JP7766980B2 - Driving assistance devices - Google Patents

Description

The present invention relates to a driving assistance device and is suitable for rail transportation systems such as railways.

The running speed pattern between stations (hereinafter referred to as "running pattern") varies depending on the driver, even when trains run between stations at similar times, and as a result, the amount of power consumed when running between stations also varies. By reducing the variation in running patterns and standardizing them to energy-efficient running patterns, it is possible to make railway operations more energy-efficient.

Effective methods for reducing variability in driving patterns include driver training based on reviewing driving patterns after operation, and driving operation support during operation. The former, driver training, involves storing driving patterns during operation in a device, and allowing drivers to review the visualized results of driving patterns along with power consumption and other data during their free time, helping to improve their own driving operations. The latter, driving operation support, includes devices that provide advice on driving operations, and devices that actually perform driving operations, such as autonomous driving. Hereinafter, these devices that contribute to reducing variability in driving patterns will be referred to as driving support devices.

To realize these driving assistance devices, vehicle position and vehicle speed are required as input. One way to obtain highly accurate vehicle position and vehicle speed is to obtain them from a vehicle information device that manages this information. However, this method requires modification of the vehicle information device, making it difficult to implement, and also presents the issue that it cannot be applied to vehicles that do not have a vehicle information device installed in the first place.

One way to implement the above-mentioned driving assistance device without connecting to a vehicle information device is to bring a mobile device equipped with a GPS receiving function into the vehicle, calculate the vehicle's position from the latitude and longitude information acquired by the GPS receiving function, and calculate the vehicle speed from the difference in vehicle position.

JP 2013-7723 A

In conditions where radio waves from GPS satellites cannot be received due to obstructions, such as inside tunnels or around covered stations, the vehicle's position cannot be determined, limiting the use of driving assistance devices. In response to this issue, Patent Document 1 discloses a method for estimating vehicle speed by integrating acceleration using the acceleration sensor function of a mobile device when radio waves from GPS satellites cannot be received due to obstructions, and then estimating vehicle position by integrating this vehicle speed.

However, methods based on acceleration sensors have the problem that the accuracy of estimating vehicle position and vehicle speed deteriorates if the orientation or angle of the mobile device is not constant. Mobile devices carried on board vehicles may also be used to check route information and operation information. In such cases, the mobile device may be operated by the driver while the vehicle is in operation, so there is no guarantee that the orientation or angle of the mobile device will be constant.

The present invention aims to provide a driving assistance technology that can be used effectively by accurately estimating vehicle position and speed even in conditions where radio waves from GPS satellites cannot be received, as a driving assistance device that uses the vehicle position and speed obtained through the GPS reception function of a mobile device.

In order to solve the above problems, one representative driving assistance device of the present invention is a driving assistance device that provides driving assistance based on the vehicle position and vehicle speed of a vehicle running on a track, and has a sound collection unit that collects sounds inside and outside the vehicle, including at least sounds generated from equipment mounted on the vehicle, as vehicle sound data, and the sound collection unit is provided on the lead car of the vehicle, and the vehicle position and vehicle speed are estimated based on the order of appearance of multiple characteristic sounds included in the vehicle sound data collected by the sound collection unit, and the multiple characteristic sounds are at least two of the following sounds: the sliding sound between the wheels and brake shoes when the vehicle's air brake is used, the operating sound of the vehicle's master controller handle, the warning sound for passengers when the vehicle doors are opened and closed, the door operation sound when the vehicle doors are opened and closed, the departure melody sound at the station where the vehicle is stopped, the sound that notifies passengers that the vehicle doors are closed, and the sound of compressed air being released when the vehicle's air brake is released .

According to the present invention, it is possible to utilize a driving assistance device that uses the GPS function of a mobile terminal even on routes and between stations where there are sections where GPS signals cannot be received.
Problems, configurations, and effects other than those described above will become apparent from the following description of the preferred embodiments.

1 is a diagram illustrating a functional configuration of a driving assistance device according to a first embodiment of the present invention and an overview of each function. FIG. 10 is a diagram illustrating an example of a flowchart of a process executed by a position/speed correction information generating unit. FIG. 10 is a diagram showing an example of a characteristic sound expression template. FIG. 10 is a diagram showing an example of a characteristic sound expression template. FIG. 10 is a diagram illustrating an example of a characteristic sound history table. FIG. 10 is a diagram illustrating an example of a characteristic sound history table. FIG. 10 is a diagram illustrating an example of a characteristic sound history table. FIG. 10 is a diagram illustrating an example of a flowchart of a process executed by a driving assistance content generation unit. 10 is a schematic diagram showing an example of driving pattern data generated by the driving assistance content generation unit in the form of a speed graph between stations. FIG. FIG. 4 is a diagram showing an example of driving pattern data in the form of a table. FIG. 10 is a diagram illustrating an example of visualization of driving assistance content. FIG. 10 is a diagram illustrating a functional configuration of a driving assistance device according to a second embodiment of the present invention and an overview of each function.

Hereinafter, with reference to the drawings, Examples 1 and 2 will be described as modes for carrying out the present invention. Note that the present invention is not limited to these Examples. In addition, in the drawings, identical parts are denoted by the same reference numerals.

Examples 1 and 2 show an example of a driving assistance device that encourages train crew members to reflect on their driving operations by visualizing the running pattern between stations based on the vehicle position and speed estimated using GPS in a railway vehicle.

In Examples 1 and 2, it is assumed that the driving assistance device is a mobile terminal equipped with a GPS function and that the mobile terminal is installed in the driver's cab. However, this is not a binding assumption, and it is of course also possible to install a device with a GPS function in the vehicle.

In addition, in sections where GPS reception is poor, the driving assistance device estimates information about the vehicle's position and speed based on sound data from inside and outside the vehicle collected by a sound collection unit included in the driving assistance device.

This makes it possible to supplement information about vehicle position and speed in sections where GPS cannot estimate these parameters during the process of visualizing driving patterns. This makes it possible to utilize driving assistance devices even in sections with poor GPS reception, leading to improved energy efficiency.

FIG. 1 is a diagram showing the functional configuration and an outline of each function of a driving assistance device according to a first embodiment of the present invention.
The driving assistance device is composed of a GPS receiving unit 101, an on-track position estimation unit 102, a vehicle speed estimation unit 103, an operation unit 104, a route information management unit 105, a position and speed correction information generation unit 106, a sound collection unit 107, a characteristic sound detection unit 108, a driving assistance content generation unit 109, and a driving assistance content transmission unit 110.

The GPS receiver 101 generates latitude and longitude 151 based on the received GPS signal at a predetermined time interval and transmits it to the on-track position estimation unit 102. It is desirable to set the time interval taking into consideration the purpose of the driving assistance device, which is to enable the driver to review their driving operation by visualizing the driving pattern.

In other words, if the time period is too short, the visualized driving pattern may contain vibration components that do not exist in the actual vehicle speed due to positional variations caused by the accuracy limits of GPS. On the other hand, if the time period is too long, the reproducibility of vehicle speed changes will be poor, making it difficult for drivers to review their driving operations by visualizing the driving pattern.

The GPS receiving unit 101 also generates a reception status 154 based on the state of the received GPS signal and transmits it to the position and speed correction information generating unit 106 and the driving assistance content generating unit 109. As an example of processing the reception status 154 generated by the GPS receiving unit 101, if it is thought that the vehicle's position detection accuracy will decrease depending on the number of GPS satellites that have been acquired and the positional relationships between the GPS satellites, the reception status 154 is set to a value of "deteriorated" (meaning poor reception), and otherwise the reception status 154 is set to a value of "good" (meaning good reception).

The on-track position estimation unit 102 calculates and estimates the on-track position 152 based on the latitude and longitude 151 received from the GPS receiving unit 101 and the track geometry 155 received from the route information management unit 105, and transmits this to the vehicle speed estimation unit 103, the route information management unit 105, and the driving assistance content generation unit 109. Here, the track geometry 155 represents the shape of the track on which the vehicle is traveling as point sequence data, and each point in the sequence is associated with a value representing latitude, longitude, and absolute position along the track. A typical example of a value representing absolute position along the track is kilometers (hereinafter, this parameter will be referred to as "kilometers").

For example, car navigation systems generally implement technology that maps the position of a moving vehicle along a predetermined route based on the latitude and longitude of the moving vehicle obtained by GPS. The on-track position estimation unit 102 also compares track shape 155 data with latitude and longitude 151 to estimate on-track position 152 corresponding to latitude and longitude 151, making it possible to apply this car navigation system technology.

The vehicle speed estimation unit 103 calculates and estimates the estimated speed 153 based on the on-track position 152 received from the on-track position estimation unit 102, and transmits it to the driving assistance content generation unit 109. For example, the estimated speed 153 can be calculated by dividing the difference between the on-track positions 152 of adjacent periods by the time for one period.

The operation unit 104 accepts input from the driver and transmits route information 156 to the route information management unit 105. For example, a specific example of hardware for the operation unit 104 is the touch panel of a mobile terminal that implements this driving assistance device. Here, the contents of the route information 156 include, for example, the current station, destination station, train type, and train number.

The track information management unit 105 receives track information 156 from the operation unit 104 and on-track position 152 from the on-track position estimation unit 102, and transmits track geometry 155 to the on-track position estimation unit 102 and running distance between stations 157 to the position and speed correction information generation unit 106. As described above, track geometry 155 is a point sequence data representation of the shape of the track on which the vehicle is running. The track information management unit 105 also prepares a database of track geometry data for routes to which the driving assistance device may be applied.

Furthermore, in order to improve the efficiency of the search process for on-track positions 152 within the on-track position estimation unit 102, the route information management unit 105 transmits the track shape 155 to the on-track position estimation unit 102, limiting it to data on sections where the vehicle is likely to travel based on the route information 156.

On the other hand, the running distance 157 is the distance between stations between which the vehicle is thought to be currently traveling, and is information determined based on the train number included in the received route information 156, the timetable information previously stored in the route information management unit 105, the current time, and the received on-track position 152. The reason the received on-track position 152 is included as a basis for determining the running distance 157 is to prepare for the possibility that a delay occurs and the appropriate running distance 157 cannot be extracted by comparing the timetable information with the current time.

The position and speed correction information generation unit 106 receives the reception status 154 from the GPS receiving unit 101, the running station interval 157 from the route information management unit 105, and the characteristic sound detection result 160 from the characteristic sound detection unit 108, and generates and transmits position and speed correction information 158 to the driving assistance content generation unit 109. Here, the position and speed correction information 158 is data that includes a set of four types of information: time, vehicle position, vehicle speed, and positive or negative acceleration. Details of the processing by the position and speed correction information generation unit 106 will be described later.

The sound collection unit 107 is equipped with a microphone for collecting ambient sounds, including those inside and outside the vehicle, and constantly collects sound, transmitting the collected sound results to the characteristic sound detection unit 108 as vehicle sound data 159. The sound collection unit 107 is expected to be installed inside the vehicle, but if the collection of characteristic sounds (the multiple characteristic sounds shown in the characteristic sound expression templates in Figures 3 and 4), which will be described later, is considered, it is preferable to install it inside a vehicle with a driver's cab (for example, the lead car).

The characteristic sound detection unit 108 searches for the presence of a specific type of characteristic sound in the vehicle sound data 159 received from the sound collection unit 107, and when a specific type of characteristic sound is detected, it transmits the type of detected characteristic sound and the time of detection as a characteristic sound detection result 160 to the position and speed correction information generation unit 106. Examples of characteristic sounds will be explained later in the detailed processing of the position and speed correction information generation unit 106.

A specific example of the processing performed by the characteristic sound detection unit 108 will now be described.
The characteristic sound detection unit 108 compares the past few seconds of vehicle sound data 159 with a plurality of prepared template data of characteristic sounds at a predetermined calculation cycle. If it is determined that any of the prepared characteristic sounds is included in the past few seconds of vehicle sound data 159, the type of detected characteristic sound and the time of detection are stored in the characteristic sound detection result 160. Examples of characteristic sound detection methods include a method of determining the similarity of frequency spectra obtained by frequency analysis, a method of image matching (pattern matching) of frequency spectra, and a method of image matching (pattern matching) of spectrograms.

The driving assistance content generation unit 109 generates driving assistance content 161 using the track position 152, estimated speed 153, reception status 154, and position/speed correction information 158 as input, and transmits it to the driving assistance content transmission unit 110. Here, the driving assistance content 161 includes at least data on the driving pattern of the vehicle equipped with the driving assistance device. The driving pattern data is time-series data on the vehicle position and vehicle speed, and is information on a unit of train operation, such as for each station-to-station trip or each route.

It is desirable that the driving assistance content 161 include time-series data on power consumption estimated from the driving pattern data, in addition to driving pattern data. This allows the user of the driving assistance device to check the relationship between the driving pattern between stations and the resulting power consumption. Furthermore, for comparison, driving pattern data and power consumption data for an ideal driving style from the perspective of energy conservation can be prepared in advance and included in the driving assistance content 161, allowing the user of the driving assistance device to check the difference between the ideal driving style and the current driving style, along with the difference in power consumption. Details of the processing by the driving assistance content generation unit 109 will be described later.

The driving assistance content transmission unit 110 visualizes and outputs the data of the driving assistance content 161 to the user of the driving assistance device. Figure 11 is a diagram showing an example of visualization of the driving assistance content 161. One visualization example is a graph with the vehicle position on the horizontal axis and the vehicle speed on the vertical axis (the graph shown at the top of Figure 11). If the driving assistance content 161 includes driving pattern data for comparison, comparing the data can be made easier by overlaying and displaying the driving pattern data.

Furthermore, if the driving assistance content 161 also includes power consumption data, the relationship between driving patterns and power consumption can be easily understood by also visualizing a graph with vehicle position on the horizontal axis and power consumption on the vertical axis (graph shown at the bottom of Figure 11). Furthermore, by also displaying the amount of power consumed between stations and driving time in a table or other format (table shown at the bottom of Figure 11), the overall evaluation results for the entire route between stations can be understood.

Next, an example of processing by the position/speed correction information generating unit 106 will be described with reference to FIG.
2 is a diagram showing an example of a flowchart of the process executed by the position/speed correction information generating unit 106. Each process is executed by the position/speed correction information generating unit 106, and therefore, the description of the process will be omitted below.

In STEP 201, the contents of the characteristic sound detection result 160 are checked to see if a new characteristic sound has been detected by the characteristic sound detection unit 108. If a new characteristic sound detection result 160 exists (yes), proceed to STEP 202; if not (no), proceed to STEP 203.

In STEP 202, the new characteristic sound detection result 160 confirmed in STEP 201 is registered in the characteristic sound history table. The characteristic sound history table is a table that manages the detected characteristic sound type together with the detection time. Figures 5, 6, and 7 show examples of the characteristic sound history table.

In STEP 203, the contents of reception status 154 are checked, and if it is in a "deteriorating" state (yes), proceed to STEP 204; if not (no), exit this processing flow and end the process.

In STEP 204, a characteristic sound expression template corresponding to the running station interval 157 is read from internal memory. The characteristic sound expression template is fixed data that defines the types and order of characteristic sounds detected at specific positions and speeds within a section where it is known in advance that the reception condition 154 will be "deteriorating." Figures 3 and 4 show examples of characteristic sound expression templates.

The characteristic sound expression template shown in Figure 3 is an example that corresponds to the timing when the train stops at a station, i.e., when the speed changes from positive to zero at the target stopping position at the station. The contents are explained below in chronological order (written in order to match Figure 3).

<Step 1> When a vehicle stops at a station, the air brakes activate just before the vehicle stops, causing a sliding noise between the brake pads and wheels (air brake sliding noise, shown in Figure 3).

<Step 2> When the driver confirms that the vehicle has stopped, he moves the master controller handle position significantly to near the maximum normal brake position, generating a sound associated with operating the master controller handle (master controller handle braking operation sound, shown in Figure 3).

<Step 3> After confirming safety on the platform, the crew operates the switch to open the doors. At this time, a warning sound will sound to notify passengers that the doors will be opening (door opening warning sound, shown in Figure 3).

<Step 4> Next, an operating sound is generated when the door leaf operates and opens completely (door opening sound, as shown in Figure 3).

The characteristic sound expression template shown in Figure 4 is an example that corresponds to the timing when the train departs from Station A, i.e., when the speed changes from zero to positive at the target stopping position at Station A. The contents are explained below in chronological order (written in order to match Figure 4).

<Step 1> When a train departs from a station, a departure melody is played to inform station passengers on the platform of the departure (departure melody (Station A) shown in Figure 4).

<Step 2> After the train crew and station staff confirm the safety of the platform, the train crew operates the switch to close the doors. At this time, a warning sound is first sounded to notify passengers that the doors are closing (see Figure 4, door closing warning sound).

<Step 3> Next, an operating sound is generated when the door leaf operates and closes completely (door closing sound, as shown in Figure 4).

<Step 4> If a conductor is on board, a buzzer will sound in the driver's cab to inform the driver that the conductor has confirmed that the doors are closed (door closed confirmation buzzer sound shown in Figure 4).

<Step 5> The driver moves the master controller handle position from near the maximum normal brake position toward the powering side, generating a sound associated with the master controller handle operation (master controller handle powering operation sound shown in Figure 4).

<Step 6> Immediately after operating the master controller handle, the air brake is released, causing the sound of air escaping from the brake cylinder (compressed air exhaust sound, as shown in Figure 4).

The characteristic sound expression templates illustrated in Figures 3 and 4 are prepared for each station, and in STEP 204, the characteristic sound expression template corresponding to the running distance 157 is read from internal memory. The reason for preparing separate characteristic sound expression templates for each station is that, for example, the departure melody included in the departure template illustrated in Figure 4 differs depending on the station, and therefore the characteristic sounds may differ.

In STEP 205, for the time since the most recent time when reception status 154 switched to "worsened", it is checked whether two or more of the characteristic sounds present in the characteristic sound expression template loaded in STEP 204 are present in the characteristic sound history table in the default order. Here, the default order means following the order defined in the characteristic sound expression template in the correct order. Furthermore, if multiple characteristic sound expression templates have been loaded, processing is performed for each characteristic sound template.

In the following (1) to (6), we will explain an example of the processing in STEP 205 and the output of the determination result in the next STEP 206 using the characteristic sound expression templates in Figures 3 and 4 and the characteristic sound history tables in Figures 5, 6, and 7.

(1) Example of processing based on the characteristic sound expression template of Fig. 3 and the characteristic sound history table of Fig. 5 The types of characteristic sounds present in the characteristic sound expression template of Fig. 3 are four: air brake sliding sound, master controller handle braking operation sound, door opening warning sound, and door opening operation sound. A search is made to see if each of these characteristic sounds exists in the characteristic sound history table of Fig. 5, and if it does exist, the time listed in the characteristic sound history table of Fig. 5 is checked.

In the example of the characteristic sound history table in FIG. 5, the following characteristic sounds can be confirmed.
・Air brake sliding sound: Detected at 15:15:00 ・Master control handle braking operation sound: Detected at 15:15:02 ・Door opening warning sound: Detected at 15:15:03 ・Door opening operation sound: Detected at 15:15:06

If the characteristic sounds are arranged in the order of their detection time, they will be: air brake sliding sound → master control handle braking operation sound → door opening warning sound → door opening sound. If we check the sequence information of the characteristic sound expression template in Figure 3 in this order, we get "1" → "2" → "3" → "4", which shows that they are arranged in correct order.

As a result, four characteristic sounds whose order matches that of the characteristic sound occurrence template in Figure 3 have been found in the characteristic sound history table in Figure 5, so the result of the determination in STEP 206 is yes. In other words, in example (1), it has been detected that the phenomenon of "a train stopping at a station," defined as the characteristic sound occurrence template in Figure 3, occurred in the time range from 15:15:00 to 15:15:06.

For a normal local train, the time between the air brake sliding sound and the door opening sound is usually within about 10 seconds. If the time interval between characteristic sounds is longer than this, the accuracy of the determination can be improved by adding a process that excludes characteristic sounds that are far apart in time from the determination.

(2) Processing example based on the characteristic sound expression template of FIG. 4 and the characteristic sound history table of FIG. 5 In the characteristic sound history table of FIG. 5, six characteristic sounds (departure melody (Station A) → door close warning sound → door closing operation sound → door close confirmation buzzer sound → master controller handle power operation sound → compressed air discharge sound) whose order matches that of the characteristic sound expression template of FIG. 4 are found. Therefore, the determination result of STEP 206 is yes.
That is, in the example of (2), it is possible to detect that the phenomenon of "a train departs from the station" defined as the characteristic sound occurrence template in Figure 4 occurred in the time range from 15:15:40 to 15:16:01.

(3) Processing example based on the characteristic sound expression template of Fig. 3 and the characteristic sound history table of Fig. 6 In the characteristic sound history table of Fig. 6, the air brake sliding sound is not found among the characteristic sounds described in the characteristic sound expression template of Fig. 3. However, the other three types of characteristic sounds (master control handle braking operation sound → door opening warning sound → door opening operation sound) are found in the order defined in the characteristic sound expression template of Fig. 3, so the determination result in STEP 206 is yes.

(4) Example of processing based on the characteristic sound expression template of Fig. 4 and the characteristic sound history table of Fig. 6 None of the characteristic sounds listed in the characteristic sound expression template of Fig. 4 are found in the characteristic sound history table of Fig. 6. In this case, the determination result of STEP 206 is no.

(5) Example of processing based on the characteristic sound expression template of Fig. 3 and the characteristic sound history table of Fig. 7 In the characteristic sound history table of Fig. 7, the master control handle braking operation sound is found among the characteristic sounds described in the characteristic sound expression template of Fig. 3, but no other characteristic sounds are found. Therefore, the determination result of STEP 206 is no.

(6) Example of processing based on the characteristic sound expression template of Fig. 4 and the characteristic sound history table of Fig. 7 In the characteristic sound history table of Fig. 7, departure melody (station A) → door close warning sound is found in the order defined in the characteristic sound expression template of Fig. 4. In this case, two characteristic sounds whose order matches that of the characteristic sound expression template of Fig. 4 are found in the characteristic sound history table of Fig. 7, so the determination result in STEP 206 is yes.

It is also possible that two or more characteristic sounds whose order matches that of the characteristic sound occurrence template are found in the characteristic sound history table, and at the same time, characteristic sounds whose order does not match are also found in the same characteristic sound history table. In such cases, for example, one method can be to prioritize characteristic sounds with a high "certainty of characteristic sound detection" for the judgment. Regarding the "certainty of characteristic sound detection," one method can be to add the degree of matching at the time of characteristic sound detection as information to the characteristic sound detection result 160 in the characteristic sound detection unit 108, and use this information in the position/speed correction information generation unit 106.

In addition, even if two or more characteristic sounds whose order matches the characteristic sound occurrence template are found in the characteristic sound history table, if the "certainty of characteristic sound detection" is lower than a predetermined threshold, it is possible to change the processing to set the result of the determination in STEP 206 as "no." This makes it possible to eliminate cases where the movement of the vehicle is mistakenly recognized due to picking up the sound of another vehicle.

If the result of the determination in STEP 206 is yes, proceed to STEP 207; if the result of the determination is no, exit this processing flow and end the process.

In STEP 207, position and speed correction information 158 is generated and sent to the driving assistance content generation unit 109. The position and speed correction information 158 is a set of data containing four types of information: time, vehicle position, vehicle speed, and positive and negative acceleration.

For example, if the determination result in STEP 206 is yes for the characteristic sound occurrence template of FIG. 3 and the characteristic sound history table of FIG. 5 corresponding to station A, the data constituting the position/speed correction information 158 is as follows:
・Time: 15:15:02
Vehicle position: kilometers corresponding to the target stop position at Station A Vehicle speed: zero Acceleration sign: negative (because this is the timing when the speed changes from positive to zero)

Here, with regard to time, the timing of the master controller handle braking operation sound is stored as the stopping time, but if the master controller handle braking operation sound is not detected, it is also possible to store a time estimated from the timing of other detected characteristic sounds. For example, since it is generally 2-3 seconds between the master controller handle braking operation sound and the door opening warning sound, if the master controller handle braking operation sound is not detected but the door opening warning sound is, the time 2-3 seconds before the door opening warning sound is detected can be used.

As another example of generating the position/speed correction information 158, if the determination result in STEP 206 is "yes" for the characteristic sound expression template of FIG. 4 and the characteristic sound history table of FIG. 5 corresponding to station A, the data constituting the position/speed correction information 158 will be as follows:
・Time: 15:16:00
Vehicle position: kilometers corresponding to the target stop position at Station A Vehicle speed: zero Acceleration sign: positive (because this is the timing when the speed changes from zero to positive)

Here, with regard to time, the timing of the master controller handle powering operation sound is stored as the departure time, but if the master controller handle powering operation sound is not detected, it is also possible to store a time estimated from the timing of other detected characteristic sounds. For example, since it is generally 1 to 3 seconds between the master controller handle powering operation sound and the compressed air discharge sound, if the master controller handle powering operation sound is not detected but the compressed air discharge sound is, the time 1 to 3 seconds before the compressed air discharge sound is detected can be used.

The explanation so far has focused on the characteristic sounds that are generated when a train stops at or departs from a station, but the locations at which trains stop and depart are not limited to stations. The driving assistance device described above can also be applied to other locations with set stopping locations. Examples of characteristic sounds that are generated when a train stops at a location other than a station include the sound of the air brake sliding and the sound of the master controller handle braking operation. Examples of characteristic sounds that are generated when a train departs from a location other than a station include the sound of the master controller handle powering operation and the sound of compressed air being discharged.

Next, an example of the processing executed by the driving assistance content generation unit 109 will be described with reference to FIGS. 8, 9, and 10. FIG.
FIG. 8 is a diagram showing an example of a flowchart of the process executed by the driving assistance content generation unit 109.
FIG. 9 is a schematic diagram showing an example of driving pattern data generated by the driving support content generation unit 109 in the form of a speed graph between stations.
10 is a diagram showing an example of driving pattern data in the form of a table, where the driving pattern data is composed of time, position, speed, reception state, and power consumption.

The flowchart shown in FIG. 8 is executed by the driving support content generation unit 109, and therefore the description of the entity that performs each of the following processing steps will be omitted.
9 is a speed graph with the horizontal axis representing the position and the vertical axis representing the speed. Here, two stations, Station A and Station B, are shown, and an example of the processing executed by the driving assistance content generation unit 109 will be described along the course of a train traveling from Station A to Station B.

The location of Station A is designated x1, and the location of Station B is designated x4. Furthermore, it is assumed that the reception condition 154 deteriorates near stations due to the influence of roofs, etc., and the reception condition 154 is designated "deteriorated" in the sections x1 to x2 and x3 to x5 at the locations shown in Figure 9. In other sections, the reception condition 154 is designated "good." It is assumed that the driving assistance device is powered on while the train is stopped at Station A.

First, when the train stops at Station A and the driving assistance device is turned on, GPS satellites have not been acquired, so the reception status 154 is in a "deteriorated" state. Therefore, in the flowchart shown in Figure 8, the determination in STEP 801 is yes, and the process proceeds to the next STEP 807.

Next, in the determination of STEP 807, there is no driving pattern data before the reception condition deteriorated, i.e., before the power was turned on, so the result is no. Therefore, processing proceeds to STEP 811, but because there is no speed data, power consumption estimation is not performed.

In the example driving pattern data shown in Figure 10, the range is from 9:14:00 to 9:14:59. The reception status 154 is "deteriorating," and there is no position or speed data, so power consumption cannot be estimated. For this reason, no data exists, as it is not written into the driving pattern data.

At the timing when the train departs from station A, the position and speed correction information 158 is input to the driving support content generation unit 109. Here, the input position and speed correction information 158 is as follows.
・Time: 9:15:00
Vehicle position: x1
Vehicle speed: Zero Acceleration sign: Positive

At this stage, the reception status 154 remains "deteriorating," so in the flowchart shown in Figure 8, processing continues in the order of STEP 801 → STEP 807 → STEP 811. At this stage, no data exists at that time.

When the train departs from station A and travels to position x2, the reception condition 154 changes from "deteriorating" to "good." At this point, in the flowchart shown in Figure 8, the determination in STEP 801 is no.

Next, in STEP 802, the on-track position 152 (x2), estimated speed 153, and reception status 154 ("good") are written into the driving pattern data. This timing corresponds to the data at 9:15:11 in Figure 10. Note that at this stage, there is still no data before 9:15:10.

Because the reception condition 154 has changed from "deteriorating" to "good," the determination in the following STEP 803 is yes, and the process proceeds to STEP 804. Here, the driving assistance content generation unit 109 determines yes in STEP 804 because there is position and speed correction information 158 received at the time the train departs from Station A.

In the next step, STEP 805, a complementary pattern is generated. A complementary pattern is time-series data consisting of time, position, and speed. Here, based on the time, position, and speed related to the departure from Station A contained in the latest position and speed correction information 158 and the time, position, and speed at the time when the reception condition 154 changed from "deteriorating" to "good," time-series data is generated that complements these.

One example of a method for generating a complementary pattern is to search a database containing past driving data (time, location, and speed) for data that matches certain conditions. The conditions here are the location and speed of the start and finish points of the section to be complemented, as well as the driving time between them.

To adopt this method, it is necessary to obtain driving data from commercial operations on the target route and section in advance using a vehicle information device or other device that can track position and speed, and store it in a database. Furthermore, by storing not only actual driving data from the past in this database, but also virtual driving data created through computer simulations, it is possible to prepare candidate driving patterns to be used as supplementary patterns.

In STEP 806, the complementary pattern generated in STEP 805 is applied to the sections where the reception condition is "deteriorated" and where position and speed data are missing, and the driving pattern data is updated. The data in the range indicated by the bold frame 1001 in Figure 10 is the applied complementary pattern. In the speed graph shown in Figure 9, the speed pattern from x1 to x2 (dashed line) corresponds to the complementary pattern.

In the next step 811, the updated driving pattern data is referenced, and power consumption is estimated for the range in which speed data exists. This corresponds to the range from 9:15:00 to 9:15:11 shown in Figure 10 (the range indicated by the bold frame 1004 in Figure 10).

Here, an example of a method for estimating power consumption from speed data is the following method.
a) In accordance with the equation of motion, the force acting on the train in the direction of travel is calculated from the train weight and the acceleration calculated from the time difference of the speed data.
b) The force in the direction of travel calculated in a) above is taken into account, along with the running resistance calculated from the train weight, speed data, and track conditions such as gradient, to determine the driving force generated by the train's drive unit.
c) The power is calculated from the driving force and speed calculated in b) above, and the power consumption is calculated by taking into account the loss of efficiency in the path from the energy source to the generation of the power.

While the train is traveling from x2 to x3, the reception status 154 remains "good." Therefore, the processing flow for this section is STEP 801 → STEP 802 → STEP 803 → STEP 811 in the flowchart shown in Figure 8. In the travel pattern database shown in Figure 10, this corresponds to the data from 9:15:11 to 9:17:30.

When the train passes position x3, the reception status 154 becomes "deteriorating." The processing flow at this point in time is shown in the flowchart in Figure 8, proceeding from STEP 801 to STEP 807 to STEP 808. Until new position and speed correction information 158 is received, the determination in STEP 808 will be no, and the process will proceed to STEP 811. During this time, there is no data in the bold frame 1002 in Figure 10, and the corresponding power consumption cannot yet be estimated (the range shown by the bold frame 1005 in Figure 10).

At the time when the train stops at position x4, that is, station B, the position and speed correction information 158 is input to the driving support content generation unit 109. Here, the input position and speed correction information 158 is as follows.
・Time: 9:17:42
Vehicle position: x4
Vehicle speed: zero Acceleration sign: negative Since new position and speed correction information 158 has been received, the determination in STEP 808 is yes.

In the next step, STEP 809, a complementary pattern is generated. Here, time series data that complements the time, position, and speed related to the stop at Station B contained in the latest position and speed correction information 158 and the time, position, and speed at the previous timing when the reception condition 154 changed from "good" to "deteriorating" is generated based on these.

101
In STEP 810, the driving pattern data is updated using the generated complementary pattern. That is, the data within the range indicated by the thick frame 1002 in Fig. 10 is the complementary pattern. Also, in the speed graph shown in Fig. 9, the speed pattern from x3 to x4 (dashed line) corresponds to the complementary pattern.

In the next step 811, the updated driving pattern data is referenced, and power consumption is estimated for the range in which speed data exists. This corresponds to the range from 9:17:31 to 9:17:42 shown in Figure 10 (the range enclosed by the bold frame 1005 in Figure 10).

While the train is stopped at station B, the reception status 154 remains "deteriorating." In Figure 8, the process proceeds in the order STEP 801 → STEP 807 → STEP 808. Here, no new position and speed correction information 158 is received until departure, so the determination in STEP 808 is no. The process proceeds to STEP 811, but during this time there is no data, and the corresponding power consumption cannot yet be estimated.

Subsequently, at the timing when the train departs from position x4, i.e., station B, the position and speed correction information 158 is input to the driving support content generation unit 109. The position and speed correction information 158 input here is as follows.
・Time: 9:18:10
Vehicle position: x4
Vehicle speed: zero Acceleration sign: positive Since new position and speed correction information 158 has been received, the determination in STEP 808 is yes.

In the next step, STEP 809, a complementary pattern is generated. Here, based on the time, position, and speed of the train departing from Station B, which is included in the latest position and speed correction information 158, and the time, position, and speed of the train stopping at Station B, whose position and speed are most recently known, time series data is generated that complements these. In other words, the complementary pattern in this case is data in which the position remains at Station B and the speed is zero.

Next, in STEP 810, the running pattern data is updated using the generated complementary pattern. The data from 9:17:43 to 9:18:10 shown in Figure 10 is the complementary pattern. In the following STEP 811, the updated running pattern data is referenced, and the amount of power consumption is estimated for the range in which speed data exists. Since the train is stopped at this point, the estimated power consumption is zero.

When the train departs station B and travels to position x5, reception condition 154 changes from "deteriorating" to "good." Subsequent processing is the same as the processing when the train travels to x2, as described above. In the flowchart shown in Figure 8, processing is performed in the following order: STEP 801 → STEP 802 → STEP 803 → STEP 804 → STEP 805 → STEP 806 → STEP 811.

That is, in STEP 805, using the position and speed correction information 158 related to the departure from Station B, a supplementary pattern is generated for the range enclosed by the bold frame 1003 in Figure 10, and in STEP 806, the running pattern data is updated. In the speed graph shown in Figure 9, the speed pattern from x4 to x5 (dashed line) corresponds to the supplementary pattern. Furthermore, for the section where the speed data has been supplemented, power consumption is estimated and reflected in the running pattern data in STEP 811 (the range enclosed by the bold frame 1006 in Figure 10).

Example 2 is a driving assistance device that detects obstacles on the track on which a vehicle is traveling and communicates the detection results to the driver. Here, obstacle detection is an important function for maintaining safe and secure operation in a situation where driver licenses are becoming increasingly unnecessary as autonomous driving becomes more prevalent.

The driving assistance device has a means for detecting obstacles, and this obstacle detection means is equipped with multiple obstacle detection logics, switching between them based on the vehicle position and vehicle speed estimated using GPS.

In the driving assistance device of Example 2, as in Example 1, in sections where GPS reception is poor, the vehicle position and vehicle speed are estimated based on sound data inside and outside the vehicle collected by a sound collection unit provided in the driving assistance device. This makes it possible to supplement information on the vehicle position and vehicle speed in sections where the vehicle position and vehicle speed cannot be estimated using GPS in the obstacle detection logic switching process. This makes it possible to use the driving assistance device even in sections where GPS reception is poor, leading to improved obstacle detection capabilities for the vehicle.

FIG. 12 is a diagram illustrating a functional configuration of a driving assistance device according to a second embodiment of the present invention and an overview of each function.
The driving assistance device is composed of a GPS receiving unit 101, an on-track position estimating unit 102, a vehicle speed estimating unit 103, an operation unit 104, a route information managing unit 105, a position and speed correction information generating unit 106, a sound collecting unit 107, a characteristic sound detecting unit 108, an obstacle detection logic unit 1209, a detection result transmitting unit 1210, and an obstacle detection sensor unit 1211.

The components from the GPS receiving unit 101 to the characteristic sound detecting unit 108 of the driving assistance device according to the second embodiment are the same as those of the first embodiment shown in FIG.
The following describes the obstacle detection logic unit 1209, the detection result transmission unit 1210, and the obstacle detection sensor unit 1211, which are components specific to the second embodiment.

The obstacle detection sensor unit 1211 uses a camera, laser radar, millimeter wave sensor, or other sensor to constantly sense the track ahead of the vehicle and transmits the sensing results 1262 to the obstacle detection logic unit 1209. The obstacle detection sensor unit 1211 is intended to be a device that is externally connected to a mobile terminal equipped with a driving assistance device, but this does not apply if the mobile terminal's built-in functions, such as a camera function, can be used.

The obstacle detection logic unit 1209 receives the on-track position 152 from the on-track position estimation unit 102, the estimated speed 153 from the vehicle speed estimation unit 103, the reception status 154 from the GPS reception unit 101, the position and speed correction information 158 from the position and speed correction information generation unit 106, and the sensing result 1262 from the obstacle detection sensor unit, and transmits the obstacle detection result 1261 to the detection result transmission unit 1210. The obstacle detection result 1261 is, for example, binary data indicating the presence or absence of an obstacle. Alternatively, it may include a graded warning according to the degree of danger depending on the type of obstacle detected or the certainty of the detection result.

The obstacle detection logic unit 1209 determines whether an obstacle has been detected on the track ahead of the vehicle equipped with the driving assistance device based on the sensing result 1262. In doing so, it has multiple detection logics for determining whether an obstacle has been detected, and switches between the multiple detection logics depending on the position and speed. Regarding the position and speed used to switch the detection logic, if the reception condition 154 is "good," the on-track position 152 and estimated speed 153 are used, and if the reception condition 154 is "deteriorating," the position and speed information included in the position and speed correction information 158 is used.

The reason for the need to switch between multiple detection logics depending on location is that, for example, the types of obstacles on the track that could potentially come into contact with a railway vehicle and cause an accident may vary depending on their location on the line. Specifically, within a station, this could be a person falling from the platform; at a railroad crossing, this could be an entering car, bicycle, or person; and near residential areas, this could be a futon, etc. When the computational resources available for detection are limited, it is desirable from the perspective of obstacle detection accuracy to prioritize the use of detection logic specialized for obstacles that are more likely to occur depending on the location.

In addition, since the location where obstacle detection is prioritized may differ depending on vehicle speed, it is expected that multiple detection logics will need to be switched depending on vehicle speed. The maximum braking distance that a railway vehicle can apply when an obstacle is detected varies depending on vehicle speed. The higher the vehicle speed, the longer it takes to stop, and the longer the distance it will travel during that time. Therefore, the higher the vehicle speed, the more priority it needs to give to detecting obstacles farther away from the vehicle. On the other hand, when the vehicle speed is low, or in the extreme case when the vehicle is stopped, the more priority it needs to give to detecting obstacles closer to the vehicle. In this way, using long-range and short-range detection logic depending on speed is desirable from the perspective of obstacle detection accuracy.

The detection result transmission unit 1210 receives the obstacle detection result 1261 from the obstacle detection logic unit 1209 and transmits the detection result to the driver. Examples of means for transmitting the result to the driver include a display on the screen of a mobile device implemented in the driving assistance device and sound output from the sound output unit of the mobile device.

The detection result transmission unit 1210 may also transmit the detection results received from the obstacle detection result 1261 to a braking/driving system of the railway vehicle (not shown). That is, the detection result transmission unit 1210 transmits the detection results to at least one of the crew and the braking/driving system. When the braking/driving system receives data indicating the presence of an obstacle as an obstacle detection result, it takes safety measures such as applying the necessary brakes, such as an emergency stop.

Furthermore, the obstacle detection logic unit 1209, detection result transmission unit 1210, and obstacle detection sensor unit 1211 of Example 2 may be provided in a form in which they are installed together with the driving assistance content generation unit 109 and driving assistance content transmission unit 110 of Example 1. This allows the driving assistance function of Example 2 to be added to the driving assistance function of Example 1.

Although Examples 1 and 2 have been described above as embodiments of the present invention, the present invention is not limited to the above-described examples, and various modifications are possible without departing from the spirit and scope of the present invention.

101 GPS receiving unit, 102 on-track position estimating unit, 103 vehicle speed estimating unit,
104 operation unit, 105 route information management unit, 106 position and speed correction information generation unit,
107 sound collection unit, 108 characteristic sound detection unit, 109 driving assistance content generation unit,
110 Driving assistance content transmission unit, 151 Latitude and longitude, 152 On-orbit position,
153 Estimated speed, 154 Reception status, 155 Track shape, 156 Route information,
157 Running station interval, 158 Position speed correction information, 159 Vehicle sound data,
160 characteristic sound detection result, 161 driving assistance content,
1001 to 1003: Data portion updated by complementary patterns in driving pattern data;
1004 to 1006: Power consumption estimation part based on updated data in the driving pattern data;
1209 obstacle detection logic unit, 1210 detection result transmission unit,
1211 obstacle detection sensor unit, 1261 obstacle detection result, 1262 sensing result

Claims (8)

A driving assistance device that provides driving assistance based on a vehicle position and a vehicle speed of a vehicle traveling on a track,
a sound collection unit that collects sounds inside and outside the vehicle, including at least sounds generated from equipment mounted on the vehicle, as vehicle sound data, the sound collection unit being provided in a leading car of the vehicle;
The vehicle position and the vehicle speed are estimated based on an appearance order of a plurality of characteristic sounds included in the vehicle sound data collected by the sound collection unit ,
The plurality of characteristic sounds are at least two of the following sounds: a sliding sound between the wheels and brake shoes when the air brakes of the vehicle are in use; an operating sound of the master controller handle of the vehicle; a warning sound for passengers when the doors of the vehicle are opened or closed; a door operation sound when the doors of the vehicle are opened or closed; a departure melody sound in the premises of the station where the vehicle stops; a sound that notifies confirmation that the doors of the vehicle are closed; and a sound of compressed air being discharged when the air brakes of the vehicle are released.
A driving assistance device characterized by: The driving assistance device according to claim 1,
a GPS receiving unit for receiving GPS information;
The vehicle position and the vehicle speed are calculated by
In a section where the reception state of the GPS information is good, the estimation is made based on the GPS information received by the GPS receiving unit, and in a section where the reception state of the GPS information is poor, the estimation is made based on the order in which the plurality of characteristic sounds appear. The driving assistance device according to claim 2,
A driving assistance device characterized in that, in an area where the reception state of the GPS information is poor, it is estimated that the vehicle will at least stop or start based on the order in which the plurality of characteristic sounds appear. The driving assistance device according to claim 3,
The stop station is included in the section where the reception state of the GPS information is poor.
A driving assistance device characterized by: A driving assistance device according to any one of claims 1 to 4 ,
The plurality of characteristic sounds are detected by pattern matching between the frequency analysis result of the vehicle sound data and the frequency analysis result of each of the plurality of characteristic sounds that is prepared in advance as a database.
A driving assistance device characterized by: A driving assistance device according to any one of claims 1 to 5,
As the driving assistance, the history of the vehicle speed is visualized.
A driving assistance device characterized by: A driving assistance device according to any one of claims 1 to 6,
As the driving assistance, an amount of power consumption while the vehicle is traveling is estimated based on the vehicle speed, and a history of the amount of power consumption is visualized.
A driving assistance device characterized by: A driving assistance device according to any one of claims 1 to 7,
an obstacle detection unit that detects an obstacle on the track;
the obstacle detection unit switches between a plurality of detection logics for detecting the obstacle depending on at least one of the vehicle position and the vehicle speed;
As the driving assistance, a result of the obstacle detection by the obstacle detection unit is transmitted to at least one of a driver and a braking/driving system of the vehicle.
A driving assistance device characterized by: