Server, System, and Method for Determining a Position of an End of a Traffic Jam

Abstract

A system and method is provided for determining a position of an end of a traffic jam using traffic measurement data received from a plurality of vehicle and at least one sigmoid function. A system server configured to execute the method includes a computer unit, a memory, a receiving unit for receiving a plurality of measured data, each having one position data indication of a vehicle.

Claims

1. A server for determining a position of an end of a traffic jam, comprising: a computer unit; a memory; a reception unit configured to receive a plurality of measurement data from a plurality of vehicles, in each case with at least one position data statement of a respective vehicle; and a transmission unit configured to transmit the position of the end of the traffic jam to the plurality of vehicles, wherein the memory is configured to store at least a portion of the measurement data received by the reception unit, and the computer unit is configured to determine the position of the end of the traffic jam using at least one sigmoid function and the portion of the measurement data stored in the memory, and control the transmission unit to transmit the determined position of the end of the traffic jam to at least one of the plurality of vehicles.

2. The server as claimed in claim 1, wherein the measurement data are data tuples which include at least one of traffic information data, speed data that includes at least one speed of the respective vehicle, distance data that includes at least one distance between the respective vehicle and a vehicle traveling ahead of the respective vehicle, and braking frequency data that includes a braking frequency of the respective vehicle.

3. The server as claimed in claim 2, wherein the computer unit is configured to determine a plurality of parameter sets, each parameter set of the plurality of parameter sets defines a first sigmoid function and a second sigmoid function of the at least one sigmoid function, and each first sigmoid function models a speed profile and each second sigmoid function models a traffic density profile.

4. The server as claimed in claim 3, further comprising: a rating unit configured to rate a quality of the sigmoid functions of at least one of the plurality of parameter sets determined by the computer unit using at least some of the plurality of measurement data.

5. The server as claimed in claim 4, wherein the rating unit is configured to rate sigmoid function quality using at least one of a particle filter, a support vector machine, and a linear discriminant analysis.

6. The server as claimed in claim 7, wherein the reception unit is configured to receive from a further server traffic jam data indicative of a traffic jam area, and the second sigmoid functions are computed using the traffic jam data.

7. An traffic jam end position determination system, comprising: a plurality of vehicles; and a server, the server including: a computer unit; a memory; a reception unit configured to receive a plurality of measurement data from the plurality of vehicles, in each case with at least one position data statement of a respective vehicle; and a transmission unit configured to transmit the position of the end of the traffic jam to the plurality of vehicles, wherein the memory is configured to store at least a portion of the measurement data received by the reception unit, and the computer unit is configured to determine the position of the end of the traffic jam using at least one sigmoid function and the portion of the measurement data stored in the memory, and control the transmission unit to transmit the determined position of the end of the traffic jam to at least one of the plurality of vehicles, and wherein the vehicles transmit measurement data to the server.

8. The system as claimed in claim 7, wherein at least one vehicle of the plurality of vehicles is configured to at least one of transmit measurement data to the reception unit at regular intervals of time, and transmit measurement data to the reception unit in response to a measurement data request from the server.

9. The system as claimed in claim 8, wherein the server is configured designed to select the at least one vehicle to receive the measurement data request based on traffic jam data.

10. The system as claimed in claim 9, wherein the server is configured to determine based on the traffic jam data a traffic direction and at least one of a provisional position of the end of the traffic jam, a center of the traffic jam, and a start of the traffic jam, determine a vehicle position and a vehicle direction of travel for each of at least a portion of the plurality of vehicles, and identify, based on the respective vehicle position and vehicle direction of travel, at least one vehicle that is at least one of before the provisional position of the end of the traffic jam and the center of the traffic jam and is moving toward the end of the traffic jam.

11. The system as claimed in claim 10, wherein the at least one vehicle includes at least one distance measuring unit configured to measure a distance between the at least one vehicle and a vehicle traveling ahead of the at least one vehicle, and the traffic information data is based on the distance between the at least one vehicle and a vehicle traveling ahead of the at least one vehicle.

12. A method for determining a position of an end of a traffic jam using traffic measurement data from a plurality of vehicles and a traffic jam end position determination system server, the server including a reception unit configured to receive the measurement data from the plurality of vehicles, in each case with at least one position data statement of a respective vehicle, a memory configured to store at least a portion of the measurement data received by the reception unit, a transmission unit configured to transmit the position of the end of the traffic jam to the plurality of vehicles, and a computer unit configured to determine the position of the end of the traffic jam using at least one sigmoid function and the portion of the measurement data stored in the memory and to control the transmission unit to transmit the determined position of the end of the traffic jam to at least one of the plurality of vehicles, the method comprising the acts of: determining a plurality of parameter sets, wherein each parameter set defines a first sigmoid function and a second sigmoid function, the first sigmoid function modelling a speed profile and the second sigmoid function modelling a traffic density profile; receiving measurement data for at least one vehicle of the plurality of vehicles; rating a quality of at least some of the sigmoid functions defined by the parameter sets based on the received measurement data; selecting at least one parameter set based on the rating; computing the position of the end of the traffic jam using the at least one selected parameter set; and transmitting with the transmission unit the position of the end of the traffic jam to the plurality of vehicles.

13. The method as claimed in claim 12, further comprising the steps of: generating further parameter sets using the at least one selected parameter set; receiving further measurement data for two or more of the plurality of vehicles; rating a quality of at least some of the sigmoid functions defined by the further parameter sets based on the further measurement data; selecting at least one further parameter set based on the rating; determining the position of the end of the traffic jam on the basis of the at least further selected parameter set; transmitting with the transmission unit an updated position of the end of the traffic jam to the plurality of vehicles.

14. A computer readable storage medium having executable instructions configured to perform the method as claimed in claim 12 when the instructions are executed by the computer unit.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0064] FIG. 1 shows a schematic depiction of a server in accordance with an embodiment of the present invention.

[0065] FIG. 2 shows a schematic depiction of two intercommunicating servers in accordance with an embodiment of the present invention.

[0066] FIG. 3 shows a schematic depiction of a system in accordance with an embodiment of the present invention.

[0067] FIG. 4 shows a schematic plan view of two vehicles traveling in succession.

[0068] FIG. 5 shows a sigmoid function that models a speed profile in accordance with an embodiment of the present invention.

[0069] FIG. 6 shows a sigmoid function that models a traffic density profile in accordance with an embodiment of the present invention.

[0070] FIG. 7 shows a sigmoid function from FIG. 5 for determining the position of the end of the traffic jam.

[0071] FIG. 8 shows a sigmoid function from FIG. 6 for determining the position x2 of the end of the traffic jam.

[0072] FIG. 9 shows a schematic depiction for determining the characteristic of an end of the traffic jam in accordance with an embodiment of the present invention.

[0073] FIG. 10 shows a schematic flowchart for determining the position of the end of the traffic jam in accordance with an embodiment of the present invention.

[0074] FIG. 11 shows a further schematic flowchart from FIG. 10 for determining the position of the end of the traffic jam; and

[0075] FIG. 12 shows an execution cycle for probabilistic rating of parameter sets on the basis of measurement data for several vehicles in accordance with an embodiment of the present invention.

DETAILED DESCRIPTION OF THE DRAWINGS

[0076] In the description below, the same reference numerals are used for parts that are the same and have the same action.

[0077] The aim of the server 100 is to compute the position of an end of a traffic jam.

[0078] In the text below, an end of a traffic jam is intended to be understood to mean the position at which a vehicle is prompted on the basis of external influences, such as by a traffic accident, increased volume of traffic or environmental influences, for example, to reduce either the vehicle's speed and/or the distance from a vehicle traveling ahead.

[0079] FIG. 1 shows a schematic depiction of a server 100 that comprises a computer unit 10, a memory 20, a reception unit 30 and a rating unit 90.

[0080] As depicted in FIG. 2, the server 100 receives traffic jam data 21 from a further server 101. The traffic jam data 21 indicate an area on a road at which a traffic jam has occurred.

[0081] The reception unit 30 is designed to receive a multiplicity of measurement data 80, 81, 82, 83, as depicted in FIG. 3. The measurement data 80, 81, 82, 83 each comprises at least one position data statement x from vehicles 71, 72, 73, 74, wherein the vehicles 71, 72, 73, 74 are a vehicle fleet 70. The vehicle fleet 70 is defined by virtue of its being vehicles 71, 72, 73, 74 that are in direct proximity to one another and are all traveling in the same direction.

[0082] The server 100 is designed to use at least one sigmoid function, which is defined by four parameters [a1, a2, a3, 4a], for example, and to use the received measurement data 80, 81, 82, 83 to compute the position x2 of the end of the traffic jam. In this case, for example in a first iteration cycle, the sigmoid functions are determined by randomly chosen parameter values. It is also possible for the parameters [a1, a2, a3, 4a] of the sigmoid function to be computed using the traffic jam data 21.

[0083] FIG. 3 likewise shows a schematic depiction of a system. The system comprises the server 100 and the vehicles 71, 72, 73, 74, wherein the vehicles 71, 72, 73, 74 are designed to transmit measurement data 80, 81, 82, 83 to the server 100. In this case, the measurement data 80, 81, 82, 83 can be automatically transmitted from the vehicles 71, 72, 73, 74 to the server 100 at regular intervals of time, or the measurement data 80, 81, 82, 83 are transmitted only at the request of the server 100. A combination of the two types of transmission is also conceivable. This ensures optimum and suitable measurement data transmission within the system.

[0084] Further, the server 100 selects at least one vehicle 71 from a list of vehicles 71, 72, 73, 74, particularly using traffic jam data 21. The selected vehicle 71 is asked to transmit measurement data 80 to the server 100. The vehicle 71 can be selected in different ways:

[0085] In a first option, the server 100 asks all vehicles 71, 72, 73, 74 that are on the list to regularly transmit to it at least position data x that are additionally assigned to the vehicles 71, 72, 73, 74 on the list. On the basis of these position statements x, the server 100 selects vehicles 71, 72, 73, 74 that are situated in the vicinity of the traffic jam known from the traffic jam data 21 and asks said vehicles to send measurement data 80, 81, 82, 83 to it, which the server 100 uses to determine the position of the end of the traffic jam x2.

[0086] A further option involves the server 100 starting the measurement data request from vehicles 71, 72, 73, 74 only as soon as it has information 21 available about a traffic jam. In this case, the server 100 can firstly request all measurement data 80, 81, 82, 83 for the vehicles 71, 72, 73, 74 that are shown on the list. Secondly, in a first step, the server 100 can start a position request for all listed vehicles 71, 72, 73, 74 and store only the position x of the vehicles 71, 72, 73, 74 on the list. Based on the traffic jam data 21, the vehicles 71, 72, 73, 74 are then selected, with vehicles 71, 72, 73, 74 that are situated in the area of the traffic jam being selected. If there are still no measurement data 80, 81, 82, 83 available from these vehicles 71, 72, 73, 74, then in a second step the server 100 can request said measurement data in order to compute the position of the end of the traffic jam x2.

[0087] The server 100 is also designed to take the traffic jam data 21 as a basis for determining the provisional position of the end of the traffic jam x2 and/or of a traffic jam center and/or of a traffic jam start, and also the traffic direction in which the traffic jam has occurred. The same determination of the server 100 is also effected for a multiplicity of vehicles 71, 72, 73, 74, wherein the vehicle position x and vehicle direction of travel thereof are determined. Using the vehicle position x and the vehicle direction of travel, the server 100 selects at least one vehicle 71 that is situated before the provisional position of the end of the traffic jam x2 and/or of the traffic jam center, preferably before the provisional position of the traffic jam start, and is moved toward the end of the traffic jam x2. As a result of the selection of vehicles 71, 72, 73, 74 that are situated at a position x1 before the position of the end of the traffic jam x2 and are traveling toward the latter, only such measurement data 80, 81, 82, 83 from vehicles 71, 72, 73, 74 as are also directly related to the position to be computed for the end of the traffic jam are used. Hence, the measurement data transmission is optimized and reduced still further.

[0088] As soon as the server 100 has computed the position of the end of the traffic jam x2, it transmits this position x2 to the vehicles 71, 72, 73, 74. This allows presentation of the position of the end of the traffic jam x2 in the vehicles 71, 72, 73, 74. This informs the driver about the exact position x2 and/or also about the characteristic of the end of the traffic jam, for example by means of his navigation appliance. If the end of the traffic jam x2 is situated after a blind curve, for example, or if a hard end of the traffic jam is involved, then the driver of the vehicle 71, 72, 73, 74 can be forewarned in good time, so that the risk of accident is reduced.

[0089] The measurement data 80, 81, 82, 83 transmitted by the vehicles 71, 72, 73, 74 are data tuples. These data tuples comprise traffic information data, speed data, which indicate a speed v of the respective vehicle 71, and distance data, which indicate a distance r between the respective vehicle 71 and a vehicle traveling ahead of the respective vehicle 71. As depicted in FIG. 4, the vehicle 71 comprises a transmission unit 76 in order to transmit the measurement data 80 to the server 100. Further, the vehicle 71 comprises a distance measuring unit 75 in order to measure the distance r from a vehicle 72 traveling ahead.

[0090] Using the measurement data 80, 81, 82, 83, the server 100 determines the surroundings of the vehicle 71, such as the traffic density p, for example, wherein the traffic density ρ is dependent on the measured distance r and the vehicle length s of the measuring vehicle 71.

[0091] FIG. 5 shows a sigmoid function that models a speed profile 50 and is defined by four parameters [v1, v2, v3, v4]. This depicts how the speed v of a vehicle 71 changes over the location x. FIG. 6 shows a sigmoid function that models a traffic density profile 60 and is defined by four parameters [ρ1, ρ2, ρ3, ρ4]. This depicts how the traffic density ρ in the vicinity of a vehicle 71 changes over the location x. A speed profile 50 and a traffic density profile 60 respectively depict a parameter set 40. The server 100 is designed to determine a multiplicity of parameter sets 40, 42 in order to compute the position of the end of the traffic jam x2. A parameter set 40, 42 can in this case be determined by eight parameters [v1, v2, v3, v4, ρ1, ρ2, ρ3, ρ4]. Lane precise location of the vehicles 71, 72, 73, 74 therefore determines the position of the end of the traffic jam x2 and the characteristics of the end of the traffic jam in a manner accurate to traffic lane.

[0092] FIG. 7 uses the speed profile 50 and FIG. 8 uses the traffic density profile 60 to show how the position of the end of the traffic jam x2 is determined. In this case, in the speed profile 50, a tangent 51 is drawn to the constant speed v at a position x1 before the end of the traffic jam x2. A second straight line at a position x3 within the traffic jam depicts the slope 52 of the speed decrease after the end of the traffic jam x2. The intersection of the tangent 51 and the slope 52 determines the position of the end of the traffic jam x2 and hence the start of the traffic jam entry. The same method is applied in FIG. 8 using the traffic density profile 60 so as likewise to determine the position of the end of the traffic jam x2. In this case, a tangent 61 is drawn to the constant traffic density ρ at a position x1 before the end of the traffic jam x2. A second straight line at a position x3 within the traffic jam depicts the slope 62 of the traffic density increase after the end of the traffic jam x2. The intersection of the tangent 61 and the slope 62 determines the position of the end of the traffic jam x2 and hence the start of the traffic jam entry.

[0093] If the slope 52 of the speed profile 50 decreases rapidly and the slope 62 of the traffic density profile 60 increases rapidly, then a hard end of the traffic jam is involved, in which vehicles 71, 72, 73, 74 encounter, from unrestricted traffic, a buildup of, by way of example, stationary vehicles. If the slope 52 of the speed profile 50 decreases slowly and the slope 62 of the traffic density profile 60 increases slowly, then a soft end of the traffic jam is involved, which the vehicles 71, 72, 73, 74 steadily enter at an ever slower speed v.

[0094] A further option for determining the characteristic of the end of the traffic jam is depicted in FIG. 9. If the gradient dv of the speed decrease over time has a high negative value and the gradient dρ of the traffic density increase over time has a high positive value, then a hard end of the traffic jam is involved in this case. Conversely, if the speed gradient over time has a low negative value and the traffic density gradient over time has a low positive value, then a soft end of the traffic jam is involved.

[0095] As a result of the transmission of the position of the end of the traffic jam x2 and the transmission of the characteristic of the end of the traffic jam by the server 100 to the vehicles 71, 72, 73, 74, this information is processed and used to output warning signals to the driver himself or to other road users.

[0096] FIG. 10 shows a flowchart for a method that is used to determine the position of the end of the traffic jam x2. In this case, the server 100 is designed to perform the following steps: [0097] determining a multiplicity of parameter sets 40, wherein each parameter set 40 defines a first sigmoid function and a second sigmoid function, wherein the first sigmoid function models a speed profile 50 and the second sigmoid function models a traffic density profile 60; [0098] receiving measurement data 80 for at least one vehicle 71; [0099] rating the quality of at least some of the sigmoid functions defined by the parameter sets 40 using a rating unit 90 based on the received measurement data 80; [0100] selecting at least one parameter set 41 based on the rating; [0101] computing the position x2 of the end of the traffic jam on the basis of the at least one selected parameter set 41; [0102] sending the position x2 of the end of the traffic jam to a/the vehicle 71.

[0103] The rating unit 90 is designed to rate the parameter sets 40 using a particle filter. The particle filter is used to produce continual updates for the sigmoid functions 50, 60 by new measurement data 80, 81, 82, 83. In this context, the particle filter approximates the a posteriori distribution of the state probabilities of the sigmoid functions 50, 60 by a finite set of parameters [v1, v2, v3, v4, ρ1, ρ2, ρ3, ρ4]. A sample set, the particles, approximates the probability density function using the sigmoid functions 50, 60. In contrast to alternative approaches, the nonparametric form of particle filters means that they can approximate any distributions.

[0104] For an even more accurate determination of the position of the end of the traffic jam x2 or for an update for the position of the end of the traffic jam x2, FIG. 11 shows a further flowchart that is used to determine the position of the end of the traffic jam x2. In this case, the server 100 is designed to perform the following steps: [0105] generating, preferably randomly generating, further parameter sets 42 on the basis of the at least one selected parameter set 41, particularly within prescribed ranges of variation; [0106] receiving further measurement data for at least the vehicle 71 and/or for a further vehicle 72; [0107] rating the quality of at least some of the sigmoid functions defined by the further parameter sets 40 using a rating unit 90 based on the further measurement data 81; [0108] selecting at least further parameter set 43 based on the rating; [0109] computing the position x2 of the end of the traffic jam on the basis of the at least further selected parameter set 43; [0110] sending the position x2 of the end of the traffic jam to a/the vehicle 71 or a/the further vehicle 72.

[0111] New parameter sets 42 can be generated by virtue of the eight parameters [v1, v2, v3, v4, ρ1, ρ2, ρ3, ρ4] per parameter set 41 each being slightly altered at random with a certain level of noise. This measure allows a multiplicity of different parameter sets 42 to be produced again. As a result of the previously selected parameter set 41, the new parameter sets 42 represent the traffic state in an improved and adapted form in comparison with the first parameter sets 40. As a result of fresh rating and selection of the parameter sets 42, it is possible for the position of the end of the traffic jam x2 that was computed in a first step to be determined in an even more concrete form and more exactly by this second step. The generation of new parameter sets 42, the collation and rating of these new parameter sets 42 with always new measurement data 81 can be repeated as often as desired. Therefore, not only is it possible for the position x2 and also the characteristic of the end of the traffic jam to be determined ever more exactly, the current changing circumstances are at the same time also repeatedly adapted.

[0112] FIG. 12 once again depicts, in another way, how the position of the end of the traffic jam x2 can be determined. The most probable parameter set 41 is estimated cyclically. At the start, randomly or with the aid of traffic jam data 21, more probable parameterizations are used—to produce a large multiplicity of parameter sets 40 having eight parameters each [v1, v2, v3, v4, ρ1, ρ2, ρ3, ρ4]. In a next step, the sigmoid functions of the speed profile 50 and of the traffic density profile 60 that are explicitly defined by eight parameters each [v1, v2, v3, v4, ρ1, ρ2, ρ3, ρ4] are rated with the aid of the measurement 1000 in the rating step 2000. Parameter sets 40 that better correspond to the measurement 1000 or are closer to the really measured case are accordingly provided with a higher rating. In the selection step 3000, the parameter sets 41 are determined that are intended to be pursued further. Subsequently, the eight parameters [v1, v2, v3, v4, ρ1, ρ2, ρ3, ρ4] per drawn parameter set 41 are each slightly altered at random with a certain level of noise. A multiplicity of different parameter sets 42 are now available again. The measurements 1000 mean that the multiplicity of parameters sets 40 now represent the traffic state better, as before the rating step 2000. If further measurement data 81 or multiple synchronous/asynchronous measurements 1000 come at a new time, then the parameter sets 41 from the last time step are predicted for the respective new time. This can be accomplished by macroscopic traffic models, for example, that are described by partial differential equations. It is also possible for the step of prediction 4000 to be completely omitted if the noise to which the parameter sets 42 are subject, which can be applied before or even after the prediction 4000, is sufficiently great to capture the dynamics of the position of the end of the traffic jam x2. This sequence of the steps rating 2000, on the basis of the measurement 1000, selection 3000 and prediction 4000 takes place cyclically and as often as desired in this case.

REFERENCE SYMBOLS

[0113] 10 Computer unit [0114] 20 Memory [0115] 21 Traffic j am data [0116] 30 Reception unit [0117] 40 Parameter sets [0118] 41 Parameter set [0119] 42 Further parameter sets [0120] 43 Further parameter set [0121] 50 Speed profile [0122] 51 Tangent to the speed before the end of the traffic jam [0123] 52 Slope of the speed decrease [0124] 60 Traffic density profile [0125] 61 Tangent to the traffic density before the end of the traffic jam [0126] 62 Slope of the traffic density increase [0127] 70 Vehicle fleet [0128] 71 Vehicle [0129] 72 Further vehicle [0130] 73 Vehicle [0131] 74 Vehicle [0132] 75 Distance measuring unit [0133] 76 Transmission unit [0134] 80 Measurement data [0135] 81 Further measurement data [0136] 82 Measurement data [0137] 83 Measurement data [0138] 90 Rating unit [0139] 100 Server [0140] 101 Further server [0141] 1000 Measurement [0142] 2000 Rating [0143] 3000 Selection [0144] 4000 Prediction [0145] x Position data statement [0146] x1 Position before the end of the traffic jam [0147] x2 Position of the end of the traffic jam [0148] x3 Position after the end of the traffic jam [0149] v Speed [0150] dv Gradient of the speed decrease over time [0151] ρ Traffic density [0152] dρ Gradient of the traffic density increase over time [0153] r Offset between two vehicles [0154] s Length of the vehicle

[0155] The foregoing disclosure has been set forth merely to illustrate the invention and is not intended to be limiting. Since modifications of the disclosed embodiments incorporating the spirit and substance of the invention may occur to persons skilled in the art, the invention should be construed to include everything within the scope of the appended claims and equivalents thereof.