vehicle 
                 Location:  
                 Location:  
                 on link at 
               
               
                 Speed 
                 Speed 
                 node (52) 
                 node (53) 
                 speed  
               
               
                 Candidate 
                 Prob. 
                 Vehicle (95) 
                 Vehicle (95) 
                 candidate 
               
               
                   
               
               
                  8 m/s 
                 0.2 
                 37.50 seconds 
                 50.00 seconds 
                 0.18 
               
               
                 10 m/s 
                 0.4 
                 30.00 seconds 
                 40.00 seconds 
                 0.36 
               
               
                 12 m/s 
                 0.2 
                 25.00 seconds 
                 33.33 seconds 
                 0.18 
               
               
                 14 m/s 
                 0.1 
                 21.43 seconds 
                 28.58 seconds 
                 0.09 
               
               
                 16 m/s 
                 0.1 
                 18.75 seconds 
                 25.00 seconds 
                 0.09 
               
               
                   
               
            
           
         
       
     
     The vehicle data in Tables 4 through 7 pertain to a single road segment of road network  300 . A person having ordinary skill in the art will understand that the vehicle data for one or more vehicles can include vehicle data for more than one road segment. For example, vehicle data  211 ,  213 , and  214  can include vehicle data for road segments  29 ,  30 , and  34 , as well as for additional road segments beyond those shown in  FIG. 3 . As another example, vehicle data  212  can include vehicle data for road segments  32  and  33 , as well as for additional road segments beyond those shown in  FIG. 3 . 
     For purposes of this description, the time parameters in Tables 1, 2, and 4 through 7 are taken to be the times when a front end of a vehicle reaches a given position (e.g., a node) in the road network. A person having ordinary skill in the art will understand that the time parameters in one or more of those tables could be taken to be the time when a position half way between the front and back of a vehicle reaches the given position, or when some other portion of the vehicle reaches the given position. 
     The vehicle data in Tables 4 through 7 may be combined to determine multi-vehicle probabilities in the same manner that the vehicle data in Tables 1 and 2 are combinable to form multi-vehicle probabilities. 
     The data in Tables 4 through 7 and platoon data  260  can be used to determine additional data regarding a vehicle expected to enter platoon  60 . That additional data can be determined, for example, via a processor at a vehicle and/or a processor at an RND. Data within Tables 4 and 5 indicate that the probability of vehicles  90  and  91  traveling on road segment  31  at an average speed of 12 m/s is 45%. In such an occurrence, the front of vehicle  90  will reach node  52  in 16.67 seconds, the rear end of vehicle  90  will reach node  52  in 17.08 second (i.e., 205 meters divided by 12 m/s) and the front end of vehicle  91  will reach node  52  in 18.33 seconds. With vehicles  90  and  91  traveling at an average speed of 12 m/s, gap  71  will exist at node  52  during the time range of 17.08 seconds and 18.33 seconds after the vehicle data in Tables 4 and 5 were generated. Thus, one possibility for vehicle  95  to merge into platoon  60 , as a vehicle at the second position, is for vehicle  95  to arrive at node  52  when gap  71  exists at node  52 . 
     Referring to Table 7, vehicle  95  is 300 m away from node  52 . In order for vehicle  95  to arrive at node  52  within the time range of 17.08 seconds and 18.33 seconds, a processor can execute program instructions to determine a range of average speeds that vehicle  95  can travel to arrive at node  52  within that time range. The range of average speeds for that time range is 16.37 m/s (i.e., 300 meters divided by 18.33 seconds) to 17.56 m/s (i.e., 300 meters divided by 17.07 seconds). Upon determining that range of average speeds, a responsive measure can be initiated. For example, a visual or audible alert can be presented at vehicle  95  so as to cause a driver of vehicle  95  or a control system within vehicle  95  to alter the speed of vehicle  95  to a speed within the determined range of speeds. 
     Alternatively, a processor may execute program instructions to determine that the probability of vehicle  95  entering platoon  60  while gap  71  exists at node  52  is too low. Such determination may be made by comparing a probability of vehicle  95  traveling on road segment  31  at an average speed of 16.37 to 17.56 m/s or at an average speed closest to that range of speeds to a threshold probability  230 . Referring to Table 7, the probability of vehicle  95  traveling on road segment  31  at a rate of 16 m/s is 9%, whereas the probability of vehicle  95  traveling on road segment  31  at a rate of 10 m/s is 36%. By referring to the data in Table 7, the processor may determine that it is more probable that vehicle  95  could enter platoon  60  when gap  72  exists over node  51  or gap  73  exists of over node  51  if vehicle  92  does not exit platoon  60 . The processor may cause an RF communications interface to transmit messages to other vehicles or RND  80  to provide notice that vehicle  95  should enter platoon  60  at a vehicle position after the second position. 
     Next,  FIG. 4  is a block diagram of example components within vehicle  90 . As illustrated in  FIG. 4 , vehicle  90  may include a processor  410 , a user interface  420 , a radio frequency (RF) communications interface  430 , a position determination device  440 , and data storage device  200 , all of which may be linked together via a system bus, network, or other connection mechanism  450 . A person having ordinary skill in the art will understand that other vehicles, such as vehicles  91 ,  92 , and  95 , may be arranged in a configuration similar to vehicle  90 . 
     Processor  410  may include one or more general purpose processors (e.g., Intel microprocessors) and/or one or more special purpose processors (e.g., digital signal processors). Processor  410  may execute computer-readable program instructions contained in data storage device  200 . 
     User interface  420  may include a device that is operable to present information to a user of vehicle  90 . As an example, user interface  420  may include a display (e.g., a liquid crystal display and/or one or more other displays) to visually present alerts to a user of vehicle  90 . As another example, user interface  420  may include one or more speakers to audibly present alerts to a user of vehicle  90 . Processor  410  may execute program instructions that cause user interface  420  to present the alerts. 
     The alerts presented via user interface  420  may include alerts that are presented as responsive measures if processor  410  determines that a multi-vehicle probability value exceeds a threshold probability value. Additionally or alternatively, the alerts presented via user interface  420  may include alerts to provide notice regarding (i) a vehicle expected to merge into the path of vehicle  90 , (ii) a vehicle entering a platoon comprising vehicle  90 , (iii) a vehicle exiting a platoon comprising vehicle  90 , (iv) a responsive measure to take such as changing a speed of vehicle  90  and/or a heading of vehicle  90 , (v) instructions for merging vehicle  90  into a flow of other vehicles, or (vi) instructions for vehicle  90  to enter or exit a platoon. Other examples of alerts presentable via user interface  420  are also possible. 
     User interface  420  may also include a device that is operable to allow a user of vehicle  90  to input data for use by the components of vehicle  90 . As an example, user interface  420  may include a switch (e.g., a push button or a key on a keypad) that is operable to (i) input a signal to terminate (e.g., turn off) an alert being presented by user interface  420 , (ii) select a desired destination for vehicle  90 , (iii) select a preferred path for traveling to the desired destination, and/or (iv) turn on or off an automatic vehicle speed control of the vehicle  90  (e.g., cruise control). 
     RF communications interface  430  may include any of a variety of RF transceivers that are operable to transmit and receive RF communications. Transmission of the RF communications may include transmitting vehicle data  211  to one or more other vehicles and/or to one or more RNDs, such as RND  80 . Receiving the RF communications may include receiving vehicle data from one or more other vehicles, such as vehicle data  212  and  213 , and/or receiving data from one or more RND, such as RND  80 . RF communications interface  430  may operate according to any of a variety of air interface protocols and/or standards, such as the Interim Standard 95 (IS-95) for code division multiple access (CDMA) RF communications, an IEEE 802.11 standard for wireless local area networks, an IEEE 802.16 standard for broadband wireless access (e.g., a WiMAX standard), or some other air interface standard now known or later developed (e.g., a Car-2-Car Communication Standard being developed by the Car 2 Car Communication Consortium, Braunschweig, Germany) With regard to the IEEE 802.11 standard, as an example, the standard may include the IEEE 802.11p standard for Wireless Access for the Vehicular Environments (WAVE). 
     Position determination device  440  may include a device that is operable to determine a position (e.g., a geographic location) of the vehicle comprising position determination device  440  (e.g., vehicle  90 ). As an example, position determination device  440  may include a global positioning system (GPS) receiver and associated circuitry for receiving and processing RF signals from GPS satellites so as to determine a position of vehicle  90 . 
     As indicated above, vehicle  90  may include data storage device  200 , which contains CRPI  240 . The CRPI in data storage  200  implemented in vehicle  90  are executable by processor  410 . The CRPI executable by processor  410  may include program instructions for generating vehicle data  211  (i.e., the electronic horizon) for vehicle  90  and for causing RF communications interface  430  to transmit vehicle data  211  to one or more other vehicles, such as vehicle  95 , or to one or more RNDs, such as RND  80 . 
     Next,  FIG. 5  is a block diagram of example components within RND  80 . As illustrated in  FIG. 5 , RND  80  may include a processor  510 , a user interface  520 , an RF communications interface  530 , and data storage device  200 , all of which may be linked together via a system bus, network, or other connection mechanism  540 . A person having ordinary skill in the art will understand that other RND may be arranged in a configuration similar to RND  80 . 
     Processor  510  may include one or more general purpose processors (e.g., Intel microprocessors) and/or one or more special purpose processors (e.g., digital signal processors). Processor  510  may execute computer-readable program instructions contained in data storage device  200 . 
     User interface  520  may include a device that is operable to present information to a user of RND  80 . As an example, user interface  520  may include a display (e.g., a liquid crystal display and/or one or more other displays) to visually present a graphical user interface that allows a user to add, modify, and or delete data within data storage device  200 . User interface  520  may also include a device that is operable to allow a user of RND  80  to input data for use by the components of RND  80 . As an example, user interface  520  may include a switch (e.g., a push button or a key on a keypad) that is operable to input a signal to add, modify, and or delete data contained in data storage device  200 . 
     RF communications interface  530  may include any of a variety of RF transceivers that are operable to transmit and receive RF communications. Transmission of the RF communications may include transmitting an alert to one or more vehicles, such as vehicle  90 . Receiving the RF communications may include receiving vehicle data from multiple vehicles, such as vehicle data  212  from vehicle  90  and vehicle data  213  from vehicle  95 . RF communications interface  530  may also transmit communications to and/or receive communications from another RND. RF communications interface  530  may operate according to any of a variety of air interface standards, such as the Interim Standard 95 (IS-95), an IEEE 802.11 air interface standard, an IEEE 802.16 air interface standard, or some other air interface standard now known or later developed. 
     As indicated above, RND  80  may include data storage device  200 , which contains CRPI  240 . The CRPI in data storage  200  implemented in RND  80  are executable by processor  510 . The CRPI executed by processor  510  can cause processor  510  to determine, for one or more intersections having traffic signals (e.g., traffic lights comprising red, amber and green lights) to control a flow of traffic through the intersection, a probable arrival time of vehicles reaching a given area prior to each of the one or more intersections. 
     Upon determining those probable arrival times for a given intersection, processor  510  can determine whether the on/off state of the traffic signals at that intersection should be altered. For example, if processor  510  determines that, at a given time, one vehicle will probably be on road segment  28  within an area prior to the intersection at node  44  and six vehicles will probably be on road segment  22  within an area prior to the intersection at node  44 , processor  510  may determine to alter the state of the traffic signals at that intersection so that the six vehicles will be allowed to pass through the intersection without stopping at the intersection. Processor  510  may base its determination to alter the state of the traffic signals based on the probabilities of vehicles being in the area prior to an intersection being greater than a threshold probability. 
     III. Example Operation 
       FIG. 6  is a flow chart depicting a set of functions  600  that may be carried out in accordance with an example embodiment. The set of functions  600  may be carried out at any of a variety of elements. As an example, the set of functions  600  may be carried out at a vehicle, such as vehicle  90 , and/or some other vehicle. In accordance with that example, at least a portion of the set of functions  600  may be carried out by processor  410 . As another example, the set of functions  600  may be carried out at an RND, such as RND  80 . In accordance with that example, at least a portion of the set of functions  600  may be carried out by processor  510 . 
     Block  602  includes receiving a first set of vehicle data. The first set of vehicle data includes data that is associated with both a first vehicle and a given road segment defined for a road network on which the first vehicle can travel. As an example, the first set of vehicle data may be generated at vehicle  90  and the first set of vehicle data may include vehicle data  211 . 
     In accordance with an embodiment in which the set of functions  600  is carried out by vehicle  90 , receiving the first set of vehicle data may include processor  410  receiving the first set of vehicle data from data storage device  200  or data storage device  200  receiving the first set of vehicle data from processor  410  after generation of the first set of vehicle data. In accordance with an embodiment in which the set of functions  600  is carried out by RND  80 , receiving the first set of vehicle data may include RF communications interface  530  receiving the first set of vehicle data via vehicle-to-RND communications  12  from vehicle  90 . 
     Next, block  604  includes receiving a second set of vehicle data. The second set of vehicle data includes data that is associated with a second vehicle and the given road segment defined for the road network. The second vehicle (e.g., vehicle  95 ) can travel on the same road segment that the first vehicle (e.g., vehicle  90 ) can travel. The second vehicle can generate the second set of vehicle data and then transmit the second set of vehicle data via an air interface. 
     In accordance with an embodiment in which the set of functions  600  is carried out by vehicle  90 , receiving the second set of vehicle data may include RF communications interface  430  receiving the second set of vehicle data via the air interface. In accordance with an embodiment in which the set of functions  600  is carried out by RND  80 , receiving the second set of vehicle data may include RF communications interface  530  receiving the second set of vehicle data via the air interface from vehicle  95 . 
     Next, block  606  includes determining a probability that the first and second vehicles will arrive at the same place at the same time. A processor using at least a portion of the first set of vehicle data and at least a portion of the second set of vehicle data determines a first multi-vehicle probability value that indicates a probability that the first vehicle and the second vehicle will arrive at a common position of the given road segment simultaneously. The common position may be located at or between nodes of a road segment. 
     A processor, such as processor  410  or processor  510  may execute CRPI  240  to determine the first multi-vehicle probability value. Executing those program instructions may include obtaining the vehicle data used to determine the value from data storage device  200 . Examples of determining a multi-vehicle probability value are described above with respect to Table 1 and Table 2. 
     In response to determining the first multi-vehicle probability value, the processor that determines the first multi-vehicle probability value may execute computer-readable program instructions to select a threshold probability value from data storage device  200  and then compare the first multi-vehicle probability value to the selected threshold probability value. If data storage device  200  contains a single threshold probability value, then selecting the threshold probability value includes selecting that threshold probability value. If data storage device  200  contains a plurality of threshold probability values, then selecting the threshold probability value includes selecting one of the threshold probability values. Such selection may be based on a variety of factors, such as road conditions, probable speeds of the first and second vehicles, time of day, or any of a variety of other factors. 
     The vehicle data for the first vehicle and the vehicle data for the second vehicle may each include vehicle data associated with a plurality of road segments of a road network. The plurality of road segments of those vehicle data may include road segments common to both vehicle data as well as road segments found in only one of those vehicle data. As the first vehicle and second vehicle move from one position in road network  100  to another position within road network  100 , the vehicle data for each of those vehicles can change. 
     Since the vehicle data for the first vehicle and the vehicle data for the second vehicle can each include data for multiple road segments, a processor having access to that vehicle data may determine a plurality of multi-vehicle probability values. Two or more of those probability values may be associated with a common road segment of road network  100  (e.g., two multi-vehicle probability values associate with road segment  28 ) or with different road segments of road network  100  (e.g., a multi-vehicle probability value associated with road segment  28  and another multi-vehicle probability value associated with road segment  22 ). 
     Next, block  608  includes taking a responsive measure if the multi-vehicle probability value exceeds a threshold probability value. Taking the responsive measure may be carried out in various ways. 
     In accordance with an example embodiment in which vehicle  90  determines the first multi-vehicle probability value exceeds the threshold probability value, taking the responsive measure may be carried out, at least in part, by processor  410  executing program instructions to carry out the responsive measure. Executing those program instructions may cause RF communications interface  430  to transmit an alert to vehicle  95  so as to cause a driver or vehicle  95  to change a speed and/or direction of vehicle  95 , or to transmit an alert to RND  80 . Additionally or alternatively, executing the program instructions may cause user interface  420  to present a visual or audible alert. 
     In accordance with an example embodiment in which RND  80  determines the first multi-vehicle probability value exceeds the threshold probability value, taking the responsive measure may be carried out, at least in part, by processor  510  executing program instructions to carry out the responsive measure. Executing those program instructions may cause RND  80  to transmit an alert to the first vehicle and/or the second vehicle via an air interface. Additionally or alternatively, executing those program instructions may cause user interface  520  to visually or audibly present an alert to drivers of vehicles traveling on road network  100 . 
     IV. Conclusion 
     Example embodiments have been described above. Those skilled in the art will understand that changes and modifications may be made to the described embodiments without departing from the true scope and spirit of the present invention, which is defined by the claims.