JP5450365B2 - Driving support system - Google Patents

Description

  The present invention relates to a driving support system, and more specifically, to a driving support system that provides driving support information for eliminating traffic jams in a vehicle group based on the traffic jam prediction information of the vehicle group.

  Conventionally, techniques related to vehicle group management have been proposed. For example, Patent Document 1 describes a traffic information system that manages a vehicle group while transmitting and receiving a vehicle group ID among an in-vehicle device, an optical communication roadside device, and an optical system network.

JP 2010-39918 A

  In conventional methods including Patent Document 1, vehicle groups are specified and managed on the premise of information from an intelligent road traffic system (ITS) such as a camera installed on a road. Therefore, when such an ITS infrastructure cannot be used, the vehicle group cannot be managed, and the vehicle congestion information cannot be provided. Furthermore, it cannot be said that the accuracy of the traffic jam prediction of the vehicle group is necessarily high, and there is room for further improvement in order to avoid or eliminate the traffic jam.

  Accordingly, an object of the present invention is to provide a driving support system that can identify a vehicle group that forms a traffic jam without an ITS infrastructure and can eliminate the traffic jam of the vehicle group.

  The present invention relates to a traffic jam prediction unit that performs traffic jam prediction in real time, a vehicle group traffic jam information acquisition unit that acquires traffic jam prediction information about a vehicle group obtained by the traffic jam prediction unit, and traffic jam information about the vehicle group. Vehicle group end vehicle specifying means for specifying a vehicle group end vehicle indicating start or end, and driving support information providing means for providing driving support information to the vehicle group end vehicle specified by the vehicle group end vehicle specifying means The travel support information providing means is a vehicle travel support system that provides travel support information for eliminating traffic congestion to at least one vehicle in the end of the vehicle group.

  According to the present invention, it is possible to eliminate the traffic jam by providing the travel support information to the vehicle group end vehicle (the last vehicle or the foremost vehicle) of the vehicle group forming the traffic jam.

  The traffic jam prediction means of the present invention calculates the acceleration of the host vehicle, calculates the power spectrum corresponding to the frequency from frequency analysis of the detected acceleration, calculates a single regression line of the calculated power spectrum, A step of calculating a maximum value of the amount of change of the slope of the single regression line in the frequency range as a slope maximum value, a step of detecting the inter-vehicle distance between the host vehicle and the preceding vehicle, and a distribution estimation method from the detected inter-vehicle distance Using the estimation of the inter-vehicle distance distribution, the step of calculating the minimum covariance from the estimated inter-vehicle distance distribution, and the vehicle group distribution ahead from the correlation between the minimum covariance value and the maximum slope value. The traffic jam prediction is performed by executing the estimation step and the traffic jam prediction step based on the vehicle group distribution in real time.

  According to the traffic jam prediction means of the present invention, traffic jam prediction is performed based on the vehicle group distribution estimated from the correlation between the maximum slope value obtained from the acceleration spectrum of the host vehicle and the minimum covariance value obtained from the inter-vehicle distance density. As a result, it is possible to improve the congestion prediction accuracy.

  According to one aspect of the present invention, the driving support information for eliminating the traffic jam includes an instruction to increase the inter-vehicle distance toward the end-of-car group vehicle indicating the end of the traffic jam.

  According to an aspect of the present invention, it is possible to reduce the traffic jam in the vehicle group by increasing the inter-vehicle distance between the last vehicle in the vehicle group and the vehicle in front of the vehicle.

  According to one aspect of the present invention, the travel support information for eliminating the traffic congestion is an instruction to increase the vehicle speed toward the vehicle end vehicle when the vehicle speed of the vehicle end vehicle indicating the start of the traffic jam is equal to or lower than a predetermined speed. Including.

  According to one aspect of the present invention, it is possible to make the vehicle group's traffic jam clear by increasing the speed of the foremost vehicle in the vehicle group that is causing the traffic jam.

  According to an aspect of the present invention, the vehicle group traffic jam information acquisition unit acquires the vehicle traffic jam information from at least one of communication between the vehicle and the base station server, vehicle-to-vehicle communication, and road-to-vehicle communication. .

  According to one aspect of the present invention, it is possible to obtain traffic information of a vehicle group that is necessary in a timely manner from an available information source and provide travel support information for eliminating the traffic jam.

It is a figure which shows the structure of the driving assistance system according to one Example of this invention. It is a figure which shows the structure of the traffic congestion prediction means according to one Example of this invention. FIG. 4 shows an acceleration spectrum according to one embodiment of the present invention. FIG. 6 is a diagram illustrating a probability density distribution according to an embodiment of the present invention. FIG. 6 is a diagram schematically showing a covariance value Σ _k according to an embodiment of the present invention. It is an image (concept) figure of the correlation map of inclination maximum value and covariance minimum value according to one Example of this invention. It is a figure which shows the relationship between traffic density and traffic volume. 6 is a correlation map between the logarithm of the minimum covariance value for the inter-vehicle distance distribution and the logarithm of the slope maximum value for the acceleration spectrum according to one embodiment of the present invention. It is a figure which shows the vehicle state before and behind a traffic light. 6 is a correlation map between the logarithm of the minimum covariance value for the inter-vehicle distance distribution and the logarithm of the slope maximum value for the acceleration spectrum according to one embodiment of the present invention. 6 is a correlation map between the logarithm of the minimum covariance value for the inter-vehicle distance distribution and the logarithm of the slope maximum value for the acceleration spectrum according to one embodiment of the present invention. It is a figure for demonstrating specification of the vehicle group end vehicle which contributes to vehicle group fluctuation | variation according to one Example of this invention. It is a flowchart of driving assistance control according to one example of the present invention.

  Embodiments of the present invention will be described with reference to the drawings. FIG. 1 is a diagram showing a configuration of a driving support system according to an embodiment of the present invention. The driving support system 100 includes a traffic jam prediction unit 10 mounted on a vehicle 80 and a driving support unit 90 mounted on a server of a base station. The traffic jam prediction unit 10 and the travel support unit 90 exchange information with each other by wireless communication. The driving support means 90 can also be installed in a communication device provided on the road. In this case, information is mutually exchanged with the traffic jam prediction means 10 through road-to-vehicle communication. Further, the driving support means 90 can be mounted on the vehicle 80 in the same manner as the traffic jam prediction means 10, and in this case, information is mutually exchanged by inter-vehicle communication.

  The driving support unit 90 includes a vehicle group traffic jam information acquisition unit 901, a vehicle group end vehicle specifying unit 902, and a driving support information providing unit 903. The vehicle group traffic jam information acquisition unit 901 acquires the traffic jam prediction information about the vehicle group obtained by the traffic jam prediction unit 10. The vehicle group end vehicle specifying means 902 specifies a vehicle group end vehicle (the last vehicle or the foremost vehicle) indicating the start or end of the traffic jam from the traffic jam information about the vehicle group. The driving support information providing unit 903 provides the driving support information to the vehicle group end vehicle (the rearmost vehicle or the frontmost vehicle, or both) specified by the vehicle group end vehicle specifying unit 902. These processes by the driving support means 90 are realized by a computer (CPU), a memory, a wireless communication interface, etc. (not shown) included in the driving support means 90.

  FIG. 2 is a block diagram showing the configuration of the traffic jam prediction means 10 for carrying out the traffic jam prediction method according to one embodiment of the present invention. The traffic jam prediction means 10 can be mounted on a vehicle as one device or as part of another device.

  The traffic jam prediction means 10 includes a vehicle speed sensor 11, a radar device 12, a navigation device 13, a processing device 14, a switch 15, various actuators 16, a speaker 17, a display 18, and a communication device 19. Note that the processing device 14 may be incorporated in the navigation device 13. In addition, the speaker 17 and the display 18 may use corresponding functions provided in the navigation device 13.

  The vehicle speed sensor 11 detects the acceleration of the host vehicle and sends a detection signal to the processing device 14. The radar device 12 divides a predetermined detection target region set around the host vehicle into a plurality of angle regions, and transmits an electromagnetic wave such as an infrared laser or millimeter wave while scanning each angle region. . The radar device 12 receives a reflection signal (electromagnetic wave) from an object in the detection target region and sends the reflection signal to the processing device 14.

  The navigation device 13 receives a positioning signal such as a GPS signal and calculates the current position of the host vehicle from the positioning signal. The navigation device 13 can also calculate the current position of the host vehicle from the acceleration and yaw rate detected by the vehicle speed sensor 11 and the yaw rate sensor (not shown) using autonomous navigation. The navigation device 13 includes map data and has a function of outputting the current position of the host vehicle, route information to a destination, traffic jam information, and the like on a map to be displayed.

  The processing device 14 includes a frequency analysis unit 31, a single regression line calculation unit 32, a slope maximum value calculation unit 33, a reflection point detection unit 34, another vehicle detection unit 35, an inter-vehicle distance detection unit 36, an inter-vehicle distance distribution estimation unit 37, A minimum variance calculation unit 38, a correlation map creation unit 40, a traffic jam prediction unit 41, a travel control unit 42, a notification control unit 43, and a communication control unit 44 are provided. The function of each block is realized by a computer (CPU) included in the processing device 14.

  The processing device 14 has, for example, an A / D conversion circuit that converts an input analog signal into a digital signal, a central processing unit (CPU) that performs various arithmetic processing, and a CPU that stores data when performing arithmetic operations. It includes a RAM to be used, a ROM for storing programs to be executed by the CPU and data to be used (including tables and maps), an output circuit for outputting a drive signal for the speaker 17, a display signal for the display 18, and the like.

  The switch 15 outputs various signals related to traveling control of the host vehicle to the processing device 14. The various signals include, for example, accelerator pedal and brake pedal operation (position) signals, various signals related to automatic travel control (ACC) (control start, control stop, target vehicle speed, inter-vehicle distance, and the like).

  The various actuators 16 are used as a general term for a plurality of actuators, and include, for example, a throttle actuator, a brake actuator, a steering actuator, and the like.

  The display 18 includes a display such as an LCD, and can be a display having a touch panel function. The display device 16 may be configured to include an audio output unit and an audio input unit. The indicator 18 notifies the driver by displaying predetermined alarm information or blinking or lighting a predetermined warning light in response to a control signal from the notification control unit 43. The speaker 17 notifies the driver by outputting a predetermined alarm sound or sound according to a control signal from the notification control unit 43.

  The communication device 19 communicates with another vehicle or a server device (not shown) or a relay station (not shown) by wireless communication under the control of the communication control unit 44, and the traffic jam prediction result output from the traffic jam prediction unit 41 Position information is transmitted in association with each other, or correspondence information between a traffic jam prediction result and position information is received from another vehicle or the like. The acquired information is sent to the notification control unit 43 or the travel control unit 42 via the communication control unit 44.

  Next, the function of each block of the processing device 14 will be described. The frequency analysis unit 31 performs frequency analysis on the acceleration of the host vehicle detected by the vehicle speed sensor 11 and calculates a power spectrum. FIG. 3 shows examples of power spectra in two different running states (a) and (b). In FIG. 3, acceleration spectra 51 and 53 corresponding to frequencies are illustrated as power spectra.

  The single regression line calculation unit 32 performs a single regression analysis on the obtained power spectrum and calculates a single regression line. In the example of FIG. 3, the straight lines indicated by reference numerals 52 and 54 are simple regression lines obtained for the acceleration spectra 51 and 53, respectively.

  The slope maximum value calculation unit 33 calculates the slope maximum value from the obtained single regression line. In the example of FIG. 3, first, the slopes of the single regression lines 52 and 54 are calculated. That is, in FIG. 3, the slope α (= Y / X) based on the change X of the spectral value in a predetermined frequency range Y (for example, a frequency range corresponding to a time range of several seconds to several minutes, 0 to 0.5 Hz, etc.). ) Is calculated. In FIG. 2, the inclinations α1 and α2 at (a) and (b) are obtained.

Next, a difference between the obtained inclinations α, that is, a difference Δα (= α _k −α _k−1 ) between the inclinations α _k and α _k−1 at a predetermined time interval is calculated. A time change of the obtained difference Δα or a maximum value of the time change of a parameter (for example, a square value (Δα) ² , an absolute value | Δα |, etc.) obtained from the difference Δα is obtained. The obtained maximum value is stored in a memory (RAM or the like) in the processing device 14 as a tilt maximum value.

  The reflection point detector 34 detects the position of the reflection point (object) from the reflection signal detected by the radar apparatus 12. The other vehicle detection unit 35 is based on the position information of the reflection point output from the reflection point detection unit 34, and is at least one or more units present in the vicinity of the host vehicle from the distance between adjacent reflection points, the distribution state of the reflection points, and the like. Detect other vehicles. The inter-vehicle distance detection unit 36 detects the inter-vehicle distance between the host vehicle and the other vehicle from the other vehicle information detected by the reflection point detection unit 34, and outputs the result together with the detected number of other vehicles.

  The inter-vehicle distance distribution estimation unit 37 estimates the inter-vehicle distance distribution from the information on the inter-vehicle distance and the number of vehicles output from the inter-vehicle distance detection unit 36. The inter-vehicle distance distribution estimation will be described with reference to FIG. FIG. 4 is a diagram showing a probability density distribution.

  If a vehicle group ahead, that is, a set of cars with relatively small distance between vehicles, can be observed from the information on the distance between vehicles and the number of vehicles, a Gaussian distribution ( Apply probability density distribution. For example, when there are two vehicle groups, the vehicle group can be regarded as a distribution obtained by linearly combining two Gaussian distributions. That is, as shown in FIG. 4, a probability function P (X) representing the entire distribution can be obtained as the sum (superposition) of the probability functions P1 (X) and P2 (X) representing the two Gaussian distributions. .

ここで、μ_ｋは期待値（平均値）であって密度が最も高い位置を表す。Σ_ｋは共分散値（行列）であって、分布のゆがみ、すなわち期待値からどの方向に離れると密度がどのように減るかを表す。π_ｋはガウス分布の混合係数（混合比）であって、各ガウス分布がどれだけ寄与しているかの割合（０≦π_ｋ≦１）を表す。混合係数π_ｋは１つの確率として捉えることができる。 When a Gaussian distribution (probability function) is represented by N (X | μ, Σ), a superposition of a plurality of Gaussian distributions as exemplified in FIG. 4 can be obtained by the following equation.

Here, μ _k is an expected value (average value) and represents a position having the highest density. Σ _k is a covariance value (matrix), and represents distortion of the distribution, that is, how the density decreases in which direction away from the expected value. π _k is a mixing coefficient (mixing ratio) of a Gaussian distribution, and represents a ratio (0 ≦ π _k ≦ 1) of how much each Gaussian distribution contributes. The mixing coefficient π _k can be regarded as one probability.

The covariance minimum value calculation unit 38 performs calculation using variational Bayes or the like in order to obtain a parameter (covariance) that maximizes the likelihood function obtained from the above-described P (X), for example. When the Gaussian distribution P (X) is obtained as a superposition of a plurality of Gaussian distributions as illustrated in FIG. 4, a covariance value Σ _k is calculated for each Gaussian distribution.

The covariance minimum value calculation unit 38 then calculates the minimum value of the plurality of covariance values Σ _k obtained for each Gaussian distribution P (X). Figure 5 is a diagram schematically representing the covariance value sigma _k. In FIG. 5A, the graph 56 representing the covariance value Σ _k is a sharp graph at delta (δ) 0, and there is no fluctuation of the vehicle group, that is, the vehicle is in a traveling state in which the inter-vehicle distance is substantially constant. It suggests. On the other hand, in FIG. 5B, two graphs are obtained: a graph 57 having a peak at δ1 in a negative region of delta (δ) and a graph 58 having a peak at δ2 in a positive region. Both the graphs 57 and 58 have a predetermined fluctuation range (δ), which indicates that there are fluctuations in the vehicle group, in other words, that there are a plurality of sets of cars having different inter-vehicle distances. In FIG. 5, the minimum value of the covariance value Σ _k is substantially zero (0) in (a) and the smaller δ1 in (b).

  The correlation map creation unit 40 in FIG. 2 creates a correlation map between the slope maximum value calculated by the slope maximum value calculation unit 33 and the minimum covariance value calculated by the covariance minimum value calculation unit 38. FIG. 6 is an image (concept) diagram of a correlation map between the maximum slope value and the minimum covariance value. In FIG. 6, the horizontal (X) axis is the covariance minimum value X and the vertical (Y) axis is the slope maximum value Y, and the correlation of the variables (X, Y) is mapped. Two areas indicated by reference numerals 59 and 60 are shown, and there is a boundary area 61 where these two areas overlap. The region 59 corresponds to a state where the covariance minimum value is relatively small and the variation of the vehicle group is small, in other words, a state where the inter-vehicle distance is relatively constant. Conversely, the region 60 corresponds to a state where the covariance minimum value is relatively large and the variation of the vehicle group is large, in other words, a state where a plurality of sets of vehicles having different inter-vehicle distances exist. The boundary region 61 is a region where the variation of the vehicle group changes from a small state to a large state, and the present invention is characterized in that the state of the vehicle group corresponding to the boundary region 61 is quantitatively found and a traffic jam is predicted. There is.

  Here, the respective regions illustrated in FIG. 6 will be further described with reference to FIG. FIG. 7 is a diagram showing the relationship between traffic density and traffic volume. The horizontal (X) axis of the graph is a traffic density that means the number of vehicles existing within a predetermined distance from the host vehicle. The reciprocal of this traffic density corresponds to the inter-vehicle distance. The vertical (Y) axis is a traffic volume that means the number of vehicles passing through a predetermined position. It can be understood that FIG. 7 represents a traffic flow that means the flow of a vehicle.

  The traffic flow illustrated in FIG. 7 can be roughly divided into four states (regions). The first is a free flow state in which the possibility of traffic congestion is low, and here, it is possible to ensure a certain level of acceleration and inter-vehicle distance. The second is a mixed flow state in which the braking state and the acceleration state of the vehicle are mixed. This mixed flow state is the state before the transition to the congestion flow, and the degree of freedom of driving by the driver is reduced, and the traffic flow is reduced and the traffic density is increased (reduction of the inter-vehicle distance). It is in a state where the probability of transition is high. The third is a traffic flow state indicating a traffic jam. The fourth is a critical region which is a transition state that exists during the transition from the free flow state to the mixed flow state. This region is a state in which the traffic volume and the traffic density are higher than those in the free stream, and a transition is made to a mixed stream due to a decrease in the traffic volume and an increase in the traffic density (a reduction in the inter-vehicle distance). The critical region is sometimes called metastable flow or metastable flow.

  6 and 7, the region 59 in FIG. 6 includes the free flow and critical region in FIG. 7, and the region 60 in FIG. 6 includes the mixed flow and congestion flow states in FIG. 7. Become. Therefore, the boundary of FIG. 6 is a boundary state including both the critical region and the mixed flow state of FIG. 7, and is referred to as a critical region boundary as shown in FIG.

  Quantification of the critical region will be described with reference to FIG. FIG. 8 is a diagram showing a correlation map between the logarithm of the minimum covariance value for the inter-vehicle distance distribution and the logarithm of the maximum slope value for the acceleration spectrum. FIG. 8A is a simplified drawing of the traffic flow map of FIG. 7, and FIG. 8B shows a correlation map between the logarithm of the minimum covariance value and the logarithm of the slope maximum value. The logarithm of the covariance minimum value and the logarithm of the slope maximum value in (b) is the difference between the slope maximum value calculated by the slope maximum value calculation unit 33 and the covariance minimum value calculated by the covariance minimum value calculation unit 38. Calculated as a logarithmic value. FIG. 8B depicts the parameterization of the phase transition state in the critical region by a single vehicle.

  In FIG. 8B, the region indicated by reference numeral 62 includes the critical region (a), and the region indicated by reference numeral 63 includes the mixed flow state of (a). The line indicated by the reference numeral 64 is a critical line, and means a critical point where there is a high possibility that traffic congestion will occur if the critical line is exceeded. The boundary region 65 between the regions 62 and 63 corresponds to the boundary of the critical region immediately before the criticality 64. The correlation map illustrated in FIG. 8B is stored in a memory (RAM or the like) in the processing device 14.

  The traffic jam prediction unit 41 in FIG. 2 determines whether or not a critical region boundary exists in the correlation map created by the correlation map creation unit 40. When the boundary of the critical region exists, the traffic jam prediction unit 41 sends information on the vehicle group in which the traffic jam occurs or occurs as the traffic jam prediction information to the communication control unit 44. The communication control unit 44 sends the received traffic jam prediction information to the travel support means 90 (FIG. 1) such as a base station server via the communication device 19.

  At the same time, the traffic jam prediction unit 41 can send a control signal including the traffic jam prediction result to the travel control unit 42 and the notification control unit 43 in order to prevent a shift to traffic jam. As a result, it is possible to execute notifications or various vehicle controls to prevent the transition to the mixed flow illustrated in FIG. 8, and as a result, it is possible not only to avoid traffic jams but also to predict traffic jams that are useful for eliminating traffic jams. It becomes. Further, the traffic jam prediction unit 41 outputs the traffic jam prediction result to the navigation device 13. Based on the traffic jam prediction result received from the traffic jam prediction unit 41 and the traffic jam prediction result predicted by the other vehicle output from the communication control unit 41, the navigation device 13 searches for a route of the host vehicle so as to avoid the traffic jam. Route guidance can be performed.

  Next, the details of the vehicle group end vehicle specifying means 602 in FIG. 1 will be described with reference to FIGS. FIG. 9 is a diagram illustrating a vehicle state before and after a signal. (A) is the case where the signal is blue (B), and (b) is the case where the signal is red (R). In (a), it is assumed that the vehicle group is eliminated at the signal by making the signal blue. In this case, the vehicle flows out smoothly and the distance between the vehicles is almost constant. In (b), it is assumed that the vehicle is decelerated and stopped before the signal to form a vehicle group by setting the signal to red. In this case, the inter-vehicle distance between the vehicles is dense and varies before and after the signal.

  FIG. 10 is a diagram showing a correlation map between the logarithm of the minimum covariance value for the inter-vehicle distance distribution and the logarithm of the maximum slope value for the acceleration spectrum, as in FIG. In FIG. 10, unlike FIG. 8, the horizontal axis is the logarithm of the slope maximum value, and the vertical axis is the logarithm of the minimum covariance value. FIG. 10A is a correlation map corresponding to the smooth running state without the vehicle group of FIG. 9A, and the inter-vehicle distance distribution 101 exists in a relatively unified form on the lower left side of the figure. On the other hand, FIG. 10B is a correlation map corresponding to a traveling state in which the vehicle group before and after the red signal in FIG. 9B is formed and the inter-vehicle distance is dense and varied, and the inter-vehicle distance distribution 102 is shown in FIG. It exists in a state that spreads relatively sideways in the middle and slightly upper right side. Thus, as is clear from a simple model using a traffic light, the difference in the running state of the vehicle, that is, the difference in the inter-vehicle distance distribution, in other words, the presence or absence of the formation of the vehicle group, is the logarithm of the minimum covariance of the present invention. It is possible to quantify from the correlation map between the slope and the logarithm of the slope maximum value for the acceleration spectrum.

  FIG. 11 is a diagram for explaining this quantification, and is a diagram basically showing a correlation map similar to FIG. The area indicated by reference numeral 103 corresponds to the distribution of FIG. 10A, and the area indicated by reference numeral 104 corresponds to the distribution of FIG. In other words, it can be determined that the region 103 corresponds to the critical region of FIG. 8, and the region 104 corresponds to the mixed flow state of FIG. A region indicated by reference numeral 105 in FIG. 11 is a region where the maximum value of inclination is observed. In the process of traffic congestion, first, the state shifts from the critical region state 103 to a region 105 where the slope maximum value changes suddenly. Next, if the region 105 is shifted to the region 104 corresponding to the mixed flow state, a vehicle group that is congested is formed. In order to eliminate the congested vehicle group, it is necessary to return from the area 104 to the area 103. If this flow is possible, even if a vehicle group is formed, it can be quickly eliminated.

  In the embodiment of the present invention, in order to create a flow for eliminating the vehicle group (congestion), the vehicle group end vehicle specifying means 602 indicates that the vehicle group end vehicle (the last vehicle or Identify the foremost vehicle. FIG. 12 is a diagram for explaining the specification of the vehicle at the end of the vehicle group. FIG. 12A is a diagram showing the absolute value (vertical axis) of the slope maximum value for the acceleration spectrum for each travel time step (horizontal axis). FIG. 12B is a diagram showing a correlation map between the absolute value (maximum horizontal axis) of the slope maximum value and the absolute value (vertical axis) of the minimum covariance for the acceleration spectrum.

  In FIG. 12 (a), the absolute value of the slope maximum value suddenly changes during the travel time indicated by the symbol A, indicating that there is a vehicle that triggers vehicle group fluctuation (vehicle group formation). Yes. Assuming that the vehicle is given the same symbol as vehicle A, the distribution of vehicle A corresponds to the point given the same symbol A in FIG. 12B, and the absolute value and the common value of the slope maximum values for the acceleration spectrum, respectively. The absolute value of the minimum variance has changed greatly. Therefore, it is possible to identify the vehicle that will trigger the formation of the vehicle group by grasping the point (distribution) indicated by the symbol A in FIG.

  In addition, in the case of the travel time indicated by B in FIG. 12A, the absolute value of the maximum slope value serves as a starting point, and the absolute value of the maximum slope value is obtained. It acts as a starting point for the value to decrease, indicating that there is a vehicle that triggers vehicle group fluctuation (vehicle group cancellation). Assuming that the vehicle is given the same symbol as vehicle B, the distribution of vehicle B corresponds to the point given the same symbol B in FIG. 12B, and the absolute value and covariance of the slope maximum value for the acceleration spectrum. The absolute value of the minimum value is decreasing (converging). Therefore, by grasping the point (distribution) indicated by the symbol B in FIG. 12, it becomes possible to identify the vehicle that triggers the elimination of the vehicle group.

  Since the vehicles corresponding to the vehicles A and B are generally the first or last vehicle in the vehicle group, the change in the absolute value of the covariance minimum value and / or the absolute value of the slope maximum value is thus changed. By observing the quantity, it becomes possible to specify a vehicle (the vehicle at the head or the tail of the vehicle group) that is useful for eliminating the vehicle group that is congested.

  After the vehicle useful for eliminating the congested vehicle group is identified, the driving support information providing means in FIG. 1 travels to the identified at least one vehicle (the first or last vehicle in the vehicle group) to eliminate the congested traffic. Provide support information. Specifically, for example, an instruction to increase the inter-vehicle distance is issued to the last vehicle, or an instruction to increase the vehicle speed is issued to the vehicle when the vehicle speed of the leading vehicle is slow (below a predetermined speed). Is done. The content of the support information is not limited to these and can be arbitrarily set.

  FIG. 13 is a flowchart of the driving support control according to one embodiment of the present invention. The details of each step are as described above. In step S10, the vehicle speed sensor 11 detects the acceleration of the host vehicle. In parallel, in step S11, an inter-vehicle distance from a vehicle around the host vehicle is detected based on an output signal from the radar device 12 (blocks 34 to 36 in FIG. 2). In step S12, the vehicle speed spectrum single regression maximum is performed. More specifically, the above-described inclination maximum value is calculated (blocks 31 to 33 in FIG. 2). In parallel, in step S13, the covariance value is specified. Specifically, the above-described minimum covariance is calculated (blocks 37 and 38 in FIG. 2).

  In step S14, the critical region is modeled. Specifically, a correlation map as illustrated in FIG. 8B is created (block 40 in FIG. 2). In step S15, it is determined whether or not a critical region (and its boundary) exists. The critical region is a critical region illustrated in FIGS. 7 and 8A described above. If this determination is No, the process returns to steps S12 and S13 and the subsequent flow is repeated. If the determination is Yes, a traffic jam is predicted in the next step S16 (block 41 in FIG. 2).

  In step S17, the traffic jam prediction information about the vehicle group is acquired from the traffic jam prediction result. In step S18, it is determined whether the first or last vehicle in the vehicle group indicating the start or end of the traffic jam has been identified. When this determination is Yes, in the next step S19, travel support information is provided to the identified vehicle. If the determination is No, the process ends. In addition, various control in the said vehicle can be performed as needed from the traffic jam prediction result of step S16. (Blocks 42 to 44 in FIG. 2).

  As described above, according to the embodiment of the present invention, the vehicles at both ends of the vehicle group are identified, and the vehicle group is eliminated by performing traveling support (various instructions, etc.) for eliminating the traffic congestion on the vehicle. As a result, traffic congestion can be eliminated. For example, by having the last vehicle clear the distance between the vehicles, the shock wave to the following vehicle of this vehicle can be relaxed and the vehicle group itself can be extinguished. On the other hand, for the foremost vehicle, when the speed of the vehicle is the cause of the traffic jam, it is possible to suppress the traffic jam by suppressing the maintenance and enlargement of the vehicle group by urging the vehicle speed to increase.

  The embodiment of the present invention has been described above, but the present invention is not limited to such an embodiment, and can be modified and used without departing from the spirit of the present invention.

DESCRIPTION OF SYMBOLS 10 Congestion prediction means 14 Processing apparatus 51, 53 Acceleration (power) spectrum 52, 54 Single regression line 56, 57, 58 Covariance 80 Vehicle 90 Base station server etc.

Claims (4)

Detecting the acceleration of the host vehicle;
Calculating a power spectrum corresponding to the frequency from frequency analysis of the detected acceleration;
Calculating a single regression line of the calculated power spectrum, and calculating a maximum value of a change amount of the slope of the single regression line in a predetermined frequency range as a slope maximum value;
Detecting an inter-vehicle distance between the host vehicle and a preceding vehicle;
Estimating the inter-vehicle distance distribution from the detected inter-vehicle distance using a distribution estimation method;
Calculating a minimum value of covariance from the estimated inter-vehicle distance distribution;
Estimating a forward vehicle group distribution from the correlation between the minimum value of the covariance and the slope maximum value;
Performing a traffic jam prediction in real time based on the vehicle group distribution;
A traffic jam prediction means for performing traffic jam prediction by executing
Vehicle group traffic information acquisition means for acquiring traffic jam prediction information about the vehicle group obtained by the traffic jam prediction means;
From the traffic jam information about the vehicle group, vehicle group end vehicle specifying means for specifying the vehicle group end vehicle indicating the start or end of the traffic jam,
Driving support information providing means for providing driving support information to the vehicle group end vehicle specified by the vehicle group end vehicle specifying means;
The driving support information providing means provides driving support information for eliminating traffic jams to at least one of the vehicles in the vehicle group.   The travel support system according to claim 1, wherein the travel support information for eliminating the traffic jam includes an instruction to increase a distance between the vehicles toward the end-of-car group vehicle indicating the end of the traffic jam.   The travel support information for eliminating the traffic congestion includes an instruction to increase the vehicle speed toward the vehicle end vehicle when the vehicle speed of the vehicle end vehicle indicating the start of the traffic jam is equal to or lower than a predetermined speed. The driving support system described.   The vehicle group traffic jam information acquisition means acquires the traffic information of the vehicle group from at least one of communication between a vehicle and a base station server, vehicle-to-vehicle communication, and road-to-vehicle communication. The driving support system according to any one of the above.

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