JP5783356B2 - Vehicle travel plan generation system - Google Patents

Description

  The present invention relates to a vehicle travel plan generation system, and more specifically, predicts a next stop point using statistical data, and generates a short trip travel plan from when the vehicle starts to stop. The present invention relates to a vehicle travel plan generation system.

  An example of a conventional travel plan generation device is disclosed in Patent Document 1 below. According to the technique disclosed in Patent Document 1, a travel plan with good fuel efficiency is generated in consideration of a stop scene based on a lighting cycle of a traffic signal at an intersection.

JP 2009-70101 A

  By the way, it may not be easy to actually obtain accurate switching timing data of blue, yellow, and red signal lights of all traffic signals.

  Therefore, the present invention provides a vehicular travel plan generation system that can generate a travel plan by predicting a stop point of a short trip where the host vehicle is likely to stop regardless of data of traffic signal switching timing. It is intended to provide.

In order to achieve the above object, a vehicular travel plan generation system according to the present invention includes a database storing statistical data of short trips of a vehicle, and candidates for stop points in the current short trip of the vehicle from the database. Statistical data extraction means for extracting statistical data, and stop point prediction for predicting planned stop points based on the current short trip driving conditions of the vehicle from among the stop point candidates included in the extracted statistical data It means, as the vehicle stops planned stop point possess a drive plan generating means for generating a trip plan, wherein the database includes a combination of a start point short trip and stop points, starting of each combination Stop probability indicating the proportion of vehicles that start from the point that stop at the stop point of the combination, and short trip of each combination Statistical data associated with statistical values of driving conditions is accumulated, and the statistical data extraction means extracts statistical data including a combination including a start point of the current short trip of the vehicle from the database, The stop point prediction means uses a stop probability included in the extracted statistical data as a prior probability, and is based on a statistical value of the driving condition included in the extracted statistical data and a value of the current short trip driving condition of the host vehicle. The likelihood is calculated, the probability of stopping at each combination stop point included in the extracted statistical data is calculated as the posterior probability, and the stop point with the highest posterior probability is predicted as the planned stop point It is characterized by.

The present invention thus configured predicts a planned stop point at which the host vehicle will stop next on the current short trip based on past statistical data and the current travel conditions of the host vehicle. As a result, according to the present invention, it is possible to generate a travel plan by predicting a stop point of a short trip where the host vehicle is likely to stop regardless of the traffic signal switching timing data.
Furthermore, based on the past statistical data and the current travel conditions of the host vehicle, a stop point with the highest probability that the host vehicle will stop next on the current short trip is obtained and predicted as a planned stop point. Here, the planned stop point is not simply the point with the highest stop probability that the vehicle will stop on the past statistical data, but the highest stop probability obtained based on the past statistical data and the current driving conditions of the host vehicle. It is a high point. For this reason, it is possible to obtain a stop point with a higher probability that the host vehicle will stop than when only past data is used.

In order to achieve the above object, a vehicular travel plan generation system according to the present invention includes a database storing statistical data of short trips of a vehicle, and candidates for stop points in the current short trip of the vehicle from the database. Statistical data extraction means for extracting statistical data, and stop point prediction for predicting planned stop points based on the current short trip driving conditions of the vehicle from among the stop point candidates included in the extracted statistical data Means and a travel plan generating means for generating a travel plan so that the host vehicle stops at the planned stop point, and the statistical data stored in the database is decelerated and stopped when passing. Including the data of both frequently occurring dilemma points, the stop point prediction means is the starting point of the current short trip of the own vehicle If there is dilemma point between the planned stop point, and characterized in that the dilemma point planned stop point.

The present invention thus configured predicts a planned stop point at which the host vehicle will stop next on the current short trip based on past statistical data and the current travel conditions of the host vehicle. As a result, according to the present invention, it is possible to generate a travel plan by predicting a stop point of a short trip where the host vehicle is likely to stop regardless of the traffic signal switching timing data.
Further, in the vicinity of the dilemma point, the frequency of the driver wondering whether to stop or pass the vehicle is high. At such a point, for example, the frequency of the vehicle suddenly accelerating immediately before the intersection with a traffic light or vice versa is high. In particular, when the vehicle accelerates rapidly, the fuel consumption of the vehicle deteriorates. Moreover, it may stop at the intersection immediately after passing suddenly. Therefore, by generating a travel plan with the dilemma point as a planned stop point, it is possible to avoid the occurrence of sudden acceleration and sudden stop near the dilemma point.

  In the present invention, it is preferable that the statistical data stored in the database is generated from probe information.

  Probe information refers to various data such as the position and speed of a vehicle collected from a traveling vehicle via wireless communication. By using probe information, the combination of the short trip start point and stop point from when the vehicle starts until it stops, and the percentage of vehicles that stop at each combination stop point among vehicles that start from the start point Can be accumulated and the statistical data in which the stop probability indicating the value and the statistical value of the traveling condition for each short trip of each combination are associated with each other.

In the present invention, it is preferable that the travel plan generation means sets at least one of the planned speed and the planned acceleration of the host vehicle in the path from the start point of the current short trip of the host vehicle to the planned stop point. Is generated.
As a result, a travel plan using the vehicle speed and acceleration as parameters can be generated.

  In the present invention, it is preferable that the travel plan generation unit includes an acceleration section, a constant speed section, a coasting section, and a deceleration section along a path from the current short trip start point of the host vehicle to the planned stop point. A travel plan that is set sequentially, and in which the ratio of the coasting section of all sections of the short trip is a predetermined value or more, is generated.

  Thus, the fuel consumption of the vehicle can be improved by setting the ratio of the coasting section to a predetermined value or more.

  In the present invention, it is preferable that the stop point predicting means is configured to detect the own stop point when the difference between the actual vehicle speed of the host vehicle and the vehicle speed according to the travel plan is a predetermined value or more in the acceleration section and the constant speed section. A new planned stop point is predicted based on the actual vehicle speed.

  In this way, in the acceleration section and the constant speed section, the driver's will is respected and the original travel plan is canceled to predict a new planned stop point. On the other hand, after entering the coasting section, the fuel consumption of the vehicle Will be given priority and the original travel plan will not be cancelled. As a result, both the will of the driver and the improvement of the fuel consumption of the vehicle can be achieved.

Preferably, in the present invention, when the accelerator pedal is continuously operated for a predetermined time or more in the coasting section, the stop point prediction means is a new planned stop located before the original planned stop point. Predict the location.
Thereby, the driving | operation plan along the good driver | operator's intent can be produced | generated.

  In the present invention, it is preferable that the travel plan generation means is configured such that the vehicle speed of the other vehicle preceding the host vehicle on the short trip route to the planned stop point is greater than the planned speed of the constant speed section of the initial travel plan. If it is lower, the stop point prediction means predicts a new planned stop point based on the vehicle speed of the other vehicle.

  As described above, when it is difficult to travel according to the travel plan due to the traffic situation around the host vehicle, a travel plan having a high possibility of realization can be generated by generating a new travel plan.

  Here, the preceding other vehicle may be a preceding vehicle that travels immediately before the host vehicle, or may be a vehicle that travels in front of the host vehicle. Further, the vehicle speed of the other vehicle may be directly measured by a device such as a radar mounted on the host vehicle, or may be acquired as probe information.

  According to the vehicular travel plan generation system of the present invention, it is possible to generate a travel plan by predicting a stop point of a short trip where the host vehicle is likely to stop regardless of the traffic signal switching timing data. .

It is a conceptual diagram of the driving plan for vehicles. It is a block diagram which shows the structure of the driving plan production | generation system for vehicles by 1st Embodiment of this invention. It is explanatory drawing of the start intersection and stop intersection in a short trip. (A) is a bar graph which shows the prior probability to stop at an intersection, and FIG.4 (b) is a bar graph which shows the posterior probability to stop at an intersection. It is a graph which shows the travel pattern of a travel plan. It is a flowchart explaining the process of a probe information center. It is a flowchart following FIG. It is a flowchart explaining the process of a vehicle-mounted system. It is a flowchart following FIG. It is a flowchart explaining an example of the prediction step of a planned stop intersection, and the production | generation step of a travel plan. It is a flowchart explaining the other example of the prediction step of a planned stop intersection. It is a flowchart explaining the further another example of the prediction step of a planned stop intersection.

Hereinafter, a vehicle travel plan generation system according to a first embodiment of the present invention will be described with reference to the accompanying drawings.
First, with reference to FIG. 1, a travel plan for a short trip from when the vehicle starts until it stops will be described. In the upper part of FIG. 1, signaled intersections (hereinafter referred to as intersections) A, B, and C are sequentially located along the route. In the middle of FIG. 1, an example of a travel pattern without a travel plan is indicated by a curve I. Furthermore, in the lower part of FIG. 1, an example of a travel pattern according to the travel plan is indicated by a curve II. The horizontal axis in the middle and lower stages in FIG. 1 represents the journey, and the vertical axis represents the vehicle speed.

  As indicated by the curve I, in a travel pattern without a travel plan, unnecessary acceleration / deceleration is repeated in a region surrounded by a broken line a between the intersection A and the intersection B. Such acceleration / deceleration deteriorates fuel consumption. Further, in this travel pattern, the vehicle is rapidly accelerating in a region surrounded by a broken line b near the intersection B. This sudden acceleration is a result of the vehicle trying to pass the intersection before the traffic lights at the intersection B are switched on. Such sudden acceleration also deteriorates fuel consumption. Moreover, although the vehicle has accelerated rapidly and passed through the intersection B, the vehicle has stopped at the intersection C immediately after the intersection B. For this reason, sudden acceleration near the intersection B is a useless result.

On the other hand, as shown by the curve II, in the traveling pattern according to the traveling plan, the vehicle starts from the intersection A and stops at the intersection B without unnecessary acceleration / deceleration. For this reason, the fuel consumption can be improved by traveling according to the travel plan. Furthermore, when a certain vehicle travels in a travel pattern with high fuel efficiency according to the travel plan, the subsequent vehicle of the vehicle also travels in a travel pattern with high fuel efficiency. Therefore, by generating a travel plan, it can be expected to contribute to the fuel efficiency of the entire road traffic.
The travel pattern of the travel plan is not limited to the one that suppresses fuel consumption. For example, a traveling pattern that can sufficiently exhibit the acceleration performance of the vehicle may be used.

  Next, the configuration of the vehicle travel plan generation system according to the embodiment of the present invention will be described with reference to FIG. FIG. 2 is a block diagram illustrating a configuration of the vehicle travel plan generation system according to the embodiment.

  As shown in FIG. 2, the vehicle travel plan generation system according to the embodiment includes a probe information center 1 and an in-vehicle system 2. The probe information center 1 includes a communication unit 11 that communicates probe information with a vehicle, a server 12 that processes probe information and generates statistical data, and a database 13 that accumulates statistical data.

  As shown in Table 1 below, the database 13 includes a combination of a short trip start intersection and a stop intersection from when the vehicle starts until it stops, and a combination of vehicles starting from the start intersection of each combination. Statistical data in which a stop probability indicating the proportion of vehicles that stop at the stop intersection is associated with a statistical value of a short trip traveling condition of each combination is accumulated.

  In Table 1 above, with respect to the combination of the starting intersection A and the stopping intersection B, the stop probability “0.04” indicating the proportion of the vehicles that have started from the starting intersection A and stop at the intersection B, and the combination Are associated with statistical values of the short trip driving conditions. Statistical values (prediction parameters) of driving conditions include the average value of the distance from the starting intersection after the vehicle starts, the average value of the elapsed time from the start to the end of acceleration, and the vehicle speed at the end of acceleration. Is the average value. Furthermore, you may add the average value of the vehicle speed during the constant speed driving | running | working after completion | finish of acceleration, and the average value of the elapsed time from a signal change. In addition, the statistical data may include an average, variance, covariance matrix, and determinant as prediction parameters for each combination.

  The in-vehicle system 2 includes a communication unit 21 that communicates probe information with the probe center 1, and a plan generation unit 22 that generates a travel plan based on accumulated data in the database 13. The plan generation unit 22 extracts, from the database 13, a statistical data extraction unit 221 that extracts related statistical data including a combination including the start point of the current short trip of the host vehicle, the related statistical data, and the current short trip of the host vehicle. Based on the travel condition value, the stop probability predicting unit 222 calculates the stop probability of stopping at each stop point of each combination included in the extracted statistical data, and predicts the stop point with the highest stop probability as the planned stop point. And a travel plan generation unit 223 that generates a travel plan so that the host vehicle stops at the planned stop point.

  The statistical data extraction unit 221 extracts, from the database 13 via the communication unit 21, related statistical data including a combination including the start point of the current short trip of the host vehicle. As shown in FIG. 3, when the host vehicle starts from the intersection A, the statistical data of combinations 1 to 4 including the departure intersection A among the statistical data of Table 1 is extracted from the database 13. In the extracted related statistical data, the stop probabilities of stopping at the intersections B to E are “0.04”, “0.14”, “0.09”, and “0.4”, respectively. These stop probabilities are obtained by statistically processing probe information of a large number of vehicles collected by the probe information server 1, starting from the intersection A, and based on the frequency of vehicles stopped at the intersections B to E, respectively. is there.

  Here, in FIG. 4A, the stop probability of stopping at the intersections B to E is schematically shown as a bar graph. As shown in FIG. 4A, in the stop probability, the probability of stopping at the intersection C is the highest.

  The stop point prediction unit 222 calculates a stop probability of stopping at each stop point of each combination included in the extracted statistical data based on the related statistical data and the value of the current short trip traveling condition of the host vehicle, The stop point with the highest stop probability is predicted as the planned stop point. In calculating the stop probability, Bayes' theorem is used.

  Bayes' theorem is expressed by Equation 1 below.

  In Equation 1 above, P (H1) represents the probability of occurrence of H1 (prior probability), P (D | H1) represents the probability of occurrence of D when the event H1 occurs (likelihood), and , P (H1 | D) represents the probability (posterior probability) of occurrence of H1 when the event D occurs.

  Assuming a normal distribution of prediction parameters, the likelihood is expressed by the following equation, where μ is an average vector, S is a covariance matrix, and x is a data vector obtained during travel.

  The prior probability P (H1) corresponds to the stop probability that the vehicle stops at the intersections H1 to Hn in the extracted related statistical data.

  In addition, the likelihoods P (D | H1) to P (D | Hn) correspond to the probability of being an average value of the traveling conditions when the vehicles stop at the respective intersections H1 to Hn. As an average value (prediction parameter) of driving conditions, for example, an average value of the distance from the start intersection at the end of acceleration, an average value of elapsed time from the start to the end of acceleration, an average value of the vehicle speed at the end of acceleration, Further, this corresponds to the probability of the average value of the vehicle speed during constant speed travel after the end of acceleration.

  The posterior probabilities P (H1 | D) to P (Hn | D) obtained from the prior probabilities and likelihoods using Bayes' theorem are the extracted statistical data and the current short trip travel of the host vehicle. This corresponds to the stop probability of stopping at each combination stop point included in the extracted statistical data based on the condition value.

  FIG. 4B shows the probability of stopping at the intersections B to E. As shown in FIG. 4B, the probability of stopping at the intersection B is the highest in the stop probability. Therefore, the intersection B is predicted as a planned stop point.

  The travel plan generation unit 223 generates a short trip travel plan so that the host vehicle started from the intersection A stops at the intersection B. In the travel plan, the travel pattern is set by setting the planned speed and the planned acceleration of the host vehicle in the path from the start intersection A of the current short trip of the host vehicle to the planned stop intersection B.

  FIG. 5 shows an example of a travel plan. The horizontal axis in FIG. 5 represents the journey, and the vertical axis represents the vehicle speed. The traveling pattern indicated by curve III in FIG. 5 is an acceleration section L1, a constant speed section L2, a coasting section L3, and a deceleration section L4 along the route from the start intersection A of the current short trip of the host vehicle to the planned stop intersection B. Are set sequentially. In the acceleration section L1, the vehicle is accelerated at a predetermined acceleration, and in the constant speed section L2, the vehicle travels at a predetermined constant vehicle speed. In the coasting section L3, the vehicle travels in a coasting state without depressing the accelerator. Then, the vehicle is decelerated at a predetermined deceleration from before the stop intersection B and stops at the stop intersection B.

  For example, when the planned stop intersection is determined based on the acceleration end point, (1) the cruise speed in the constant speed section L2, (2) the speed at the end of the coasting section L3, (3) the deceleration in the coasting section L3, And (4) A deceleration profile during deceleration by the brake in the deceleration section L4 is set, and a speed profile constituting the travel pattern is generated.

  Here, (1) the cruising speed in the constant speed section L2 may be set to the average value of the cruising speed in the accumulated data of the vehicle traveling in the same short trip section as the current short trip of the host vehicle. In this way, it is considered that if the cruise speed according to the average value of the cruise speeds in the accumulated data is set, it is easy to stop at each planned stop position.

  Further, (2) the speed at the end of the coasting section L3 may be set in advance at a predetermined speed (for example, 30 km / h). Further, (3) the deceleration in the coasting section L3 may be set in advance to a predetermined deceleration (for example, 0.04G). Further, (4) a predetermined deceleration (for example, 0.15 G) may be set in advance in the deceleration section L4.

  And the ratio of the coasting section L3 of all the sections of the short trip between the intersection A and the intersection B is more than a predetermined value (for example, 50%). Thus, the fuel efficiency can be improved by increasing the ratio of the coasting section L3.

  Further, the in-vehicle system 2 includes an information providing unit 23 that provides the driver with a travel pattern according to the travel plan, and an accelerator pedal for guiding the driver to travel with the travel pattern according to the travel plan. The pedal control unit 24 for adjusting the repulsive force and the accelerator pedal 25 capable of adjusting the repulsive force are provided. For example, the information providing unit 23 displays the acceleration section L1, the constant speed section L2, the coasting section L3, and the deceleration section L to the driver according to the short trip traveling pattern, or notifies the driver by voice. Good. Further, the pedal control unit 24 increases the repulsive force of the accelerator pedal 25 during traveling in the constant speed section L2 than during traveling in the acceleration section L1. Furthermore, the pedal control unit 24 further increases the repulsive force of the accelerator pedal 25 during traveling in the coasting section L3 than during traveling in the constant speed section L2. Thereby, the driver can be guided to drive according to the travel pattern of the travel plan.

  The in-vehicle system 2 includes a radar 26 for measuring the speed of the preceding vehicle, a GPS (global positioning system) 27 for measuring the position of the vehicle, and a storage unit 28 in which map information of the car navigation system is stored. It has.

Next, an operation example of the vehicle travel plan generation system according to the embodiment will be described.
First, the processing of the probe information center 1 will be described with reference to the flowcharts of FIGS. The probe information center 1 collects probe information (probe data) from a large number of vehicles via the communication unit 11 and stores it in the database 13 (S601).
The server 12 of the probe information center 1 extracts probe data for each trip from the accumulated probe information (S602).

  Further, the server 12 divides the trip probe data for each short trip from when the vehicle starts to the next stop, and generates short trip data (ST data) (S603). Here, after the vehicle speed V becomes the start determination speed Vs or more (V ≧ Vs), after the vehicle speed V becomes the minimum arrival speed Vmin or more (V ≧ Vmin), the vehicle speed becomes the stop determination speed Ve or less (V ≦ V A short trip is taken until Ve). For example, the time until the vehicle speed becomes V = 0 again from V = 0 may be extracted as a short trip.

  Next, the server 12 specifies a start intersection and a stop intersection for each short trip (S604). In specifying the intersection, the position of the vehicle may be specified from the GPS mounted on the vehicle, and the position of the vehicle may be checked against a map database.

The server 12 performs the following processing on the data for each short trip.
When the start intersection and the stop intersection of the short trip data are different (in the case of “Yes” in S605), the server 12 starts time t0, the travel distance 10 of the vehicle at the start, and the distance D to the start intersection. (S606).

  Next, the time ta at the end of acceleration of the vehicle, the travel distance la, and the vehicle speed Va are obtained (S607). The conditions for determining the end of acceleration are preferably V ≧ Vmin and acceleration <0.

Next, the travel distance lp when the vehicle passes the starting intersection is obtained (S608).
The distance D obtained in step S606 may be obtained by using a car navigation system, or may be obtained as a difference (lp−l0) between the travel distance l0 when starting and the travel distance lp when passing the intersection. Good.

  Next, the travel distance La from the starting intersection at the end of acceleration of the vehicle and the elapsed time Ta from the signal change at the end of acceleration are obtained. The distance La from the starting intersection may be obtained as a difference (la-lp) between the traveling distance la at the end of acceleration and the traveling distance lp when passing through the starting intersection. The elapsed time Ta from the signal change at the end of the acceleration is the elapsed time (t0−ta) from the time t0 at the start to the time ta at the end of the acceleration, and the distance D to the intersection is the start wave propagation velocity Ws. It may be obtained by adding the divided value (D / Ws).

  Next, the travel distance le at the deceleration start point before the stop intersection and the time te that has passed through the deceleration start point are obtained (S610). The determination of the start of deceleration may be obtained as a point where acceleration> 0, going back from the stop intersection.

  Next, an average vehicle speed Vl from the end of acceleration to the start of deceleration is obtained (S701). The average vehicle speed is the difference between the distance le at the deceleration start point and the distance la at the end of acceleration (le-la), and the difference between the passage time te at the deceleration start point and the time ta at the end of acceleration (te-ta). It may be obtained as a value obtained by dividing ((le-la) / (te-ta)).

  Next, the accumulated short trip data is grouped (grouped) for each same starting intersection (S702).

  Then, after a predetermined number or more of short trip data has been accumulated (in the case of “Yes” in S704), statistical processing of the grouped short trip data is performed (S705). The average, variance, covariance matrix, and inverse matrix of the various parameters described above may be obtained by statistical processing.

Next, various statistical data obtained by the statistical processing are arranged for each combination (pair) of the outgoing intersection and the stop intersection (S706).
In this way, the statistical data shown in Table 1 is obtained using the probe information.

Next, processing of the in-vehicle system 2 will be described with reference to the flowcharts of FIGS. 8 and 9. The in-vehicle system 2 obtains the identifier (ID) of the intersection immediately before the own vehicle, the distance D, and the travel distance 10 at the time of the current stop (S802) while the own vehicle is stopped (“Yes” in S801). In the example shown in FIG. 4A, the host vehicle stops just before the intersection A. Therefore, the in-vehicle system 2 obtains the current position of the host vehicle by the GPS 27, compares the current position with the map data stored in the storage unit 28, and obtains the identifier of the immediately preceding intersection A. Thereby, the intersection A becomes a start intersection.
In addition, when the vehicle speed V of the own vehicle is equal to or lower than the stop determination speed Ve (V ≦ Ve) and the brake of the own vehicle is applied, it is preferable to determine that the own vehicle is stopped.

  Next, the statistical data extraction unit 221 includes the start intersection A of the current short trip of the host vehicle among the statistical data stored in the database 13 with respect to the probe information center 1 via the communication unit 21. The related statistical data including the combination is requested (S803). Specifically, related data including the start intersection A among the statistical data shown in Table 1 is requested.

Next, when the host vehicle starts (in the case of “Yes” in S804), the time t0 at the time of start is obtained (S805).
Subsequently, when the host vehicle passes through the start intersection A (in the case of “Yes” in S805), the travel distance lp when the vehicle passes the start intersection A is obtained (S806).

Next, when the acceleration of the host vehicle is completed (in the case of “Yes” in S808), the time ta, the travel distance la, and the vehicle speed Va when the acceleration ends are obtained (S809).
Further, the distance La from the starting intersection A at the end of acceleration and the elapsed time Ta from the signal change are obtained (S810).

  Next, when related statistical data including a combination including the start intersection A of the current short trip of the vehicle is extracted and transmitted from the database 13 of the probe information center 1 via the communication unit 21 (" In the case of “Yes”), the stop point prediction unit 222 stops at each stop intersection of each combination included in the extracted statistical data based on the related statistical data and the value of the current short trip driving condition of the host vehicle. Calculate the stop probability. And the stop intersection with the highest stop probability is predicted as a planned stop point (S901).

  In this embodiment, in predicting the planned stop intersection, the various running conditions of the host vehicle obtained in the previous steps, that is, the distance La from the start intersection A at the end of acceleration, and the elapsed time Ta from the signal change, Using the velocity Va at the end of acceleration as a prediction parameter, the stop probability (post-probability) at each stop intersection is calculated using the Bayes' theorem shown in Equation 1. When the posterior probability of stopping at the intersection B becomes the maximum, the intersection B is predicted as a planned stop intersection.

Then, the travel plan generation unit 223 sequentially sets the acceleration section L1, the constant speed section L2, the coasting section L3, and the deceleration section L4 as shown in FIG. 5 so that the host vehicle stops at the planned stop intersection B. A travel plan of the travel pattern thus created is generated (S902).
In addition, the information presentation unit 23 informs the driver of the travel pattern of the travel plan and guides the driver to drive the vehicle according to the travel pattern of the travel plan.

  By the way, the driver does not always drive the vehicle according to the travel plan. As a result, the actual vehicle speed of the host vehicle may differ from the vehicle speed according to the travel plan. Therefore, in the acceleration section L1 and the constant speed section L2, that is, until the start point P2 of the coasting section L3 is reached (in the case of “No” in S906), the difference between the actual vehicle speed of the host vehicle and the vehicle speed according to the travel plan is If the value is equal to or greater than the predetermined value (“Yes” in S903), the stop point prediction unit 222 predicts a new planned stop intersection based on the actual vehicle speed of the host vehicle (S904).

  Next, the travel plan generation unit 223 cancels the initial travel plan and generates a short trip travel plan to a new planned stop intersection (S905).

  Next, when the start point P2 of the coasting section L3 is reached (in the case of “Yes” in S906), when the following vehicle is separated by a certain distance or more (in the case of “Yes” in S907), the plan generating unit 22 The coasting start is instructed (S908).

  On the other hand, when the starting point P2 of the coasting section L3 is reached (in the case of “Yes” in S906), the coasting start point is changed when the following vehicle is a certain distance or less (in the case of “No” in S907) ( S909). If coasting is started when the following vehicle is approaching, the following vehicle may collide. For this reason, it is possible to prevent the rear-end collision by delaying the coasting start.

  When the coasting start position after the change is reached (S910), the plan generation unit 22 instructs the coasting start (S908). In response to the coasting start instruction, the information presentation unit 23 instructs the driver to start coasting (S908). Further, the pedal control unit 24 increases the repulsive force of the accelerator pedal 25 and prompts the driver to perform a coasting operation without depressing the accelerator pedal 25.

  By the way, even after the coasting start instruction, when the driver operates the accelerator pedal 25 continuously for a predetermined time or more (in the case of “No” in S912), the stop point prediction unit 222 respects the driver's will. Thus, a new planned stop point located ahead of the original planned stop point is predicted.

  Then, when the vehicle stops (in the case of “Yes” in S913), the short trip ends.

Next, a second embodiment of the vehicle travel plan generation system of the present invention will be described.
The vehicle travel plan generation system of the second embodiment is basically the same as that of the first embodiment except for the processing contents of steps S901 to S902 in FIG. In the second embodiment, the travel plan is generated in consideration of the vehicle speed of the preceding vehicle.

Hereinafter, the process of the changed part in the second embodiment will be described with reference to the flowchart of FIG.
As shown in FIG. 10, in the second embodiment, similarly to step S901 of the first embodiment, a predicted stop intersection is predicted using a prediction parameter at the end of acceleration (S1001).

  Next, the planned speed (cruising speed) in the constant speed section is set from the prediction parameters (S1002).

  Next, when there is a preceding vehicle on the short trip to the planned stop point (“Yes” in S1003), the vehicle speed of the preceding vehicle is measured by a radar or the like (S1004).

  Next, when the preceding vehicle field speed is lower than the planned speed of the constant speed section of the initial travel plan of the host vehicle (in the case of “Yes” in S1006), the stop point prediction unit 221 determines the vehicle speed of the preceding vehicle, It is set as a new planned speed (cruising speed) in the constant speed section of the host vehicle (S1007).

  Next, the stop point prediction unit 221 predicts a new planned stop intersection based on the newly set planned speed (S1008).

Next, similarly to step S902 of the first embodiment, the travel plan generation unit 223 generates a travel plan based on the newly set planned speed and the new planned stop intersection (S1009).
As described above, when it is difficult to travel according to the travel plan due to the traffic situation around the host vehicle, a travel plan having a high possibility of realization can be generated by generating a new travel plan.

Next, a third embodiment of the vehicle travel plan generation system of the present invention will be described.
The vehicle travel plan generation system of the third embodiment is basically the same as that of the first embodiment except for the processing contents of steps S901 to S902 in FIG. In the third embodiment, a travel plan is generated in consideration of the flow of surrounding traffic.

Hereinafter, with reference to the flowchart of FIG. 11, the process of the change part in 3rd Embodiment is demonstrated.
As shown in FIG. 11, in the third embodiment, similarly to step S <b> 901 of the first embodiment, after predicting the planned stop intersection, the vehicle speed of the other vehicle preceding the host vehicle is acquired (S <b> 1101). Here, the vehicle speed of the other vehicle on the road to the planned stop intersection in front of the host vehicle may be acquired by VICS (registered trademark) ( vehicle information and communication system) and real-time probe information.

VICS (registered trademark) is an information communication system that transmits road traffic information such as traffic jams, traffic regulations, required time, traffic obstacles, and parking lots in real time and displays them in characters and figures on in-vehicle devices such as car navigation systems. Say.

  Next, when the vehicle speed (link flow velocity) of the preceding other vehicle is lower than the planned speed of the constant speed section of the initial travel plan, the stop point prediction unit 222 determines the vehicle speed (link flow velocity) of the preceding other vehicle. As a planned speed of a constant speed section, a new planned stop intersection is predicted (S1103).

  Next, the travel plan generation unit 223 generates a new travel plan based on the vehicle speed (link flow velocity) of the preceding other vehicle and the new planned stop intersection (S1104). The new travel plan also has a travel pattern in which an acceleration section, a constant speed section, a coasting section, and a deceleration section are sequentially set. In the new travel plan, the planned speed of the constant speed section is the vehicle speed of the preceding other vehicle, and the host vehicle stops at the new planned stop intersection.

Subsequently, the process returns to step S903 in FIG.
As described above, when it is difficult to travel according to the travel plan due to the traffic situation around the host vehicle, a travel plan having a high possibility of realization can be generated by generating a new travel plan.

Next, a fourth embodiment of the vehicle travel plan generation system of the present invention will be described.
The vehicle travel plan generation system of the fourth embodiment is basically the same as that of the first embodiment except for the contents of statistical data in the database 13 and the processing contents of steps S901 to S902 in FIG. In the fourth embodiment, the travel plan is generated in consideration of the dilemma at the turn of the signal when passing the intersection.

  As shown in Table 2 below, in the fourth embodiment, the statistical data stored in the database 13 passes when the vehicle accelerates over a predetermined value and passes and when the vehicle stops with a sudden deceleration over a predetermined value. Includes data for both frequent dilemma points.

  Further, the database 13 accumulates the characteristic values of the driving conditions as dilemma data separately from the above Table 2 for the combination in which the dilemma intersection is indicated. In the dilemma data, the feature amount of a short trip in which a dilemma (rapid acceleration or rapid deceleration) occurs is calculated. Examples of the feature amount include a distance from the start intersection at the end of acceleration, an elapsed time, and a vehicle speed.

  Hereinafter, with reference to the flowchart of FIG. 12, the process of the change part in 4th Embodiment is demonstrated.

  In the fourth embodiment, when a planned stop intersection is predicted in the same manner as in step S901 of the first embodiment, a dilemma point exists between the current short trip start point of the host vehicle and the planned stop point ( In the case of “Yes” in S1201), the similarity with the dilemma data stored in the database 13 is calculated (S1202). In calculating the similarity with the dilemma data, for example, a difference between one or more feature amounts accumulated in the dilemma data and the value of the current running condition of the host vehicle is calculated.

  Next, when the similarity is equal to or higher than the reference value (in the case of “Yes” in S1203), the dilemma intersection is set as a planned stop intersection (S1204). For example, when the difference calculated in the previous step is less than or equal to a predetermined reference value, it is determined that the similarity is greater than or equal to the reference value.

  For example, in the case of the combination “2” of the starting intersection “A” and the stop intersection “C” in Table 1 above, the dilemma intersection “B” is registered. In this case, for example, the difference between the distance from the starting intersection at the end of acceleration accumulated as dilemma data and the distance from the starting intersection at the end of acceleration under the current running condition of the host vehicle is less than a predetermined reference value. In some cases, the dilemma intersection “B” is set as a new planned stop intersection.

Subsequently, the process returns to step S903 in FIG.
Thus, by generating a travel plan with the dilemma intersection as a planned stop intersection, it is possible to avoid sudden acceleration and sudden stoppage near the dilemma intersection.

  In the above-mentioned embodiment, although the example which comprised this invention on the specific conditions was demonstrated, this invention can perform a various change and combination, and is not limited to this. For example, in the above-described embodiment, an example in which the start point and the stop point are intersections with traffic lights has been described. However, in the present invention, the start point and the stop point are not limited thereto. For example, a point with a high traffic frequency may be set as a start point or a stop point.

  In the above-described embodiment, the example of generating the travel plan having the travel pattern in which the acceleration section, the constant speed section, the coasting section, and the deceleration section are sequentially set has been described. However, the travel of the travel plan generated by the present invention is described. The pattern is not limited to this.

DESCRIPTION OF SYMBOLS 1 Probe information center 2 In-vehicle system 11 Communication part 12 Server 13 Database 21 Communication part 22 Plan generation unit 23 Information presentation part 24 Pedal control part 25 Accelerator pedal 26 Radar 27 GPS
28 storage unit 221 statistical data extraction unit 222 stop point prediction unit 223 travel plan generation unit

Claims (8)

A database that accumulates statistical data on past vehicle short trips,
Statistical data extracting means for extracting statistical data including candidates for stopping points in the current short trip of the vehicle from the database;
Out of the candidate stop points included in the extracted statistical data, stop point prediction means for predicting the planned stop point based on the current short trip driving condition of the vehicle,
Travel plan generating means for generating a travel plan so that the host vehicle stops at the planned stop point;
I have a,
The database includes a combination of a short trip start point and a stop point, a stop probability indicating a ratio of vehicles that stop at the combination stop point among vehicles started from the start point of each combination, and a short trip for each combination. Statistical data that correlates with statistical values of driving conditions for
The statistical data extracting means extracts, from the database, statistical data including a combination including a start point of a current short trip of the host vehicle,
The stop point prediction means uses the stop probability included in the extracted statistical data as a prior probability, the statistical value of the driving condition included in the extracted statistical data, and the value of the current short trip driving condition of the host vehicle. The likelihood is calculated based on the above, the probability of stopping at each combination stop point included in the extracted statistical data is calculated as the posterior probability, and the stop point with the highest posterior probability is predicted as the planned stop point A vehicular travel plan generation system characterized by: A database that accumulates statistical data on past vehicle short trips,
Statistical data extracting means for extracting statistical data including candidates for stopping points in the current short trip of the vehicle from the database;
Out of the candidate stop points included in the extracted statistical data, stop point prediction means for predicting the planned stop point based on the current short trip driving condition of the vehicle,
Travel plan generating means for generating a travel plan so that the host vehicle stops at the planned stop point;
Have
The statistical data stored in the database includes data of dilemma points that are frequently used both when accelerating and passing and when decelerating.
The stop point prediction means predicts the dilemma point as a planned stop point when a dilemma point exists between the start point of the current short trip of the host vehicle and the planned stop point.
A vehicle travel plan generation system characterized by the above. The statistical data accumulated in the database is generated from probe information.
The vehicle travel plan generation system according to claim 1, wherein the vehicle travel plan generation system is provided. The travel plan generating means generates a travel plan that sets at least one of a planned speed and a planned acceleration of the host vehicle in a journey from the start point of the current short trip of the host vehicle to a planned stop point. The vehicle travel plan generation system according to any one of claims 1 to 3. The travel plan generation means is a travel plan in which an acceleration section, a constant speed section, a coasting section, and a deceleration section are sequentially set along a path from the start point of the current short trip of the host vehicle to the planned stop point. The vehicle travel plan generation system according to claim 4, wherein a travel plan in which a ratio of the coasting section of all sections of the short trip is a predetermined value or more is generated. In the acceleration section and the constant speed section, when the difference between the actual vehicle speed of the host vehicle and the vehicle speed according to the travel plan is equal to or greater than a predetermined value, the stop point prediction means is based on the actual vehicle speed of the host vehicle. 6. The vehicle travel plan generation system according to claim 5, wherein a new planned stop point is predicted. In the coasting section, when the accelerator pedal is operated continuously for a predetermined time or more, the stop point prediction means predicts a new planned stop point that is located before the original planned stop point. The vehicle travel plan generation system according to claim 5 or 6. The travel plan generation means includes
When the vehicle speed of the other vehicle preceding the host vehicle on the short trip route to the planned stop point is lower than the planned speed of the constant speed section of the original travel plan, the stop point predicting means 8. A vehicle travel plan generation system according to claim 5, wherein a new planned stop point is predicted based on the vehicle speed of the vehicle.

Images