Patent Grant
11804080
The present invention relates to a method and a system for scoring, more particularly a method and a system for inspecting and scoring vehicle transportation.
In recent years, vehicles in the vehicle transportation industry, such as buses, passenger transport vehicle, tourist buses, etc., have caused many traffic accidents due to problematic driving behaviors. For example, when turning at an intersection, if a driver does not stop to observe whether there are other pedestrians or vehicles, it will easily lead to car accidents.
Although most of the vehicles have installed driving recorders, the driving recorders can only record driving behaviors of the vehicles, and cannot record driving behaviors of drivers of the vehicles in the vehicle transportation industry. Therefore, it is difficult to evaluate the driving behaviors of the drivers. In addition, some carriers in the vehicle transportation industry have designed a feedback method, such as asking passengers to fill out a questionnaire to rate the drivers' driving behaviors. However, it is too subjective to judge the drivers' driving behaviors based on the results of human scoring alone, and a method for following-up manual inspection is labor-intensive and highly time-consuming. Namely, there is no method for effectively inspecting and scoring drivers' driving behaviors. Therefore, a conventional method for inspecting and scoring drivers' driving behaviors still needs further improvement.
The present invention provides a method and a system for inspecting and scoring vehicle transportation. The present invention can help carriers in the vehicle transportation industry or smart driving developers to build an automatic inspecting and scoring system.
The method for inspecting and scoring vehicle transportation is executed by a cloud server, and includes steps of: executing an intersection analysis to generate a non-stop rate at corners, times of non-stop corners, and an average of minimum cornering speeds; executing a speed analysis to generate a sharp deceleration duration, a rapid acceleration duration, an overspeed duration, an overspeed ratio, and an average of maximum overspeeds; executing a smoothness analysis to generate a rate of heavy accelerator pedaling, times of vehicle body tilting forward, times of vehicle body tilting backward, times of vehicle body rolling, and times of vehicle body vibrations; executing a supervised learning scoring process to generate an audit score according to the non-stop rate at corners, the times of non-stop corners, the average of minimum cornering speeds, the sharp deceleration duration, the rapid acceleration duration, the overspeed duration, the overspeed ratio, the average of maximum overspeeds, the rate of heavy accelerator pedaling, the times of vehicle body tilting forward, the times of vehicle body tilting backward, the times of vehicle body rolling, and the times of vehicle body vibrations.
Moreover, the system for inspecting and scoring vehicle transportation includes the cloud server, and the cloud server executes the intersection analysis to generate the non-stop rate at corners, the times of non-stop corners, and the average of minimum cornering speeds. The cloud server further executes the speed analysis to generate the sharp deceleration duration, the rapid acceleration duration, the overspeed duration, the overspeed ratio, and the average of maximum overspeeds. The cloud server further executes the smoothness analysis to generate the rate of heavy accelerator pedaling, the times of vehicle body tilting forward, the times of vehicle body tilting backward, the times of vehicle body rolling, and the times of vehicle body vibrations. The cloud server further executes the supervised learning scoring process to generate the audit score according to the non-stop rate at corners, the times of non-stop corners, the average of minimum cornering speeds, the sharp deceleration duration, the rapid acceleration duration, the overspeed duration, the overspeed ratio, the average of maximum overspeeds, the rate of heavy accelerator pedaling, the times of vehicle body tilting forward, the times of vehicle body tilting backward, the times of vehicle body rolling, and the times of vehicle body vibrations.
The cloud server receives vehicle information, inertial measurement information, and positioning information of a vehicle device, and the cloud server collects statistics and analyzes the information. Therefore, the present invention can automatically generate judgements of driving behaviors, and can report the judgements to drivers to remind the drivers to improve their driving behaviors. Further, the judgements can be stored to provide the carriers in the vehicle transportation industry or the smart driving developers for references to improve drivers' driving behaviors.
The present invention directly receives information from the vehicle device, and the information is judged by the cloud server to avoid subjective judgement made by humans. Therefore, the present invention can audit and evaluate the drivers' driving behaviors in an objective and efficient manner.
With reference to
The positioning unit 21 generates vehicle position information according to a position of the vehicle. For example, the positioning unit 21 is a GPS unit. The vehicle information acquiring unit 22 generates speed information, brake information, accelerator pedaling information, and door information by sensing a state of the vehicle. For example, the vehicle information acquiring unit 22 is an on-board diagnostics (OBD) unit to sense a current speed, an accelerator pedaling depth, or a door state for generating corresponding information. The IMU 23 generates inertial measurement information by sensing the vehicle. For example, the inertial measurement information includes angular velocity information and acceleration information. The wireless transceiver 24 is connected to the positioning unit 21, the vehicle information acquiring unit 22, and the IMU 23 to receive the vehicle position information, the speed information, the brake information, the accelerator pedaling information, the door information, and the inertial measurement information from the positioning unit 21, the vehicle information acquiring unit 22, and the IMU 23. The wireless transceiver 24 further transmits the vehicle position information, the speed information, the brake information, the accelerator pedaling information, the door information, and the inertial measurement information to the cloud server 10.
A first embodiment of the method for inspecting and scoring vehicle transportation includes steps as follows:
S101: the cloud server 10 executes an intersection analysis to generate a non-stop rate at corners, times of non-stop corners, and an average of minimum cornering speeds.
Further with reference to
For example, the cloud server 10 determines the vehicle position by receiving the vehicle position information from the vehicle device 20, and compares the vehicle position with the traffic sign position obtained from the driving information database 30. When the first distance between the vehicle position and the traffic sign position is smaller than the intersection threshold, it means that the vehicle is close enough to the traffic sign. Generally speaking, the traffic sign is set at an intersection, so when the vehicle is close enough to the traffic sign, it means that the vehicle is currently at the intersection. Because when the vehicle passes through the intersection, the vehicle needs to slow down to maintain driving safety. If the cloud server 10 determines that the vehicle has not decelerated or stopped at the intersection based on the brake information, a driver's driving behavior is determined to be poor. Therefore, the cloud server 10 uses the times of non-stop corners as one of its audit criteria.
In addition, when the vehicle is at the intersection, that is, when the first distance is smaller than the intersection threshold, the cloud server 10 can further determine whether the vehicle is cornering according to the vehicle position information. If the vehicle is cornering, the times of cornering are counted. If the vehicle does not corner, the times of cornering are not counted. The cloud server 10 further records the minimum cornering speed when the vehicle is cornering according to the speed information.
When the audit condition is satisfied, such as when the vehicle completes a trip, that is, the vehicle drives from a start to an end, the cloud server 10 can calculate the non-stop rate at corners, the times of non-stop corners, and the average of minimum cornering speeds according to the times of cornering, the times of non-stop interactions, and the minimum cornering speeds between the start and the end of the trip. For example, the non-stop rate at corners is a number of dividing the times of non-stop corners by the times of cornering. The average of minimum cornering speeds is an average of the minimum cornering speeds at the interactions between the start and the end of the trip.
The intersection analysis uses the vehicle position information, the speed information, and the brake information to determine whether there is a stop when cornering at the intersection, so as to avoid traffic accidents.
Step S102: the cloud server 10 executes a speed analysis to generate a sharp deceleration duration, a rapid acceleration duration, an overspeed duration, an overspeed ratio, and an average of maximum overspeeds.
Further with reference to
For example, the cloud server 10 determines the vehicle position by receiving the vehicle position information from the vehicle device 20, and determines the vehicle speed according to the speed information. The cloud server 10 further receives the route speed limit information from the driving information database 30. Since different driving routes have different speed limits, the cloud server 10 first determines the vehicle position based on the vehicle position information, and determines the location speed limit corresponding to the vehicle position. For example, if the vehicle position is in an urban area, the location speed limit is 50 km/h. But if the vehicle position is on a highway, the location speed limit is 100 km/h. After the cloud server 10 determines the location speed limit information, the cloud server 10 can determine whether the vehicle is overspeeding based on the speed information and the location speed limit information. When the vehicle speed is greater than the location speed limit, the cloud server 10 determines that the vehicle is overspeeding. The cloud server 10 further calculates a duration for which the vehicle is overspeeding as the overspeed duration. When the vehicle is overspeeding, the cloud server 10 also records the maximum overspeed.
When the audit condition is satisfied, such as when the vehicle drives from the start to the end, the cloud server 10 can calculate times of overspeeds and the maximum overspeeds of each time when the vehicle is overspeeding from the start to the end. The cloud server 10 further calculates the average of maximum overspeeds according to the maximum overspeeds. In addition, the audit duration is, for example, a duration from the start to the end of the trip. The cloud server 10 calculates the overspeed ratio, which is obtained by dividing the overspeed duration by the audit duration. The average of maximum overspeeds is an average of the maximum overspeeds from the start to the end, such as dividing a sum of the maximum overspeeds by the times of overspeeds.
In addition, after the cloud server 10 receives the vehicle position information and the speed information, the cloud server 10 determines whether the vehicle is suddenly accelerating or decelerating based on the speed information. If the vehicle is accelerating, the first difference between the maximum vehicle speed and a minimum vehicle speed is greater than a first threshold, it means that the vehicle is accelerating suddenly. Therefore, the cloud server 10 generates the rapid acceleration duration according to a time difference corresponding to the first difference. When the audit condition is satisfied, the cloud server 10 adds all the rapid acceleration durations within the audit duration.
In the same way, when the second difference is greater than the second threshold, it means that the vehicle is decelerating suddenly. Therefore, the cloud server 10 generates the sharp deceleration duration. When the audit condition is satisfied, the cloud server 10 adds all the sharp deceleration durations within the audit duration.
The speed analysis uses the vehicle position information and the speed information to determine situations of overspeeding, rapid acceleration, and sharp deceleration.
Step S103: the cloud server 10 executes a smoothness analysis to generate a rate of heavy accelerator pedaling, times of vehicle body tilting forward, times of vehicle body tilting backward, times of vehicle body rolling, and times of vehicle body vibrations.
Further with reference to
In step S508, when the first absolute value is greater than the first acceleration threshold, and when the first axial acceleration is a positive value, the cloud server 10 generates the times of vehicle body tilting forward, and then the cloud server 10 executes step S504 to determine whether the audit condition is satisfied. But when the first axial acceleration is a negative value, the cloud server 10 generates the times of vehicle body tilting backward, and then the cloud server 10 executes step S504 to determine whether the audit condition is satisfied.
In step S509, when the second absolute value is greater than the second acceleration threshold, the cloud server 10 generates the times of vehicle body rolling, and then the cloud server 10 executes step S504 to determine whether the audit condition is satisfied.
In step S510, when the third absolute value is greater than the third acceleration threshold, the cloud server 10 generates the times of vehicle body vibrations, and then the cloud server 10 executes step S504 to determine whether the audit condition is satisfied.
In step S511, when the audit condition is satisfied, the cloud server 10 calculates the rate of heavy accelerator pedaling during an audit duration according to the duration of heavy accelerator pedaling. But, when the audit condition is dissatisfied, the cloud server 10 receives the inertial measurement information and the accelerator pedaling information from the wireless transceiver of the vehicle device again.
For example, the cloud server 10 determines the accelerator pedaling depth by receiving the accelerator pedaling information from the vehicle device 20. When the accelerator pedaling depth is greater than the depth threshold, it means that the driver is heavily accelerator pedaling, and the cloud server 10 accordingly generates the duration of heavy accelerator pedaling to record a duration for which the driver is heavily accelerator pedaling. When the audit condition is satisfied, the cloud server 10 can record a total duration for which the driver is heavily accelerator pedaling within the audit duration according to the duration of heavy accelerator pedaling. The cloud server 10 can further calculate the rate of heavy accelerator pedaling by dividing the duration of heavy accelerator pedaling by the audit duration.
In addition, the cloud server 10 also receives the inertial measurement information from the vehicle device 20, and determines smoothness of the vehicle based on an acceleration of the inertial measurement information. For example, the first axis is a forward direction of the vehicle, the second axis is a side direction of the vehicle, and the third axis is a vertical direction of the vehicle. When the first absolute value of the first axial acceleration is greater than the first acceleration threshold, it means that the vehicle is advancing or retreating violently, which will cause the vehicle body to tilt forward or backward. The cloud server 10 further determines the vehicle body is tilting forward or backward according to the positive and negative values of the first axial acceleration. For example, when the first axial acceleration is positive, it means that the vehicle is advancing violently, which will cause the vehicle body to tilt backward. When on the contrary, it will cause the vehicle body to tilt forward. When the second absolute value of the second axial acceleration is greater than the second acceleration threshold, it means that the vehicle is turning left or right violently, causing the vehicle body to roll. When the third absolute value of the third axial acceleration is greater than the third acceleration threshold, it means that the vehicle is vibrating violently up and down.
Therefore, whenever the first absolute value is greater than the first acceleration threshold, the cloud server 10 will determine the forward or backward tilt of the vehicle body according to the positive or negative value of the first axis acceleration, and count the times of vehicle body tilting forward or backward. Similarly, whenever the second absolute value is greater than the second acceleration threshold, the cloud server 10 counts the times of vehicle body rolling, and whenever the third absolute value is greater than the third acceleration threshold, the cloud server 10 counts the times of vehicle body vibrations.
When the audit condition is satisfied, the cloud server 10 outputs the times of vehicle body tilting forward, the times of vehicle body tilting backward, the times of vehicle body rolling, and the times of vehicle body vibrations.
In the embodiment, the first acceleration threshold is calculated by multiplying a square of the first axial acceleration and a dynamic adjustment parameter. The second acceleration threshold is calculated by multiplying a square of the second axial acceleration and the dynamic adjustment parameter. The third acceleration threshold is calculated by multiplying a square of the third axial acceleration and the dynamic adjustment parameter. The dynamic adjustment parameter is related to types of vehicles. For example, the dynamic adjustment parameter corresponding to a bus is different from the dynamic adjustment parameter corresponding to a small car.
Further, in another embodiment, the audit duration may be a fixed duration, such as 24 hours, and the cloud server 10 further determines whether any one of a first analysis duration of the intersection analysis, a second analysis duration of the speed analysis, and a third analysis duration of the smoothness analysis exceeds the audit duration. When any one of the first analysis duration, the second analysis duration, and the third analysis duration exceeds the audit duration, the cloud server 10 determines that the audit condition is satisfied. For example, from 12:00 noon on the first day to 13:00 on the second day, the cloud server 10 has executed any of the intersection analysis, the speed analysis, or the smoothness analysis for 25 hours, which exceeds the audit duration, such as 24 hours. Therefore, the cloud server 10 determines that the audit condition is satisfied.
The smoothness analysis is used to determine the driver's behavior of stepping on the accelerator pedaling according to the accelerator pedaling information. The smoothness analysis is further used to analyze whether passenger experience is good according to the inertial measurement information.
Step S104: the cloud server 10 executes a supervised learning scoring process to generate an audit score according to the non-stop rate at corners, the times of non-stop corners, the average of minimum cornering speeds, the sharp deceleration duration, the rapid acceleration duration, the overspeed duration, the overspeed ratio, the average of maximum overspeeds, the rate of heavy accelerator pedaling, the times of vehicle body tilting forward, the times of vehicle body tilting backward, the times of vehicle body rolling, and the times of vehicle body vibrations.
Further with reference to
For example, the intersection machine learning module, the speed machine learning module, and the smoothness machine learning module are neural network modules using supervised learning or multiple regression analysis modules using supervised learning. Moreover, the present invention takes the non-stop rate at corners, the times of non-stop corners, and the average of minimum cornering speeds as key features of the intersection machine learning module, further takes the sharp deceleration duration, the rapid acceleration duration, the overspeed duration, the overspeed ratio, and the average of maximum overspeeds as key features of the speed machine learning module, and further takes the rate of heavy accelerator pedaling, the times of vehicle body tilting forward, the times of vehicle body tilting backward, the times of vehicle body rolling, and the times of vehicle body vibrations as key features of the smoothness machine learning module to train the intersection machine learning module, the speed machine learning module, and the smoothness machine learning module for generating the intersection analyzing score, the speed analyzing score, and the smoothness analyzing score.
The audit score is calculated by the following formula:
audit score=awa+bwb+cwc.
In the formula, a is the intersection analyzing score, b is the speed analyzing score, c is the smoothness analyzing score, wa is an intersection analyzing weight, wb is a speed analyzing weight, and wc is a smoothness analyzing weight.
The intersection analyzing weight, the speed analyzing weight, and the smoothness analyzing weight can be adjusted based on a number of traffic cases and complaint incidents or key audit items. For example, the intersection analyzing weight can be adjusted based on the number of the accident caused by not giving way or improper turning. The speed analyzing weight can be adjusted based on the number of the accident caused by overspeeding or failure to maintain the distance. The smoothness analyzing weight can be adjusted based on the number of the accident caused by suddenly stopping or improper driving. Moreover, with reference to
Step S704: the cloud server 10 further executes a passing station analysis to generate a non-stop rate at stations, times of non-stop stations, and average of minimum passing station speeds.
Further with reference to
For example, the cloud server 10 determines the vehicle position by receiving the vehicle position information from the vehicle device 20, and compares the vehicle position with the station position obtained from the driving information database 30. When the second distance between the vehicle position and the station position is smaller than the station threshold, it means that the vehicle is close enough to the station. Generally speaking, when the vehicle is close enough to the station, it means that the vehicle is currently entering the station. Because when the vehicle passes through the station, the vehicle needs to slow down and stop at the station to let passengers get on and off the vehicle. If the cloud server 10 determines that the vehicle has not decelerated and stopped at the station based on the brake information and the door information, the driver's driving behavior is determined to be poor. Therefore, the cloud server 10 further uses the times of non-stop stations as one of the audit criteria.
When the audit condition is satisfied, the cloud server 10 can calculate the non-stop rate at stations, the times of non-stop stations, and the average of minimum passing station speeds according to a number of the stations and times of non-opening the door at stations. For example, the non-stop rate at stations is obtained by dividing the times of non-stop stations by the number of the stations. The average of minimum passing station speeds is an average of the minimum passing station speeds at the stations.
The passing station analysis uses the vehicle position information and the station position information to determine whether there is a stop when arriving at the station.
Step S705: when the cloud server 10 executes the supervised learning scoring process, the cloud server 10 generates the audit score further according to the non-stop rate at stations, the times of non-stop stations, and the average of minimum passing station speeds.
Further with reference to
Step S904: the cloud server 10 further generates a passing station analyzing score by a passing station machine learning module according to the non-stop rate at stations, the times of non-stop stations, and the average of minimum passing station speeds.
Step S905: the cloud server 10 further adds the intersection analyzing score, the speed analyzing score, the smoothness analyzing score, and the passing station analyzing score by weights to generate the audit score.
In the second embodiment, the present invention further includes the passing station analyzing. The passing station machine learning module is a neural network module using supervised learning or multiple regression analysis module using supervised learning. Similarly, the present invention takes the non-stop rate at stations, the times of non-stop stations, and the average of minimum passing station speeds as key features of the passing station machine learning module to train the passing station machine learning module for generating the passing station analyzing score.
The audit score is calculated by the following formula:
audit score=awa+bwb+cwc+dwd
In the formula, d is the passing station analyzing score, wd is a passing station analyzing weight, and other parameters are the same as those in the first embodiment. Further, the passing station analyzing weight can be adjusted based on the number of the complaints for non-stop at the station or for improper stop not in accordance with the regulations.
As mentioned above, the present invention can automatically generate judgements of driving behaviors, and the judgements can be stored to provide the carriers in the vehicle transportation industry for inspecting the drivers' driving behaviors. The cloud server 10 may further transmit the judgements, such as the audit score, to a head-up display installed in the vehicle. Therefore, the judgements can be further displayed on the head-up display of the vehicle to remind the drivers for improving their driving behaviors.
In addition, the present invention establishes four analyses, appropriately analyzing whether the drive's driving behavior causes danger and whether the passenger has good experience. Further, the results of the four analyses are separately determined, and added by weights. Then, a final score and evaluations are given, so that the vehicle transportation industry can objectively and efficiently evaluate the driver's driving behaviors.
The above details only a few embodiments of the present invention, rather than imposing any forms of limitation to the present invention. Any professionals in related fields of expertise relating to the present invention, within the limitations of what is claimed, are free to make equivalent adjustments regarding the embodiments mentioned above. However, any simple adjustments and equivalent changes made without deviating from the present invention would be encompassed by what is claimed for the present invention.
| Number | Name | Date | Kind |
|---|---|---|---|
| 11548516 | Yu | Jan 2023 | B2 |
| 20210070309 | Kodali | Mar 2021 | A1 |
| 20210166323 | Fields | Jun 2021 | A1 |
| 20220126864 | Moustafa | Apr 2022 | A1 |
| 20220237956 | Kumar | Jul 2022 | A1 |
| 20230098727 | Sahebnassagh | Mar 2023 | A1 |
| Number | Date | Country | |
|---|---|---|---|
| 20230169801 A1 | Jun 2023 | US |
