Vehicle information processing system, vehicle information processing method, and non-transitory storage medium

Abstract

The present disclosure relates to a vehicle information processing system, a vehicle information processing method, and a non-transitory storage medium. The vehicle information processing system includes a vehicle information processing device including one or more processors. The one or more processors are configured to receive input information including information on wheel speeds of a vehicle, and calculate a speed of the vehicle traveling on a highway as a feature by using the input information. The input information is information received in a period of time in which a predetermined condition is satisfied in a period of time in which the one or more processors receive the input information. The predetermined condition indicates that the vehicle is traveling on the highway.

Description

Technical Field

[0001] This disclosure relates to techniques for estimating highway congestion using information obtained from vehicles. Background Technology

[0002] There are techniques for predicting the required travel time of a vehicle from an entrance toll station to an exit toll station installed on a highway, etc., by using data on the time when a vehicle passes through an entrance toll station on a highway and data on the time when a vehicle passes through an exit toll station on a highway. For example, Japanese Unexamined Patent Application Publication No. 2002-298282 (JP 2002-298282 A) discloses such a technique: storing a required travel time pattern obtained daily by calculating actual values ​​of the required travel time using data on the time of passage of entrance and exit toll stations, and predicting the required travel time from the required travel time pattern of the predicted day based on the traffic conditions of the predicted day. Summary of the Invention

[0003] However, it may be impossible to accurately estimate the required travel time on highways and similar routes because travel time varies greatly depending on factors such as road congestion. Therefore, data on the passage times of entrance and exit toll plazas as described in JP 2002-298282 A may not be sufficient to accurately estimate the required travel time.

[0004] This disclosure provides a vehicle information processing system, a vehicle information processing method, and a non-transitory storage medium capable of accurately estimating the required travel time of a vehicle.

[0005] The first aspect of this disclosure is a vehicle information processing system. The vehicle information processing system includes a vehicle information processing device, which includes one or more processors. The one or more processors are configured to receive input information including information about the wheel speeds of a vehicle, and to calculate, using the input information, the speed of the vehicle traveling on a highway as a characteristic. The input information is information received within a time period during which the one or more processors receive the input information, and the predetermined conditions indicate that the vehicle is traveling on the highway.

[0006] According to the first approach, the speed of vehicles traveling on the highway is calculated as a feature. Therefore, for example, the congestion status of the highway can be estimated using this feature. Consequently, the required travel time can be accurately predicted based on the congestion status.

[0007] In the first approach, the input information may include information about the wheel speeds of the left and right wheels of the vehicle. The predetermined condition may include a condition that the driving state continues for a predetermined time or more. The driving state may be a state in which the vehicle's speed is higher than a first threshold and the difference between the wheel speeds of the left and right wheels is less than a second threshold.

[0008] Based on the above configuration, the wheel speeds of the left and right wheels of the vehicle can be used to accurately determine whether the vehicle is traveling on a highway.

[0009] In the first scheme, the one or more processors can be configured to generate a congestion level indicating the degree of congestion on the highway by using a distribution of multiple features calculated over a time period that satisfies the predetermined conditions.

[0010] Based on the above configuration, when a vehicle is traveling on a highway, the congestion level, indicating the highway's congestion status, can be accurately estimated by using the distribution of features. Furthermore, valuable information for users, such as the highway congestion status, can be generated by performing concealment (e.g., statistical quantification) to prevent individuals from being identified.

[0011] In the first scheme, the one or more processors can be configured to calculate the standard deviation of the vehicle's speed calculated multiple times within a time period that meets predetermined conditions, and when the standard deviation is greater than a threshold, set the congestion level as a value indicating highway congestion.

[0012] Based on the above configuration, the congestion status of highways can be accurately estimated by using the standard deviation of vehicle speed.

[0013] In the first embodiment, the one or more processors are configured to set the congestion level to a value indicating highway congestion when the proportion is less than a threshold during a time period that meets the predetermined conditions. The proportion may be the percentage of the vehicle's speed under cruise control that reaches a target speed set in the cruise control.

[0014] Based on the above configuration, the congestion status of highways can be accurately estimated by the proportion of vehicles using speeds that reach the target speed.

[0015] In the first embodiment, the one or more processors may be configured to send information about the congestion level to the outside of the vehicle.

[0016] Based on the above configuration, since the vehicle can send information about highway congestion to the outside of the vehicle, the utility value of the information about highway congestion can be increased.

[0017] In the first embodiment, the vehicle information processing system may further include a prediction device. The prediction device may be configured to predict the required travel time of a vehicle within a predetermined section of the highway using information from a plurality of vehicles equipped with the vehicle information processing device, and to predict the required travel time based on the proportion of vehicles with a predetermined level of congestion among the vehicles.

[0018] Based on the above configuration, the required travel time for a vehicle within a section of the highway can be accurately predicted by using information on congestion from multiple vehicles.

[0019] The second aspect of this disclosure is a vehicle information processing method. The vehicle information processing method includes: receiving input information including information about the wheel speeds of a vehicle; and calculating, using the input information, the speed of the vehicle traveling on a highway as a feature. The input information is information received within a time period during which predetermined conditions are met. The predetermined conditions indicate that the vehicle is traveling on the highway.

[0020] The third aspect of this disclosure is a non-transitory storage medium that stores instructions executable by one or more processors in a computer, causing the processors to perform functions. These functions include: receiving input information including information about the wheel speed of a vehicle; and calculating, using the input information, the speed of the vehicle traveling on a highway as a characteristic. The input information is information received within a time period during which predetermined conditions are met. The predetermined conditions indicate that the vehicle is traveling on the highway.

[0021] According to the second approach, the speed of vehicles traveling on the highway is calculated as a feature. Therefore, for example, this feature can be used to estimate the congestion status of the highway. Consequently, the required travel time can be accurately predicted based on the congestion status.

[0022] According to the third approach, the speed of vehicles traveling on the highway is calculated as a feature. Therefore, for example, this feature can be used to estimate the congestion level of the highway. Consequently, the required travel time can be accurately predicted based on the congestion level.

[0023] According to the first, second, and third embodiments of this disclosure, a vehicle information processing system, a vehicle information processing method, and a non-temporary storage medium capable of accurately estimating the required travel time of a vehicle can be provided. Attached Figure Description

[0024] The features, advantages, and technical and industrial significance of exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, wherein like reference numerals denote like elements, and wherein:

[0025] Figure 1 An example configuration of a vehicle information management system is shown;

[0026] Figure 2 An example configuration of a vehicle information processing device according to an embodiment is shown;

[0027] Figure 3 An example of the processing performed by the second processing unit in the embodiment is shown;

[0028] Figure 4 An example of the processing performed by the third processing unit in the embodiment is shown;

[0029] Figure 5 This is a coordinate graph showing an example of the distribution of vehicle speed when a vehicle is traveling on an uncongested highway;

[0030] Figure 6 This is a coordinate graph showing an example of the distribution of vehicle speed when a vehicle is traveling on a congested highway;

[0031] Figure 7 This is a coordinate graph illustrating an example of how the required travel time within a predetermined interval changes relative to time.

[0032] Figure 8 This is a flowchart illustrating an example of the processing performed by the second processing unit of the brake electronic control unit (ECU);

[0033] Figure 9 This is a flowchart illustrating an example of the processing performed by the third processing unit of the braking ECU;

[0034] Figure 10 This is a flowchart illustrating an example of processing performed by a data center; and

[0035] Figure 11 This is a flowchart illustrating an example of the processing performed by the third processing unit of the brake ECU in the variant example. Detailed Implementation

[0036] In the following, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. Throughout the drawings, the same or corresponding parts are indicated by the same reference numerals and their descriptions will not be repeated.

[0037] Figure 1 This shows an example of the configuration of the vehicle information management system 1. For example... Figure 1As shown, in this embodiment, the vehicle information management system 1 includes multiple vehicles 2 and 3, a communication network 6, a base station 7, a data center 100, and an electronic toll collection (ETC) communication device 110.

[0038] Vehicles 2 and 3 can be any vehicle capable of communicating with data center 100. For example, vehicles 2 and 3 can be vehicles using an engine as a drive source, pure electric vehicles using an electric motor as a drive source, or hybrid vehicles including both an engine and an electric motor and using one or both as drive sources. Although for ease of explanation... Figure 1 Only two vehicles, 2 and 3, are shown, but the number of vehicles is not specifically limited to two and can be more than three.

[0039] The vehicle information management system 1 is configured to obtain predetermined information from vehicles 2 and 3 configured to communicate with the data center 100, and to manage the obtained information.

[0040] Data center 100 includes a control device 11, a storage device 12, and a communication device 13. The control device 11, storage device 12, and communication device 13 are connected to each other via a communication bus 14, enabling these devices 11, 12, and 13 to communicate with each other.

[0041] Although not shown in the figure, the control device 11 includes a central processing unit (CPU), memory such as read-only memory (ROM) and random access memory (RAM), and input and output ports for inputting and outputting various signals. The various controls executed by the control device 11 are performed through software processing, that is, by the CPU reading and executing programs stored in the memory. The various controls executed by the control device 11 can also be implemented by a general-purpose server (not shown) that executes programs stored in a storage medium. However, the various controls executed by the control device 11 do not necessarily need to be performed through software processing and can be performed using dedicated hardware (electronic circuitry).

[0042] Storage device 12 stores predetermined information about vehicles 2 and 3 configured to communicate with data center 100. This predetermined information includes, for example, information about the characteristics of each vehicle 2 and 3, which will be described later, and information identifying each vehicle 2 and 3 (hereinafter referred to as vehicle identifier (ID)). The vehicle ID is unique information assigned to each vehicle. Data center 100 can identify the sending vehicle using its vehicle ID.

[0043] The communication device 13 enables bidirectional communication between the control device 11 and the communication network 6. The data center 100 can communicate with multiple vehicles, including vehicles 2 and 3, via the base station 7 on the communication network 6 using the communication device 13.

[0044] ETC communication device 110 includes, for example, communication devices installed at the entrances and exits of highways (including, for example, toll roads such as toll highways). More specifically, ETC communication device 110 includes, for example, communication devices installed at toll booths located at interchanges or intersections connecting highways and local roads or other highways.

[0045] ETC communication device 110 includes, for example, a first communication device 110a installed on a road at the entrance of a highway, and a second communication device 110b installed on a road at the exit of the highway. The highway has multiple entrances and exits at its starting point, ending point, and intermediate points. The first communication device 110a and the second communication device 110b are installed on the roads at each entrance and exit. As an example, Figure 1 A first communication device 110a is shown installed on the road that vehicle 2 passes through when entering the highway, and a second communication device 110b is shown installed on the road that vehicle 2 passes through when leaving the highway. The ETC communication device 110 is configured to communicate with the data center 100 via communication network 6. The ETC communication device 110 can also be configured to communicate with the data center 100 via a network different from communication network 6.

[0046] When vehicle 2 passes through a road equipped with the first communication device 110a, the first communication device 110a establishes communication with the ETC on-board unit 32 of vehicle 2, obtains the payment information of the ETC card required for toll payment and information that can identify vehicle 2 (e.g., on-board unit identifier (ID) or vehicle ID) from the ETC on-board unit 32, and sends the time when vehicle 2 passes through the road (entrance passage time) Tin and information that can identify the location of the first communication device 110a (e.g., toll station number, etc., hereinafter also referred to as "entrance toll station information") to the ETC on-board unit 32.

[0047] When vehicle 2 passes through a road equipped with a second communication device 110b, the second communication device 110b establishes communication with the vehicle 2's ETC on-board unit 32, obtains payment information, information that can identify vehicle 2, entrance passage time Tin, and entrance toll station information from the ETC on-board unit 32, and sends the vehicle 2's passage time (exit passage time) Tout, information that can identify the location of the second communication device 110b (e.g., toll station number, etc., hereinafter also referred to as "exit toll station information"), and information about toll fees to the ETC on-board unit 32 and the data center 100.

[0048] The information obtained by the first communication device 110a and the second communication device 110b from the ETC on-board unit 32 is not particularly limited to the information described above. The information sent by the ETC communication device 110 to the data center 100 is also not particularly limited to the information described above. This embodiment describes an example where the data center 100 also performs ETC card transactions. However, the server used to perform ETC card transactions can be set up separately from the data center 100.

[0049] Next, the specific configurations of vehicles 2 and 3 will be described. Since vehicles 2 and 3 have essentially the same configuration, the configuration of vehicle 2 will be described representatively below.

[0050] Vehicle 2 includes drive wheels 50 and driven wheels 52. When the drive wheels 50 rotate by the operation of the drive source, a driving force is applied to vehicle 2, and vehicle 2 moves accordingly.

[0051] Vehicle 2 further includes an advanced driver assistance system-electronic control unit (ADAS-ECU) 10, a brake ECU 20, a data communication module (DCM) 30, and a central ECU 40.

[0052] ADAS-ECU 10, Braking ECU 20, and Central ECU 40 are all computers that include processors (such as CPUs) that execute programs, memory, and input and output interfaces.

[0053] ADAS-ECU 10 includes a driver assistance system with functions related to driving assistance of vehicle 2. The driver assistance system is configured to assist driving of vehicle 2 by running applications installed on the driver assistance system, including at least one of three types of control of vehicle 2: steering control, drive control, and braking control. Examples of applications installed on the driver assistance system include applications that implement functions of an automated driving (AD) system, applications that implement functions of an automatic parking system, and applications that implement functions of an advanced driver assistance system (ADAS) (hereinafter referred to as "ADAS applications").

[0054] For example, ADAS applications include at least one of the following: applications that enable vehicle following driving (adaptive cruise control (ACC), etc.) to maintain a constant distance from the vehicle in front; applications that enable automatic speed limiters (ASL) to sense speed limits and adapt the maximum speed of vehicle 2 to the speed limit; applications that enable lane keeping assist (lane keeping assist (LKA), lane tracing assist (LTA), etc.) to keep vehicle 2 within its lane; applications that enable collision damage mitigation braking (automatic emergency braking (AEB), pre-collision safety (PCS), etc.) to automatically brake vehicle 2 to reduce damage caused by a collision; and applications that enable lane departure warning (lane departure warning (LDW), lane departure alert (LDA), etc.) to warn the driver of vehicle 2 that vehicle 2 is deviating from its lane.

[0055] The various applications on the driver assistance system, based on information about the vehicle's surroundings acquired (input) from, for example, multiple sensors (not shown) and assistance requests from the driver, output requests to the braking ECU 20 for individual motion plans that ensure the suitability (functionality) of the applications. Examples of sensors include vision sensors such as forward-facing cameras, radar, light detection and ranging (LiDAR) sensors, and position detection devices.

[0056] Each application acquires information about the vehicle's surroundings by integrating detection results from more than one sensor as perception sensor information, and also obtains assistance requests from the driver via a user interface (not shown), such as a switch. For example, by processing images or videos of the vehicle's surroundings acquired by sensors using artificial intelligence (AI) or an image processor, each application can perceive other vehicles, obstacles, or people around vehicle 2.

[0057] The motion plan includes, for example, requests related to longitudinal acceleration or deceleration generated in vehicle 2, requests related to the steering angle of vehicle 2, and requests related to brake holding of vehicle 2.

[0058] The brake ECU 20 controls the brake actuators that generate braking force in the vehicle 2 by using detection results from sensors. The brake ECU 20 also sets motion requests for the vehicle 2 to fulfill the motion plan requested from the ADAS-ECU 10. The motion requests set by the brake ECU 20 for the vehicle 2 are implemented by an actuator system (not shown) mounted on the vehicle 2. The actuator system includes, for example, various types of actuator systems, such as powertrain systems, braking systems, and steering systems.

[0059] For example, the first wheel speed sensor 54 and the second wheel speed sensor 56 are connected to the brake ECU 20.

[0060] The first wheel speed sensor 54 detects the rotational speed (wheel speed) Vl of one of the two driven wheels 52 (e.g., the left driven wheel 52). The first wheel speed sensor 54 sends a signal indicating the detected rotational speed Vl of the left driven wheel 52 to the brake ECU 20.

[0061] The second wheel speed sensor 56 detects the rotational speed Vr of another driven wheel 52 (e.g., the right driven wheel 52). The second wheel speed sensor 56 sends a signal indicating the detected rotational speed Vr of the right driven wheel 52 to the brake ECU 20.

[0062] exist Figure 1 The configuration shown is an example in which the first wheel speed sensor 54 and the second wheel speed sensor 56 are connected to the brake ECU 20 and the detection results are sent directly to the brake ECU 20. However, any sensor can be connected to other ECUs, and the detection results of that sensor can be input to the brake ECU 20 via a communication bus or the central ECU 40.

[0063] For example, in addition to information about the driving plan, the braking ECU 20 also receives information from the ADAS-ECU 10 about the operating status of various applications, information about the steering angle, the amount of pressure on the accelerator or brake pedal, or other driving operations such as gear shifting, and information about the behavior of the vehicle 2.

[0064] DCM 30 is a communication module configured for bidirectional communication with Data Center 100.

[0065] The central ECU 40 is configured to communicate with, for example, the brake ECU 20, and is also configured to communicate with the data center 100 via the DCM 30. For example, the central ECU 40 transmits information received from the brake ECU 20 to the data center 100 via the DCM 30.

[0066] In this embodiment, the central ECU 40 is described as an ECU that transmits information received from the brake ECU 20 to the data center 100 via the DCM 30. However, for example, the central ECU 40 may be an ECU with the function of relaying communication between various ECUs (gateway function), or it may be an ECU that includes a memory (not shown) whose stored contents can be updated using update information received from the data center 100, and whose predetermined information including update information stored in the memory from various ECUs is read from the memory when the system of the vehicle 2 is started.

[0067] Vehicle 2 further includes an ETC on-board unit 32. The ETC on-board unit 32 is configured to communicate with the first communication device 110a and the second communication device 110b of the aforementioned ETC communication device 110. Since the information received and transmitted during communication is as described above, its detailed description will not be repeated. The ETC on-board unit 32 is configured to communicate with other ECUs via a communication bus. Therefore, the ETC on-board unit 32 is configured to transmit information obtained from the ETC communication device 110 to other ECUs.

[0068] When a vehicle 2 with the above configuration travels on a section of highway from an entrance near its departure point to an exit near its destination, it is desirable to accurately predict the vehicle's required travel time. However, if the required travel time is calculated using, for example, the times calculated using the entrance and exit times, the required travel time may not be accurately estimated because it varies greatly depending on factors such as road congestion.

[0069] In this embodiment, the brake ECU 20 receives input information including information about the wheel speeds of the vehicle 2. The brake ECU 20 calculates the speed of the vehicle 2 traveling on the highway as a feature by using the input information received during a time period indicating that predetermined conditions for the vehicle 2 to be traveling on a highway are met within the time period during which the brake ECU 20 receives the input information. The predetermined conditions include a driving state where the vehicle speed is higher than a first threshold and the difference between the speeds of the left and right wheels is less than a second threshold for a predetermined time.

[0070] In this scenario, since the speed of vehicle 2 traveling on the highway is calculated as a feature, the highway congestion status can be estimated using the features described below. Therefore, the required travel time can be accurately predicted based on the congestion status.

[0071] Figure 2 This illustration shows an example configuration of a vehicle information processing device according to this embodiment. The vehicle information processing device according to this embodiment is implemented by a brake ECU 20. For example... Figure 2 As shown, the braking ECU 20 includes a first processing unit 22, a second processing unit 24, and a third processing unit 26.

[0072] The first processing unit 22 receives information such as driving assistance status (indicating the operating status of the driving assistance system), driving operation quantities (such as steering wheel, accelerator pedal, brake pedal, etc.), and vehicle status quantities (indicating detection results from various sensors). From the received information, the first processing unit 22 outputs to the second processing unit 24 the rotational speed Vl of the left driven wheel 52 detected by the first wheel speed sensor 54 and the rotational speed Vr of the right driven wheel 52 detected by the second wheel speed sensor 56.

[0073] The second processing unit 24 uses the input information received during a time period that meets predetermined conditions within the time period during which the first processing unit 22 receives the input information to calculate the speed (vehicle speed) Vx of the vehicle 2 as a feature.

[0074] Figure 3 This illustrates an example of the processing performed by the second processing unit 24 in this embodiment. For example... Figure 3 As shown, the first processing unit 22 inputs the rotational speed Vl of the left driven wheel 52 detected by the first wheel speed sensor 54 and the rotational speed Vr of the right driven wheel 52 detected by the second wheel speed sensor 56 as input information to the second processing unit 24.

[0075] The second processing unit 24 determines whether the predetermined conditions are met by using the input information.

[0076] Specifically, the second processing unit 24 calculates the vehicle speed Vx using input information. The second processing unit 24 calculates the vehicle speed Vx using, for example, the average value of rotational speeds Vr and Vl (=(Vr+Vl) / 2) and information such as tire diameter. The second processing unit 24 calculates the difference Vd (=|Vr–Vl|) between the rotational speeds Vr and Vl of the left and right driven wheels 52 using input information.

[0077] The predetermined conditions include conditions instructing vehicle 2 to travel on the main road of a highway. Specifically, the predetermined conditions include a driving state where the vehicle speed Vx is higher than a first threshold (e.g., 40 km / h) and the difference Vd (=|Vr–Vl|) between the rotational speeds Vr and Vl of the left and right driven wheels 52 is less than a second threshold (e.g., a value equivalent to 3 km / h) for a predetermined time (e.g., approximately 15 minutes). The first threshold is set based on highway speed limits, number of lanes, etc. The first threshold is, for example, a predetermined value adjusted through experiments, etc. The second threshold is set based on, for example, the number of lanes on a highway. The second threshold is set through experiments, etc., to include the range of speed differences caused by lane changes, etc., and exclude the range of speed differences caused by sharp turns. The second threshold is set such that, for example, the angular velocity of vehicle 2 in the yaw direction (turning direction) is equal to or less than the threshold (e.g., 30 degrees / second, etc.).

[0078] When the second processing unit 24 determines that the predetermined conditions are met, it sets a flag (hereinafter referred to as "highway driving sign Fj"). When the second processing unit 24 determines that the predetermined conditions are not met, it clears the highway driving sign Fj. The second processing unit 24 outputs a signal indicating the state of the highway driving sign Fj as a scene recognition signal to the third processing unit 26. The second processing unit 24 outputs the vehicle speed Vx as a feature along with the scene recognition signal to the third processing unit 26.

[0079] By using this feature, the third processing unit 26 outputs information indicating whether the highway on which the vehicle 2 is traveling is congested to the central ECU 40. More specifically, the third processing unit 26 sets a value indicating the degree of congestion of the highway on which the vehicle 2 is traveling (hereinafter referred to as "congestion level Cg") by using information output from the second processing unit 24. For example, when the highway driving sign Fj included in the scene recognition signal is on, the third processing unit 26 sets the congestion level Cg by using information output from the second processing unit 24.

[0080] Figure 4 An example of the processing performed by the third processing unit 26 in this embodiment is shown. For example... Figure 4 As shown, information indicating scene recognition signals, features, and time is input from the second processing unit 24 to the third processing unit 26. The information input to the third processing unit 26 is stored in a storage device such as a memory.

[0081] The third processing unit 26 uses multiple vehicle speeds Vx input from the second processing unit 24 within a time period that meets predetermined conditions to calculate the average value Avx of the vehicle speeds Vx. The third processing unit 26 also uses the vehicle speeds Vx input from the second processing unit 24 within the same time period to calculate the standard deviation σ of the vehicle speeds Vx. For the methods used to calculate the average value Avx and the methods used to calculate the standard deviation σ, only predetermined techniques are required, and their detailed descriptions will not be provided.

[0082] The third processing unit 26 uses the calculated standard deviation σ and average value Avx to set the congestion level Cg. For example, when the value obtained by subtracting the standard deviation σ from the average value Avx (Avx – σ) is lower than the speed at which drivers of vehicles traveling on the highway perceive highway congestion (e.g., 60 km / h) (i.e., when the relationship “Avx – σ < 60” (km / h) is satisfied), the third processing unit 26 sets the congestion level Cg to a value indicating highway congestion (e.g., 1).

[0083] On the other hand, when the above relationship is not satisfied, the third processing unit 26 sets the congestion degree Cg to a value indicating that the highway is not congested (for example, 2). The third processing unit 26 outputs the information about the set congestion degree Cg to the central ECU 40. During a period when a predetermined condition is satisfied, the third processing unit 26 sets the congestion degree Cg using the average value Avx and the standard deviation σ calculated every time a predetermined time elapses, and outputs the set congestion degree Cg to the central ECU 40.

[0084] Figure 5 is a coordinate diagram showing an example of the distribution of the vehicle body speed Vx when the vehicle 2 is traveling on a non-congested highway. Figure 6 is a coordinate diagram showing an example of the distribution of the vehicle body speed Vx when the vehicle 2 is traveling on a congested highway. Figure 5 and Figure 6 the ordinate in represents the frequency (count). Figure 5 and Figure 6 the abscissa in represents the vehicle body speed Vx. Figure 5 and Figure 6 The distribution coordinate diagram of is created by, for example, dividing the speed range into a plurality of consecutive intervals and repeating the process of determining which interval the obtained vehicle body speed Vx corresponds to and incrementing the frequency of that interval by 1. For example, as Figure 5 shown, when calculating the standard deviation σ1 of the vehicle body speed related to the average value Avx1 of the vehicle body speed, when the relationship that the value of "Avx1 – σ1" is higher than 60 km / h (equivalent to the relationship of "σ1 < Avx1 – 60") is satisfied, the congestion degree Cg is set to a value indicating that the highway is not congested.

[0085] On the other hand, as Figure 6 shown, when calculating the standard deviation σ2 of the vehicle body speed related to the average value Avx2 of the vehicle body speed, when the relationship that the value of "Avx2 – σ2" is equal to or less than 60 km / h (equivalent to the relationship of "σ2 ≥ Avx2 – 60") is satisfied, the congestion degree Cg is set to a value indicating that the highway is congested.

[0086] The central ECU 40 sends the information input from the third processing unit 26 to the data center 100 via the DCM 30. The central ECU 40 sends the latest value among the values of the congestion degree Cg input from the third processing unit 26 to the data center 100 at a predetermined transmission time via the DCM 30.

[0087] For example, the third processing unit 26 can obtain the highway entrance passage time Tin and entrance toll station information corresponding to the time period that meets the predetermined conditions from the ETC on-board unit 32, and output the obtained entrance passage time Tin and entrance toll station information together with the aforementioned congestion level Cg to the central ECU 40. Alternatively, for example, when information about the congestion level Cg is input from the third processing unit 26 to the central ECU 40, the central ECU 40 can obtain the highway entrance passage time Tin and entrance toll station information corresponding to the time period that meets the predetermined conditions from the ETC on-board unit 32, and send the obtained information together with the information input from the third processing unit 26 to the data center 100 via the DCM 30.

[0088] When the congestion level Cg, the entry passage time Tin, and the entry toll station information are input from vehicle 2 to data center 100, and the exit passage time Tout and the exit toll station information of vehicle 2 are input from ETC communication device 110 to data center 100, data center 100 estimates the change in required travel time relative to the time of congestion period based on actual values, estimates the change in required travel time relative to the time of normal period (non-congestion period) based on actual values, and estimates the change in required travel time from the entry toll station that vehicle 2 has already passed to the exit toll station relative to the current time and the time after the current time by using the input congestion level Cg, entry passage time Tin, and exit passage time Tout.

[0089] In the following text, reference will be made to Figure 7 Examples of methods for estimating how the required travel time changes relative to the current time and times after the current time. Figure 7 This is a coordinate graph showing an example of how the required travel time varies with time in a predetermined interval (from the entrance to the exit toll station of the highway through which vehicle 2 passes).

[0090] Figure 7 The horizontal axis in the figure represents the exit time Tout. Figure 7 The vertical axis represents the change in required travel time. The required travel time is obtained by subtracting the entrance passage time Tin from the exit passage time Tout. Figure 7 The LN1 (dotted line) in the diagram shows the change in required travel time relative to time, predicted from actual data accumulated during past congestion periods. Figure 7 The LN2 (thin solid line) in the figure shows the variation of the required travel time relative to the time point, predicted from actual data accumulated over a normal period of time in the past. Figure 7 LN3 (dashed line) in the diagram shows the change in the predicted required travel time relative to the current time. Figure 7LN4 (thick solid line) in the figure shows the change of the required travel time relative to time obtained by using other vehicles up to the current time through the entry pass time Tin and the exit pass time Tout.

[0091] For example, when the required travel time based on the current time of the entrance passage time Tin and the exit passage time Tout received from vehicle 2 corresponds to the time at point A, data center 100 uses the time from point A to point B ( Figure 7 The vector from point A to point C (the square in the diagram) and the vector from point A to point C (the square in the diagram) Figure 7 The vector from point A to point D (of the triangle in the diagram) is used to calculate the distance from point A to point D. Figure 7 The vector (of the circle in the image). Data Center 100 therefore estimates the peak travel time required throughout the day and the times when the peak travel time will occur, and predicts... Figure 7 The change shown by LN3 makes the predicted change pass through the estimated peak point.

[0092] More specifically, the required travel time at point B corresponds to the peak of the required travel time predicted from actual data accumulated during past congested periods, relative to the change in time. The required travel time at point C corresponds to the peak of the required travel time predicted from actual data accumulated during past normal periods, relative to the change in time.

[0093] Data center 100 sets coefficients a for a first vector from point A to point B and coefficients b for a second vector from point A to point C using congestion level Cg. Data center 100 obtains congestion levels Cg from multiple vehicles, including vehicles 2 that have passed through the section from the entrance to the exit of the highway via vehicle 2 in the most recent predetermined time period, and calculates the proportion Rcr of vehicles whose congestion level Cg indicates highway congestion among the multiple vehicles, and the change dRcr of the proportion Rcr per unit time. Data center 100 sets coefficient a using a first function f(Rcr, dRcr) with the proportion Rcr and the change dRcr as input parameters. Data center 100 also sets coefficient b using a second function g(Rcr, dRcr) with the proportion Rcr and the change dRcr as input parameters.

[0094] Data center 100 identifies point D by calculating the sum of a vector with length equal to the first vector multiplied by coefficient a and a vector with length equal to the second vector multiplied by coefficient b, as the vector from point A to point D. Data center 100 sets a curve showing the change in required travel time relative to time, which peaks at point D. Data center 100 can, for example, shrink and move... Figure 7 The curve shown by LN1 causes the peak point to move to point D, to set the current time and the time after the current time. Figure 7The curve shown is LN3. Alternatively, data center 100 can be expanded and moved, for example. Figure 7 The curve shown by LN2 causes the peak point to move to point D, to set the current time and the time after the current time. Figure 7 The curve shown is LN3.

[0095] Once set Figure 7 The curve shown in LN3 allows data center 100 to publish the set curve or the required travel time corresponding to the time to the public.

[0096] When, for example, vehicle 2 inputs congestion level Cg, entrance passage time Tin, and entrance toll station information, data center 100 can request vehicle 2's exit passage time Tout from ETC communication device 110. In response to this request, when ETC communication device 110 obtains the exit passage time Tout, it can send vehicle 2's exit passage time Tout along with the exit toll station information to data center 100. Alternatively, when vehicle 2 obtains the entrance passage time Tin and exit passage time Tout from ETC on-board unit 32 and obtains the most recent value of congestion level Cg, vehicle 2 can send this information along with the entrance toll station information and exit toll station information to data center 100.

[0097] Next, we will refer to Figure 8 An example of the processing performed by the second processing unit 24 of the braking ECU 20 of vehicle 2 is described. Figure 8 This is a flowchart illustrating an example of the processing performed by the second processing unit 24 of the brake ECU 20. The series of steps shown in this flowchart are repeatedly executed by the second processing unit 24 of the brake ECU 20 at predetermined control cycles.

[0098] In step (hereinafter referred to as "S") 100, the brake ECU 20 (specifically, the second processing unit 24) acquires data corresponding to the input information. Specifically, the brake ECU 20 acquires data corresponding to the input information, including, for example, information about the rotational speed Vr of the right driven wheel 52 and information about the rotational speed Vl of the left driven wheel 52.

[0099] In S102, the brake ECU 20 calculates the vehicle speed Vx. Specifically, the brake ECU 20 uses the average value of the rotational speeds Vr and Vl to calculate the vehicle speed Vx. The brake ECU 20 stores the calculated vehicle speed Vx in a memory or the like in association with, for example, the value of the highway driving sign Fj, which will be described later.

[0100] In S104, the brake ECU 20 calculates the difference Vd (=|Vr–Vl|) between the rotational speeds Vr and Vl of the left and right driven wheels 52.

[0101] In S106, the brake ECU 20 calculates the value of the time counter Cn. Specifically, the brake ECU 20 adds a predetermined value (e.g., 1) to the previous value of the time counter Cn–1 to calculate the current value of the time counter Cn.

[0102] In S108, the braking ECU 20 determines whether predetermined conditions are met. Specifically, the braking ECU 20 determines that predetermined conditions are met when the vehicle speed Vx is greater than a first threshold, the difference Vd between the rotational speeds Vr and Vl of the left and right driven wheels 52 is less than a second threshold, and the time counter Cn is greater than a third value (e.g., equivalent to 15 minutes). When the braking ECU 20 determines that predetermined conditions are met ("Yes" in S108), the program proceeds to S110.

[0103] In S110, the brake ECU 20 sets the highway driving sign Fj. For example, the brake ECU 20 sets the value of the highway driving sign Fj to a value indicating the on state (e.g., 1).

[0104] In S112, the brake ECU 20 sets the value of the current time counter Cn to the value of the previous time counter Cn–1. Then, the process ends. When the brake ECU 20 determines that the predetermined condition is not met ("No" in S108), the routine proceeds to S114.

[0105] In S114, the brake ECU 20 clears the highway driving sign Fj. For example, the brake ECU 20 sets the value of the highway driving sign Fj to a value indicating a closed state (e.g., zero).

[0106] In S116, the brake ECU 20 resets the value of the current time counter Cn to its initial value (e.g., zero). Then, the process ends.

[0107] Next, we will refer to Figure 9 An example of the processing performed by the third processing unit 26 of the braking ECU 20 of vehicle 2 is described. Figure 9 This is a flowchart illustrating an example of processing performed by the third processing unit 26 of the brake ECU 20. The series of steps shown in the flowchart are repeatedly executed by the third processing unit 26 of the brake ECU 20 at predetermined control cycles. For example, the third processing unit 26 executes the process when the vehicle 2 starts moving or when the vehicle 2's system is activated. Figure 9 The process is shown in the flowchart.

[0108] In S200, the brake ECU 20 (specifically, the third processing unit 26) resets the values ​​of the congestion level indicator Cg, the standard deviation σ of the vehicle speed indicator Vx, the average value Avx of the vehicle speed indicator Vx, and the indicator counter n to their initial values. Specifically, the brake ECU 20 sets each of these values ​​to, for example, zero.

[0109] In S202, the brake ECU 20 acquires the vehicle speed Vx and the highway driving sign Fj. For example, the brake ECU 20 acquires... Figure 8 The process shown includes the calculated vehicle speed Vx and highway driving sign Fj.

[0110] In S204, the brake ECU 20 determines whether the highway driving sign Fj is open. For example, when the value of the highway driving sign Fj is 1, the brake ECU 20 determines that the highway driving sign Fj is open. When the brake ECU 20 determines that the highway driving sign Fj is open ("Yes" in S204), the routine proceeds to S206. When the brake ECU 20 determines that the highway driving sign Fj is closed ("No" in S204), the routine proceeds to S220.

[0111] In S206, the brake ECU 20 increments the counter n. Specifically, the brake ECU 20 adds a predetermined value (e.g., 1) to the previous value of the counter n to calculate the current value of the counter n.

[0112] In S208, the braking ECU 20 adds the vehicle speed Vx obtained in S202 as the current value of Vx(n) and stores it up to the vehicle speed Vx stored up to the previous value.

[0113] In S210, the braking ECU 20 uses n vehicle speeds Vx, i.e., Vx(1) to Vx(n), to calculate the average value Avx of the vehicle speed Vx.

[0114] In S212, the braking ECU 20 uses n vehicle speeds Vx, i.e., Vx(1) to Vx(n), to calculate the standard deviation σ of the vehicle speed Vx.

[0115] In S214, the braking ECU 20 determines whether the standard deviation σ is greater than the average value Avx minus a predetermined value (e.g., 60 km / h in this embodiment). When the braking ECU 20 determines that the standard deviation σ is greater than the average value Avx minus 60 km / h ("yes" in S214), the routine proceeds to S216.

[0116] In S216, the braking ECU 20 sets the congestion level Cg to 1, which is an indicator of highway congestion. When the braking ECU 20 determines that the standard deviation σ is equal to or less than the average value Avx minus 60 km / h (No in S214), the routine proceeds to S218.

[0117] In S218, the brake ECU 20 sets the congestion level Cg to 2, which is a value indicating that the highway is not congested (traffic is smooth).

[0118] In S220, the brake ECU 20 outputs a value indicating the congestion level Cg to the central ECU 40. The central ECU 40 then sends the input information to the data center 100 via the DCM 30.

[0119] Next, we will refer to Figure 10 An example describing the processing performed by data center 100. Figure 10 This is a flowchart illustrating an example of a process performed by data center 100. The series of steps shown in the flowchart are repeatedly executed by data center 100 at predetermined control cycles.

[0120] In S300, data center 100 determines whether it has received congestion level Cg, entrance passage time Tin, and entrance toll station information from the vehicle. For example, when data center 100 receives information including congestion level Cg and entrance passage time Tin from a vehicle (such as vehicle 2 or vehicle 3) that can communicate with data center 100, data center 100 determines that it has received congestion level Cg and entrance passage time Tin from that vehicle. When data center 100 determines that it has received congestion level Cg and entrance passage time Tin from the vehicle ("Yes" in S300), the routine proceeds to S302.

[0121] In S302, the data center 100 determines whether it has received from the ETC communication device 110 the exit passage time Tout and exit toll station information of the vehicle that has received the congestion level Cg and the entrance passage time Tin. When the data center 100 determines that the exit passage time Tout has been received ("Yes" in S302), the routine proceeds to S304. When the data center 100 determines that it has not yet received the congestion level Cg and the entrance passage time Tin from the vehicle ("No" in S300), or determines that it has not yet received the exit passage time Tout ("No" in S302), the routine returns to S300.

[0122] In S304, the data center 100 calculates the proportion Rcr of vehicles whose congestion level Cg has a value indicating "congestion" (=1) among multiple vehicles that have passed through the same interval as the vehicles that received the congestion level Cg of the highway and the entry passage time Tin, and also calculates the change dRcr of the proportion Rcr per unit time.

[0123] In S306, the data center 100 sets a prediction curve for the change in required travel time relative to time based on the ratio Rcr, the change amount dRcr, and the actual data accumulated in the past. Since the method used to set the prediction curve is as described above, its detailed description will not be repeated.

[0124] The operation of the braking ECU 20 (i.e., the vehicle information processing device according to this embodiment) based on the above structure and flowchart will be described.

[0125] For example, when vehicle 2 starts moving, the second processing unit 24 acquires the rotational speeds Vr and Vl of the left and right driven wheels 52 (S100), and uses the acquired rotational speeds Vr and Vl to calculate the vehicle speed Vx (S102). Then, the second processing unit 24 calculates the difference Vd between the rotational speeds Vr and Vl of the left and right driven wheels 52 (S104), counts the time counter Cn (S106), and determines whether a predetermined condition is met (S108).

[0126] When the vehicle speed Vx is higher than the first threshold and the difference Vd between the rotational speeds Vr and Vl of the left and right driven wheels 52 is less than the second threshold for a predetermined time, the second processing unit 24 sets the highway driving sign Fj (S110) and sets the value of the time counter Cn to the previous value (S112).

[0127] On the other hand, when the predetermined conditions are not met (no in S108), the second processing unit 24 clears the highway driving sign Fj (S114) and resets the value of the time counter Cn to its initial value (S116).

[0128] When vehicle 2 starts moving, the third processing unit 26 resets the congestion degree Cg, the standard deviation σ of vehicle speed Vx, the average value Avx of vehicle speed Vx, and the counter n to their initial values ​​(S200).

[0129] The third processing unit 26 obtains the vehicle speed Vx and the highway driving sign Fj from the second processing unit 24 (S202). When the highway driving sign Fj is open ("Yes" in S204), the third processing unit 26 increments the counter n (S206) and adds the obtained vehicle speed Vx to it, storing it as the vehicle speed Vx(n) corresponding to the incremented counter n (S208). Then, the third processing unit 26 calculates the average value Avx using the vehicle speeds Vx(1) to Vx(n) (S210). The third processing unit 26 also calculates the standard deviation σ using the vehicle speeds Vx(1) to Vx(n) (S212).

[0130] When the calculated standard deviation σ is greater than the average value Avx minus 60 km / h ("Yes" in S214), the third processing unit 26 sets the congestion level Cg to 1, which is an indicator of "congestion" (S216), and outputs the congestion level Cg to the central ECU 40 (S220). This process is repeated as long as vehicle 2 continues to travel. The central ECU 40 obtains, for example, the entrance passage time Tin from the ETC on-board unit 32, i.e., the time it takes for vehicle 2 to pass through the highway entrance. The central ECU 40 sends the congestion level Cg and the entrance passage time Tin to the data center 100 via the DCM 30.

[0131] Data center 100 receives congestion level Cg and entrance passage time Tin from vehicle 2 ("Yes" in S300). Congestion level Cg is generated every 15 minutes until vehicle 2 passes the highway exit, and the generated congestion level Cg and entrance passage time Tin are sent to data center 100 via central ECU 40 and DCM 30.

[0132] When vehicle 2 exits the highway, data center 100 obtains the exit passage time Tout of vehicle 2 from ETC communication device 110 ("Yes" in S302). Therefore, data center 100 uses the congestion level Cg, the entrance passage time Tin, and the exit passage time Tout at the time data center 100 receives the exit passage time Tout to calculate the proportion Rcr and the change dRcr (S304). By using the calculated proportion Rcr and change dRcr as input parameters, data center 100 calculates coefficient a using a first function f(Rcr, dRcr) and coefficient b using a second function g(Rcr, dRcr). Data center 100 thus identifies... Figure 7 Point D in the equation, and setting the prediction curves for the current time and subsequent times as follows. Figure 7 As shown by the dashed line, the required travel time relative to a given time can be predicted using a predefined prediction curve.

[0133] As described above, according to the vehicle information processing apparatus of this embodiment, the vehicle speed Vx of vehicle 2 traveling on the highway is calculated as a feature. Therefore, the congestion level indicating the congestion status of the highway can be accurately set by using the distribution of multiple features. Therefore, the required travel time can be accurately predicted based on the congestion status. Therefore, a vehicle information processing apparatus, prediction system, vehicle information processing method, and program capable of accurately estimating the required travel time of vehicles can be provided. In this embodiment, the prediction system consists of vehicles 2 and 3, a data center 100 (example of a "prediction device"), and an ETC communication device 110.

[0134] The predetermined conditions include a driving state that continues for a predetermined time, where the vehicle speed Vx is greater than a first threshold and the difference Vd between the rotational speeds Vr and Vl of the left and right driven wheels 52 is less than a second threshold. Therefore, the rotational speeds Vr and Vl can be used to accurately determine whether the vehicle 2 is traveling on a highway. Furthermore, since the vehicle speed Vx is calculated using the rotational speed of the driven wheels 52, it is more accurate than calculating the vehicle speed Vx using the rotational speed of the drive wheels 50.

[0135] While vehicle 2 is traveling on the highway, the congestion level, indicating the highway's congestion status, can be accurately set by calculating the standard deviation, for example, using the distribution of multiple features. Furthermore, valuable information for users, such as the highway's congestion status, can be generated by performing actions such as concealment (e.g., statistical quantification) to prevent individuals from being identified.

[0136] Furthermore, since vehicle 2 can send information about highway congestion to the outside of vehicle 2 (data center 100), the utility value of the information about highway congestion can be increased.

[0137] Furthermore, since the feature calculation and feature statistical quantization are performed by the second processing unit 24 and the third processing unit 26 respectively, the feature statistical quantization processing performed only by the third processing unit 26 can be modified and used to generate information about other changes. This modification of the statistical quantization processing can be achieved, for example, by the brake ECU 20 reading updated information received from the data center 100 and stored in the memory of the central ECU 40.

[0138] In addition, an accurate prediction curve can be set by receiving congestion Cg from multiple vehicles in the section from the entrance to the exit toll station where vehicle 2 has already passed.

[0139] The following describes a variant example. In the above embodiment, it is described how input information to the brake ECU 20 is received in the brake ECU 20. Figure 8 and Figure 9The flowchart processing is an example of performing feature calculations and statistical quantifications. However, these processes can be performed within data center 100.

[0140] In the above embodiment, the data center 100 receives the congestion level Cg and the entrance passage time Tin from vehicle 2, and receives the exit passage time Tout from ETC communication device 110. However, for example, the data center 100 can receive the congestion level Cg, entrance passage time Tin, and exit passage time Tout from vehicle 2, or it can receive the congestion level Cg, entrance passage time Tin, and exit passage time Tout from ETC communication device 110. When exiting the highway, vehicle 2 can send the congestion level Cg and entrance passage time Tin to ETC communication device 110 via the second communication device 110b.

[0141] In the above embodiment, the operation of estimating the required travel time for vehicle 2 to traverse the section from the entrance to the exit toll station is described as an example. However, the required travel time for other vehicles to traverse the section from the entrance to the exit toll station can be estimated similarly. Therefore, by estimating the required travel time for various combinations of sections between the entrance and exit toll stations as described above, an estimate of the required travel time can be provided upon user request.

[0142] In the above embodiment, the congestion level Cg is set using the standard deviation σ of the vehicle speed Vx as a characteristic and the average value Avx of the vehicle speed Vx. However, the congestion level Cg can be set by using the proportion of vehicles whose vehicle speed Vx as a characteristic does not match the target speed in cruise control among multiple vehicles under cruise control.

[0143] Next, we will refer to Figure 11 An example of the processing performed by the braking ECU 20 (specifically, the third processing unit 26) of vehicle 2 in the variant is described. Figure 11 This is a flowchart illustrating an example of the processing performed by the third processing unit 26 of the brake ECU 20 in this variant.

[0144] In S400, the braking ECU 20 resets the values ​​of the congestion level indicator Cg, the smooth driving counter Cj, and the indicator counter n to their initial values ​​(e.g., zero).

[0145] In S402, the brake ECU 20 acquires the vehicle speed Vx, the target speed Vtx, and the highway driving sign Fj. The brake ECU 20 acquires, for example, in... Figure 8The processing shown includes the calculated vehicle speed Vx and highway driving sign Fj. The braking ECU 20 also obtains a target speed Vtx from the ADAS-ECU 10, for example, for cruise control. The ADAS-ECU 10 has driver assistance systems for applications such as enabling ACC (Adaptive Cruise Control).

[0146] In S404, the brake ECU 20 determines whether the highway driving sign Fj is open. When the brake ECU 20 determines that the highway driving sign Fj is open ("Yes" in S404), the routine proceeds to S406. When the brake ECU 20 determines that the highway driving sign Fj is closed ("No" in S404), the routine proceeds to S418.

[0147] In S406, the brake ECU 20 increments the counter n. Specifically, the brake ECU 20 adds a predetermined value (e.g., 1) to the previous value of the counter n to calculate the current value of the counter n.

[0148] In S408, the braking ECU 20 determines whether the vehicle speed Vx is equal to or greater than the target speed Vtx minus a predetermined value α. The predetermined value α is, for example, a value used to determine whether the vehicle speed Vx is near the target speed Vtx. For example, the predetermined value α can be a pre-determined value, or it can be set using highway speed limits, the number of lanes, etc. When the braking ECU 20 determines that the vehicle speed Vx is equal to or greater than the target speed Vtx minus the predetermined value α ("Yes" in S408), the routine proceeds to S410.

[0149] In S410, the brake ECU 20 increments the smooth driving counter Cj. Specifically, the brake ECU 20 adds a predetermined value (e.g., 1) to the previous value of the smooth driving counter Cj to calculate the current value of the smooth driving counter Cj.

[0150] In S412, the braking ECU 20 determines whether the smooth driving counter Cj divided by the counter n is less than a third threshold (e.g., 0.7). The third threshold is used to determine whether the highway on which vehicle 2 is traveling is congested. For example, the third threshold can be a predetermined value, or it can be set using highway speed limits, number of lanes, etc. When the braking ECU 20 determines that the smooth driving counter Cj divided by the counter n is less than the third threshold ("Yes" in S412), the routine proceeds to S414.

[0151] In S414, the braking ECU 20 sets the congestion level Cg to 1, which indicates highway congestion. When the braking ECU 20 determines that the smooth driving counter Cj divided by the counter n is equal to or greater than the third threshold ("No" in S412), the routine proceeds to S416.

[0152] In S416, the brake ECU 20 sets the congestion level Cg to 2, which indicates that the highway is not congested (traffic is smooth).

[0153] In S418, the brake ECU 20 outputs a value indicating the congestion level Cg to the central ECU 40. The central ECU 40 then sends the input information to the data center 100 via the DCM 30.

[0154] The operation of the brake ECU 20 (i.e., the vehicle information processing device in this variant) based on the above structure and flowchart will be described. Since the operation of the second processing unit 24 is as described above, its detailed description will not be repeated.

[0155] When vehicle 2 starts moving, the third processing unit 26 resets the congestion level Cg, smooth driving counter Cj and counter n to their initial values ​​(S400).

[0156] The third processing unit 26 acquires the vehicle speed Vx, target speed Vtx, and highway driving sign Fj from the second processing unit 24 (S402). When the highway driving sign Fj is activated ("Yes" in S404), the third processing unit 26 increments the counter n (S406). When the acquired vehicle speed Vx is equal to or greater than the target speed Vtx minus a predetermined value α, the third processing unit 26 increments the smooth driving counter Cj. When this state continues, the smooth driving counter Cj divided by the counter n becomes equal to or greater than 0.7 ("No" in S412). Therefore, the third processing unit 26 sets the congestion level Cg to 2, which is a value indicating a non-congested state.

[0157] On the other hand, when the highway becomes congested and the vehicle speed Vx becomes lower than the target speed Vtx minus a predetermined value α ("No" in S408), the third processing unit 26 increments the counter n, but does not increment the smooth driving counter Cj. Therefore, the smooth driving counter Cj divided by the counter n will decrease. When this state continues and the smooth driving counter Cj divided by the counter n becomes less than 0.7 ("Yes" in S412), the third processing unit 26 sets the congestion level Cg to 1, which is a value indicating the congestion state (S414). The third processing unit 26 outputs the set congestion level Cg to the central ECU 40 (S418). The above process is repeated as long as the vehicle 2 continues to travel. The central ECU 40 sends the congestion level Cg and the entry time Tin to the data center 100 via the DCM 30. Since the operation of the data center 100 is as described above, its detailed description will not be repeated.

[0158] As described above, according to the vehicle information processing device of the variant example, the congestion status of the highway can be accurately estimated using the proportion by which the vehicle speed Vx reaches the target speed Vtx. Therefore, the required travel time can be accurately predicted based on the congestion status.

[0159] Some or all of the above-described variations may be appropriately combined. The embodiments disclosed herein should be interpreted in all respects as illustrative and not restrictive. The scope of the invention is set forth in the claims, not in the foregoing description, and is intended to include all variations within the meaning and scope equivalent to the claims.

Claims

1. A vehicle information processing system, characterized in that... The device includes a vehicle information processing unit, which includes one or more processors, wherein the one or more processors are configured to: Receive input information including information about the vehicle's wheel speed; and The speed of the vehicle traveling on the highway is calculated as a feature using the input information, wherein... The input information is information received within a predetermined time period that meets certain conditions during the time period when the one or more processors receive the input information. The predetermined conditions indicate that the vehicle is traveling on the highway. The one or more processors are further configured to: Congestion levels are obtained from multiple vehicles, including the vehicle itself, that passed through the section of the highway from the entrance to the exit of the highway within the most recent predetermined time period. When the required travel time for the current moment, obtained from the entry and exit transit times received from the vehicle, corresponds to the time at the first point, a vector from the first point to the fourth point is calculated using a first vector from the first point to the second point and a second vector from the first point to the third point. The congestion level is used to set the coefficients of the first and second vectors. The fourth point is determined by calculating the sum of a vector with a length equal to the first vector multiplied by its coefficient and a vector with a length equal to the second vector multiplied by its coefficient. Using the fourth point as the peak point, a curve predicting the change in required travel time relative to the current moment and subsequent moments is generated. This estimates the peak required travel time of the day and the moment when the required travel time will reach the peak. The required travel time at the second point corresponds to the peak of the required travel time predicted from actual data accumulated in the past during congested periods, relative to the change in time. The required travel time at the third point corresponds to the peak of the required travel time predicted from actual data accumulated in the past during normal periods, which are non-congested periods.

2. The vehicle information processing system according to claim 1, characterized in that... The input information includes information about the wheel speeds of the left and right wheels of the vehicle, and The predetermined conditions include conditions where the driving state continues for a predetermined time or more, and The driving state is a state in which the vehicle's speed is higher than a first threshold and the difference between the wheel speeds of the left and right wheels is less than a second threshold.

3. The vehicle information processing system according to claim 1, characterized in that, The one or more processors are configured as follows: Calculate the standard deviation of the vehicle's speed calculated multiple times within the time period that satisfies the predetermined conditions; and When the standard deviation is greater than the threshold, the congestion level is set to a value indicating highway congestion.

4. The vehicle information processing system according to claim 1, characterized in that... The one or more processors are configured to set the congestion level to a value indicating highway congestion when the proportion is less than a threshold during the time period that meets the predetermined conditions. The ratio is the proportion by which the speed of the vehicle under cruise control reaches the target speed set in the cruise control.

5. The vehicle information processing system according to any one of claims 1 to 4, characterized in that, The one or more processors are configured to send information about the congestion level to the outside of the vehicle.

6. The vehicle information processing system according to claim 1, characterized in that, The system further includes a prediction device, which is configured to: By using information from multiple vehicles equipped with the vehicle information processing device, the required travel time of the vehicle within a predetermined section on the highway is predicted; and The required travel time is predicted from the proportion of vehicles with a predetermined level of congestion.

7. A vehicle information processing method, characterized in that... include: Receive input information including information about the vehicle's wheel speed; and The speed of the vehicle traveling on the highway is calculated as a feature using the input information, wherein... The input information is information received within a time period that meets predetermined conditions during the time period in which the input information is received. The predetermined conditions indicate that the vehicle is traveling on the highway. The vehicle information processing method further includes: Congestion levels are obtained from multiple vehicles, including the vehicle itself, that passed through the section of the highway from the entrance to the exit of the highway within the most recent predetermined time period. When the required travel time for the current moment, obtained from the entry and exit transit times received from the vehicle, corresponds to the time at the first point, a vector from the first point to the fourth point is calculated using a first vector from the first point to the second point and a second vector from the first point to the third point. The congestion level is used to set the coefficients of the first and second vectors. The fourth point is determined by calculating the sum of a vector with a length equal to the first vector multiplied by its coefficient and a vector with a length equal to the second vector multiplied by its coefficient. Using the fourth point as the peak point, a curve predicting the change in required travel time relative to the current moment and subsequent moments is generated. This estimates the peak required travel time of the day and the moment when the required travel time will reach the peak. The required travel time at the second point corresponds to the peak of the required travel time predicted from actual data accumulated in the past during congested periods, relative to the change in time. The required travel time at the third point corresponds to the peak of the required travel time predicted from actual data accumulated in the past during normal periods, which are non-congested periods.

8. A non-transitory storage medium storing instructions executable by one or more processors of a computer, causing the one or more processors to perform a function, the function being characterized by comprising: Receive input information including information about the vehicle's wheel speed; and The speed of the vehicle traveling on the highway is calculated as a feature using the input information, wherein... The input information is information received within a time period that meets predetermined conditions during the time period in which the input information is received. The predetermined conditions indicate that the vehicle is traveling on the highway. The functionality also includes: Congestion levels are obtained from multiple vehicles, including the vehicle itself, that passed through the section of the highway from the entrance to the exit of the highway within the most recent predetermined time period. When the required travel time for the current moment, obtained from the entry and exit transit times received from the vehicle, corresponds to the time at the first point, a vector from the first point to the fourth point is calculated using a first vector from the first point to the second point and a second vector from the first point to the third point. The congestion level is used to set the coefficients of the first and second vectors. The fourth point is determined by calculating the sum of a vector with a length equal to the first vector multiplied by its coefficient and a vector with a length equal to the second vector multiplied by its coefficient. Using the fourth point as the peak point, a curve predicting the change in required travel time relative to the current moment and subsequent moments is generated. This estimates the peak required travel time of the day and the moment when the required travel time will reach the peak. The required travel time at the second point corresponds to the peak of the required travel time predicted from actual data accumulated in the past during congested periods, relative to the change in time. The required travel time at the third point corresponds to the peak of the required travel time predicted from actual data accumulated in the past during normal periods, which are non-congested periods.

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