Vehicle driving behavior evaluation method and system based on ETC data of Internet of Things

Abstract

The invention discloses a vehicle driving behavior evaluation method and system based on ETC data of the Internet of Things, and relates to the technical field of electric digital data processing. The vehicle driving behavior evaluation method based on the ETC data of the Internet of Things comprises the following steps of real-time driving evaluation, emergency lane evaluation, fatigue driving judgment and whole-course driving evaluation. According to the invention, whether driving warning is carried out is judged through the obtained real-time driving behavior evaluation index, then the road data is obtained and combined with the real-time driving data to obtain the emergency lane occupation evaluation index and whether emergency lane occupation verification is carried out is judged, and then whether fatigue driving exists is judged based on the traffic data. And finally, according to the real-time driving behavior evaluation index and the passing data, obtaining a whole-course driving behavior index, and carrying out driving feedback, thereby achieving an effect of more accurately evaluating the vehicle driving behavior, and solving a problem of inaccurate data in high-speed vehicle driving behavior evaluation based on ETC data in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic digital data processing, and in particular to a vehicle driving behavior evaluation method and system based on ETC data of the Internet of Things. Background Art

[0002] With the rapid development of Internet of Things (IoT) technology, intelligent management in the transportation field has become an important means to improve road safety and efficiency. Electronic Toll Collection (ETC) is a typical application of IoT in the transportation field. It collects data on vehicles passing through toll stations quickly through wireless communication, greatly improving vehicle traffic efficiency and driving experience. ETC data based on IoT can not only be used for toll management, but also can be combined with information such as vehicle driving path, speed and time to evaluate and analyze driving behavior. By combining with on-board sensors and external information, these data can be used to identify driving habits, evaluate safety, optimize traffic management and auto insurance pricing, and other scenarios, thereby helping to further improve the intelligent transportation system.

[0003] In the existing technology, the vehicle driving behavior evaluation methods based on ETC data of the Internet of Things have been applied and verified in many fields such as traffic management, insurance pricing and fleet management. These methods mainly rely on the timestamp, location, driving speed and other data collected by the ETC system when the vehicle passes through the toll station, and build a driving behavior evaluation model by combining with the sensor data installed on the vehicle (such as acceleration, direction change) and external traffic information (such as real-time road conditions, weather data). Using machine learning and big data analysis technology, the existing system can identify unsafe driving behaviors such as speeding, sudden braking, and sudden acceleration, and can even generate personalized driving habit evaluation reports based on historical driving behavior data. The application of these technologies can not only help improve road safety, but also realize new insurance models such as "pricing by driving behavior", which helps to encourage safe driving.

[0004] For example, the patent application with publication number: CN114756761A discloses a method and system for modeling the driving behavior habits of new energy vehicle owners based on the big data of the Internet of Vehicles, including: obtaining vehicle information, the vehicle information includes the frame number; the enterprise cloud server logs in to the national new energy vehicle data server, and searches for the vehicle driving data document corresponding to the vehicle according to the frame number; according to the original data obtained from the national new energy vehicle data server, the multi-dimensional data of the driving behavior habits of the owner is obtained to complete the portrait of the driving behavior habits.

[0005] For example, the invention patent with announcement number: CN116305735B announces a computer data remote management system based on the Internet of Things and its method, including: a data acquisition module, an intelligent monitoring module, a model building and analysis module, a probability calculation module, and an intelligent management module; the output end of the data acquisition module is connected to the input end of the model building and analysis module; the output end of the intelligent monitoring module is connected to the input end of the model building and analysis module; the output end of the model building and analysis module is connected to the input end of the probability calculation module; the output end of the probability calculation module is connected to the input end of the intelligent management module.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems: In the existing technology, ETC data is rarely integrated with other IoT data. Relying solely on ETC data for driving behavior recognition leads to insufficient data dimensions and the problem of inaccurate data when evaluating high-speed vehicle driving behavior based on ETC data. Summary of the invention

[0007] The embodiments of the present application solve the problem of inaccurate data when evaluating high-speed vehicle driving behavior based on ETC data in the prior art by providing a vehicle driving behavior evaluation method and system based on ETC data of the Internet of Things, thereby achieving a more accurate evaluation of vehicle driving behavior.

[0008] An embodiment of the present application provides a vehicle driving behavior evaluation method based on ETC data of the Internet of Things, comprising the following steps: acquiring real-time driving data of a vehicle passing through each ETC gantries after entering a highway, obtaining a real-time driving behavior evaluation index based on the real-time driving data and judging whether to issue a driving warning, wherein the real-time driving behavior evaluation index is used to quantify the degree of compliance between the vehicle's real-time driving behavior and the standardized driving behavior on the highway; acquiring road data and combining it with the real-time driving data to obtain an emergency lane occupation evaluation index, judging whether to perform emergency lane occupation verification based on the emergency lane occupation evaluation index, wherein the emergency lane occupation evaluation index is used to quantify the degree of compliance of the vehicle's occupation of the emergency lane; acquiring high-speed vehicle traffic data based on ETC, judging whether there is fatigue driving based on the traffic data; obtaining a full-course driving behavior index based on the real-time driving behavior evaluation index and the traffic data and performing driving feedback, wherein the full-course driving behavior index is used to comprehensively quantify the degree of standardization of the driver's driving behavior.

[0009] Furthermore, the real-time driving data includes vehicle detection speed, number of longitudinal acceleration changes, number of lateral acceleration changes, number of abnormal braking and number of abnormal throttle; the road data includes number of lane changes, number of road lanes, number of turn signal uses, lane departure alarm frequency and average road congestion index; the average road congestion index represents the average of the road congestion index of the road section between two adjacent ETC gantries; the traffic data includes travel time, travel kilometres and number of stops at service areas.

[0010] Furthermore, the specific acquisition process of the real-time driving behavior evaluation index is as follows: number the ETC gantry and obtain reference driving data from a preset database, wherein the reference driving data includes a maximum speed limit and a minimum speed limit; process the vehicle detection speed and the reference driving data to obtain a vehicle detection speed evaluation index, wherein the vehicle detection speed evaluation index is used to quantify the degree of standardization of the vehicle detection speed; perform mean operation on the result of processing the sum of the number of longitudinal acceleration changes, the number of lateral acceleration changes, the number of abnormal braking times and the number of abnormal throttle times and the vehicle detection speed evaluation index to obtain the real-time driving behavior evaluation index.

[0011] Furthermore, the specific process of obtaining the emergency lane occupation evaluation index is as follows: obtaining the emergency lane evaluation weight from a preset database, the emergency lane evaluation weight including the congestion weight and the lane change weight; numbering the driving lanes, and obtaining the lane change index based on the number of road channels and the number of lane changes, the lane change index is used to quantify the probability of the vehicle changing lanes to the emergency lane; obtaining the emergency lane occupation evaluation index based on the obtained number of times the turn signal is used, the lane departure alarm frequency and the average road congestion index and the emergency lane evaluation weight and the lane change index.

[0012] Furthermore, the specific limiting expression of the full driving behavior index is as follows: ; In the formula, Indicates the number of the ETC gantry. , Indicates the total number of ETC gantries. Indicates the number of kilometers traveled. Indicates arrival The real-time driving behavior evaluation index of ETC gantry vehicles, represents the whole driving behavior index, and e represents a natural constant.

[0013] The embodiment of the present application provides a vehicle driving behavior evaluation system based on ETC data of the Internet of Things, including: a real-time driving evaluation module, an emergency lane evaluation module, a fatigue driving judgment module and a full-time driving evaluation module; wherein the real-time driving evaluation module is used to obtain real-time driving data of the vehicle passing through each ETC gantries after entering the highway, obtain a real-time driving behavior evaluation index according to the real-time driving data, and judge whether to issue a driving warning, and the real-time driving behavior evaluation index is used to quantify the degree of compliance between the real-time driving behavior of the vehicle on the highway and the standardized driving behavior; the emergency lane evaluation module is used to obtain an emergency lane occupation evaluation index by obtaining road data and combining the real-time driving data, and judge whether to perform emergency lane occupation verification according to the emergency lane occupation evaluation index, and the emergency lane occupation evaluation index is used to quantify the degree of compliance of the vehicle occupying the emergency lane; the fatigue driving judgment module is used to obtain the traffic data of the vehicle driving on the highway based on ETC, and judge whether there is fatigue driving according to the traffic data; the full-time driving evaluation module is used to obtain a full-time driving behavior index according to the real-time driving behavior evaluation index and the traffic data and provide driving feedback, and the full-time driving behavior index is used to comprehensively quantify the degree of standardization of the driving behavior of the driver.

[0014] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. The real-time driving data obtained is used to obtain the real-time driving behavior evaluation index and determine whether to issue a driving warning. Then, the road data is obtained and combined with the real-time driving data to obtain the emergency lane occupation evaluation index and determine whether to conduct emergency lane occupation verification. Then, based on the acquired traffic data, it is determined whether there is fatigue driving. Finally, the full-course driving behavior index is obtained based on the real-time driving behavior evaluation index and the traffic data, and driving feedback is given, thereby more accurately evaluating the vehicle's driving behavior, thereby achieving a more accurate evaluation of the vehicle's driving behavior, and effectively solving the problem of inaccurate data when evaluating high-speed vehicle driving behavior based on ETC data in the prior art.

[0015] 2. By numbering the ETC gantries and obtaining reference driving data from the preset database, the vehicle detection speed and the reference driving data are processed to obtain the vehicle detection speed evaluation index. Then, the sum of the longitudinal acceleration change times, the lateral acceleration change times, the abnormal braking times and the abnormal throttle times is processed and averaged with the vehicle detection speed evaluation index to obtain the real-time driving behavior evaluation index, thereby evaluating the vehicle driving behavior more timely and achieving a more accurate evaluation of the vehicle driving behavior.

[0016] 3. By obtaining the emergency lane evaluation weight from the preset database and numbering the driving lanes at the same time, the lane change index is obtained based on the number of road channels and the number of lane changes. Then, the emergency lane occupation evaluation index is obtained based on the number of times the turn signal is used, the lane departure alarm frequency and the average road congestion index and the emergency lane evaluation weight and lane change index, so as to judge whether the vehicle occupies the emergency lane more timely, thereby guiding the vehicle driving behavior to be more standardized. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flowchart of a vehicle driving behavior evaluation method based on ETC data of the Internet of Things provided in an embodiment of the present application; Figure 2 A schematic diagram of the change of the real-time driving behavior evaluation index provided in an embodiment of the present application, wherein (a) is a schematic diagram of the change of the real-time driving behavior evaluation index with the vehicle detection speed, (b) is a schematic diagram of the change of the real-time driving behavior evaluation index with the number of changes in longitudinal acceleration, (c) is a schematic diagram of the change of the real-time driving behavior evaluation index with the number of changes in lateral acceleration, (d) is a schematic diagram of the change of the real-time driving behavior evaluation index with the number of abnormal braking times, and (e) is a schematic diagram of the change of the real-time driving behavior evaluation index with the number of abnormal throttle times. DETAILED DESCRIPTION

[0018] The embodiment of the present application solves the problem of inaccurate data when evaluating high-speed vehicle driving behavior based on ETC data in the prior art by providing a vehicle driving behavior evaluation method and system based on ETC data of the Internet of Things. The method solves the problem of inaccurate data when evaluating high-speed vehicle driving behavior based on ETC data in the prior art. The ETC gantries are numbered and reference driving data is obtained from a preset database. Then, the vehicle detection speed and the reference driving data are processed to obtain a vehicle detection speed evaluation index. Then, the result of processing the sum of the number of longitudinal acceleration changes, the number of lateral acceleration changes, the number of abnormal braking times and the number of abnormal throttle times is averaged with the vehicle detection speed evaluation index to obtain a real-time driving behavior evaluation index, and it is judged whether to issue a driving warning based on this. Then, the emergency lane evaluation weight is obtained from the preset database and the driving lane is numbered at the same time, and a lane change index is obtained based on the number of road channels and the number of lane changes. Then, the emergency lane occupation evaluation index is obtained based on the obtained number of turn signal uses, lane departure alarm frequency and average road congestion index, the emergency lane evaluation weight and the lane change index, and it is judged whether to perform emergency lane occupation verification based on this. Then, based on the obtained traffic data, it is judged whether there is fatigue driving. Finally, the full-course driving behavior index is obtained according to the real-time driving behavior evaluation index and the traffic data, and driving feedback is performed, so as to achieve a more accurate evaluation of vehicle driving behavior.

[0019] The technical solution in the embodiment of the present application is to solve the problem of inaccurate data when evaluating high-speed vehicle driving behavior based on ETC data. The overall idea is as follows: By acquiring real-time driving data, real-time driving behavior evaluation indicators are obtained and it is determined whether to issue a driving warning. Then, road data is acquired and combined with real-time driving data to obtain emergency lane occupation evaluation indicators and determine whether to conduct emergency lane occupation verification. Then, based on the acquired traffic data, it is determined whether there is fatigue driving. Finally, based on the real-time driving behavior evaluation indicators and traffic data, the full driving behavior index is obtained and driving feedback is provided, thereby achieving the effect of more accurately evaluating vehicle driving behavior.

[0020] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0021] like Figure 1 As shown, it is a flow chart of a vehicle driving behavior evaluation method based on ETC data of the Internet of Things provided in an embodiment of the present application, the method comprising the following steps: obtaining real-time driving data of a vehicle passing through each ETC gantries after entering the highway, obtaining a real-time driving behavior evaluation index based on the real-time driving data and judging whether to issue a driving warning, the real-time driving behavior evaluation index is used to quantify the degree of compliance between the real-time driving behavior of the vehicle on the highway and the standardized driving behavior, and the driving warning is used to warn the driver to pay attention to restraining driving behavior; obtaining road data and combining it with the real-time driving data to obtain an emergency lane occupation evaluation index, judging whether to perform emergency lane occupation verification based on the emergency lane occupation evaluation index, the emergency lane occupation evaluation index is used to quantify the degree of compliance of the vehicle occupying the emergency lane, and the emergency lane occupation verification is used to detect the actual situation of the vehicle to verify whether the emergency lane is occupied; obtaining the traffic data of the vehicle driving on the highway based on ETC, and judging whether there is fatigue driving based on the traffic data; obtaining a full-time driving behavior index based on the real-time driving behavior evaluation index and the traffic data and performing driving feedback, the full-time driving behavior index is used to comprehensively quantify the degree of standardization of the driver's driving behavior, and the driving feedback includes score visualization and giving driving suggestions.

[0022] In this embodiment, by real-time monitoring and evaluation of driving behavior, driving warnings are issued in a timely manner, which is conducive to preventing dangerous driving behavior and reducing the risk of traffic accidents; and the occupation of emergency lanes is accurately verified to ensure that emergency passages are unobstructed, providing strong support for emergency rescue; through continuous driving behavior monitoring and feedback, the driver's safe driving habits are cultivated, driving skills and safety awareness are improved, and the popularization and compliance of driving behavior norms are promoted, while also more accurately evaluating vehicle driving behavior.

[0023] It should be added that real-time driving data includes vehicle detection speed, number of longitudinal acceleration changes, number of lateral acceleration changes, number of abnormal braking and number of abnormal throttle; road data includes number of lane changes, number of road lanes, number of turn signal uses, lane departure alarm frequency and average road congestion index; the average road congestion index represents the average of the road congestion index of the road section between two adjacent ETC gantries; traffic data includes travel time, travel kilometres and number of stops at service areas.

[0024] It should be understood that the method for obtaining real-time driving data is as follows: the ETC gantry usually obtains the vehicle detection speed through the communication between the on-board unit (OBU) and the road-side unit (RSU), combined with the geomagnetic sensor. The OBU device records the current speed of the vehicle, and the ETC system can periodically collect this information; the longitudinal acceleration and lateral acceleration changes of the vehicle are detected by the inertial sensors (such as accelerometers and gyroscopes) installed on the vehicle and the number of longitudinal acceleration changes and the number of lateral acceleration changes are obtained by counting using counters; the OBU device can obtain engine data, throttle and brake data. If the vehicle brakes suddenly or accelerates quickly in a short period of time, the OBU will record it as an abnormal operation and count the number of abnormal brakes and abnormal throttles. The method for judging whether the vehicle brakes suddenly or accelerates quickly in a short period of time is the time window method. For example, if the speed of the vehicle drops by more than 20 km / h within 2 seconds, it is recorded as an emergency brake. If the speed of the vehicle rises by more than 20 km / h within 2 seconds, it is recorded as a rapid acceleration.

[0025] The method for obtaining road data is as follows: the lane changing situation is monitored by the lane keeping system installed in the vehicle and the number of lane changes is counted. When the vehicle changes lanes to the right, the number of lane changes increases, and vice versa. The number of road channels can be obtained through road sensors, and the ETC system will automatically match the relevant road attributes according to the vehicle's location data. The Internet of Things collects turn signal data from the vehicle and records the number of times the turn signal is turned on and off to obtain the number of times the turn signal is used. The lane departure warning system uses the camera in the vehicle to identify the lane markings to determine whether the vehicle has deviated from the lane, and the OBU collects the alarm frequency of the system to obtain the lane deviation alarm frequency; the corresponding average road congestion index is obtained through the traffic management system in the Internet of Things.

[0026] The method for obtaining traffic data is as follows: the ETC system records the time it takes for a vehicle to pass through the starting ETC gantry and the ending ETC gantry to obtain the travel time; the ETC system calculates the mileage by the distance between the ETC gantries passed by the vehicle; the ETC system records the time and number of times a vehicle enters and exits the service area entrance and exit to obtain the number of server stops; the above data obtained can be used for driving behavior analysis, road congestion management, driving safety warnings, etc., which is conducive to traffic management departments and car owners to have a clearer understanding and management of driving safety, traffic efficiency and other aspects.

[0027] Furthermore, the specific process of obtaining the real-time driving behavior evaluation index is as follows: number the ETC gantries and obtain reference driving data from a preset database, the reference driving data including the maximum speed limit and the minimum speed limit; process the vehicle detection speed and the reference driving data to obtain the vehicle detection speed evaluation index, which is used to quantify the degree of standardization of the vehicle detection speed; perform mean calculation on the sum of the number of longitudinal acceleration changes, the number of lateral acceleration changes, the number of abnormal braking times and the number of abnormal throttle times and the vehicle detection speed evaluation index to obtain the real-time driving behavior evaluation index; The specific restriction expressions of real-time driving behavior evaluation indicators are as follows: ; Where n is the number of the ETC gantry, , Indicates the total number of ETC gantries. Indicates the vehicle detection speed of the vehicle passing through the nth ETC gantry, Indicates the cumulative number of longitudinal acceleration changes when the vehicle reaches the nth ETC gantry. Indicates the number of cumulative lateral acceleration changes when the vehicle reaches the nth ETC gantry. Indicates the number of abnormal braking times accumulated when the vehicle reaches the nth ETC gantry. Indicates the number of abnormal throttle times accumulated when the vehicle reaches the nth ETC gantry. Indicates the maximum speed limit. Indicates the minimum speed limit. Represents the real-time driving behavior evaluation index of the vehicle arriving at the nth ETC gantry.

[0028] In this embodiment, the algorithm combines the number of longitudinal acceleration changes, the number of lateral acceleration changes, the number of abnormal braking times, the number of abnormal throttle times and the vehicle detection speed evaluation index for comprehensive analysis to obtain a real-time driving behavior evaluation index, wherein, when the vehicle detection speed is greater than the maximum speed limit or less than the minimum speed limit, the vehicle detection speed evaluation index is smaller, indicating that the degree of compliance between the vehicle detection speed of the real-time driving behavior and the standardized driving behavior is lower; when the number of longitudinal acceleration changes, the number of lateral acceleration changes, the number of abnormal braking times and the number of abnormal throttle times are greater, the value of the real-time driving behavior evaluation index is smaller, indicating that the degree of compliance between the vehicle driving behavior and the standardized driving behavior is lower.

[0029] like Figure 2 1 and 2 are schematic diagrams showing changes in the real-time driving behavior evaluation index provided by the embodiment of the present application, wherein (a) is a schematic diagram showing changes in the real-time driving behavior evaluation index with the vehicle detection speed, wherein it is assumed that the number of longitudinal acceleration changes, the number of lateral acceleration changes, the number of abnormal braking times, and the number of abnormal throttle times are all 10, (b) is a schematic diagram showing changes in the real-time driving behavior evaluation index with the number of longitudinal acceleration changes, wherein it is assumed that the vehicle detection speed is 90 km / h, the number of lateral acceleration changes, the number of abnormal braking times, and the number of abnormal throttle times are all 10, and (c) is a schematic diagram showing changes in the real-time driving behavior evaluation index with the number of lateral acceleration changes, wherein it is assumed that the vehicle detection speed is 90 km / h, the number of lateral acceleration changes, the number of abnormal braking times, and the number of abnormal throttle times are all 10. The speed is 90km / h, the number of longitudinal acceleration changes, the number of abnormal braking times and the number of abnormal throttle times are all 10. (d) is a schematic diagram of the change of real-time driving behavior evaluation indicators with the number of abnormal braking times, assuming that the vehicle detection speed is 90km / h, the number of longitudinal acceleration changes, the number of lateral acceleration changes and the number of abnormal throttle times are all 10. (e) is a schematic diagram of the change of real-time driving behavior evaluation indicators with the number of abnormal throttle times, assuming that the vehicle detection speed is 90km / h, the number of longitudinal acceleration changes, the number of lateral acceleration changes and the number of abnormal braking times are all 10, and the maximum speed limit is 120km / h and the minimum speed limit is 60km / h.

[0030] As can be seen from Figure (a), when the vehicle detection speed is between the minimum speed limit and the maximum speed limit, the vehicle detection speed evaluation index reaches the maximum, indicating that the vehicle detection speed meets the specifications. In Figures (b), (c), (d) and (e), as the number of longitudinal acceleration changes, the number of lateral acceleration changes, the number of abnormal braking times and the number of abnormal throttle times increase, the value of the real-time driving behavior evaluation index decreases accordingly, indicating that during driving, the vehicle driving behavior deviates from the standard driving behavior more frequently; and the higher the number of longitudinal acceleration changes, the number of lateral acceleration changes, the number of abnormal braking times and the number of abnormal throttle times, it indicates that the driver is accustomed to aggressive driving and frequently performs rapid acceleration, emergency braking and lane change operations, which increases the risks brought by driving behavior. The higher the longitudinal acceleration, the change is mainly caused by acceleration and deceleration operations, especially sudden braking will cause drastic changes in longitudinal acceleration. Therefore, the more abnormal braking times, the more likely the longitudinal acceleration changes. High lateral acceleration changes may indicate frequent lane changes or sharp turns, while high abnormal throttle times indicate that the driver tends to accelerate frequently during driving. This combination often occurs in aggressive driving behaviors, such as frequent weaving in or chasing in traffic. Therefore, when the lateral acceleration changes, the abnormal throttle times may also increase. By evaluating the driving behavior of the driver in real time, it is helpful to detect possible safety hazards and guide the driver to drive in a standardized manner and strictly abide by traffic rules.

[0031] Specifically, the reference driving data is defined by traffic rules and has nothing to do with the ETC gantry, and the maximum speed limit and the minimum speed limit are obtained from a preset database. In a specific embodiment, the minimum speed limit and the maximum speed limit are obtained by querying traffic rules. For example, by consulting the traffic rules, it can be known that the minimum speed limit is 60 kn / h and the maximum speed limit is 120 kn / h.

[0032] Furthermore, the specific process of determining whether to issue a driving warning is as follows: obtaining a real-time driving evaluation threshold from a preset database, the real-time driving evaluation threshold being used to determine the degree of compliance between the vehicle's real-time driving behavior on the highway and the standardized driving behavior; comparing the real-time driving behavior evaluation index with the real-time driving evaluation threshold: if the real-time driving behavior evaluation index is not less than the real-time driving evaluation threshold, no driving warning is issued; if the real-time driving behavior evaluation index is less than the real-time driving evaluation threshold, a driving warning is issued, and the driving warning includes a voice warning and a central control warning; a voice warning indicates that a voice broadcast is used to warn the driver to pay attention to driving habits and inform the driver of the hidden dangers of the current driving behavior; a central control warning indicates that the real-time driving behavior evaluation index is visualized and informed to the driver through the car's central control display screen.

[0033] In this embodiment, the driving warning process evaluates and judges the real-time driving behavior through the preset driving evaluation threshold. When the driving behavior deviates from the standard driving behavior (that is, the real-time driving behavior evaluation index is less than the real-time driving evaluation threshold), feedback is immediately given through voice warnings and central control warnings, effectively preventing potential safety hazards and improving the safety and interactivity of the driving process. At the same time, this process also promotes the popularization and compliance of driving behavior norms, and helps to build a safer and more orderly traffic environment.

[0034] Specifically, the real-time driving evaluation threshold is obtained from a preset database. In a specific embodiment, the real-time driving data corresponding to the situation of violating traffic rules is substituted into the specific restriction expression of the real-time driving behavior evaluation index to obtain the corresponding data set, and the result of the mean operation of the data set is recorded as the real-time driving evaluation threshold.

[0035] Furthermore, the specific process of obtaining the emergency lane occupation evaluation index is as follows: the emergency lane evaluation weight is obtained from the preset database, the emergency lane evaluation weight includes the congestion weight and the lane change weight, the congestion weight is used to indicate the probability of the emergency lane being occupied due to road congestion, and the lane change weight indicates the probability of the vehicle changing lanes and occupying the emergency lane; the driving lanes are numbered, and the lane change index is obtained based on the number of road channels and the number of lane changes, the number of road channels indicates the sum of the total number of driving lanes and the number of emergency lanes, and the lane change index is used to quantify the probability of the vehicle changing lanes to the emergency lane; the emergency lane occupation evaluation index is obtained based on the number of times the turn signal is used, the lane departure alarm frequency and the average road congestion index, the emergency lane evaluation weight and the lane change index; The specific restriction expression of the emergency lane occupation evaluation index is as follows: ; ; Where n is the number of the ETC gantry, , represents the total number of ETC gantries, m represents the number of the driving lane, , represents the total number of driving lanes, Indicates the number of road channels corresponding to the nth ETC gantry. Indicates the lane the vehicle is in when it reaches the nth ETC gantry. Indicates the cumulative number of lane changes when the vehicle reaches the nth ETC gantry. Indicates the lane change indicator when the vehicle reaches the nth ETC gantry. Indicates the number of times the vehicle's turn signal is used when it reaches the nth ETC gantry. Indicates the accumulated lane departure alarm frequency of vehicles arriving at the nth ETC gantry. represents the average road congestion index of the nth ETC gantry, represents the congestion weight, represents the lane change weight, Represents the evaluation index of emergency lane occupation by vehicles arriving at the nth ETC gantry.

[0036] In this embodiment, the algorithm combines the number of times the turn signal is used, the frequency of lane departure alarm, the average road congestion index, the emergency lane evaluation weight and the lane change index for comprehensive analysis to obtain the emergency lane occupation evaluation index. In the formula, based on the average road congestion index, when the lane change index is 1, it indicates that the emergency lane is not occupied, then the impact on the emergency lane occupation evaluation index is minimized; otherwise, when the lane change index is 0, the impact on the emergency lane occupation evaluation index is maximized; wherein, the average road congestion index ranges from 0 to 1, and the congestion weight ranges from 0 to 1, so the product of the average road congestion index and the congestion weight (i.e. ) is also between 0 and 1, and the number m of the driving lane represents the specific lane in which the vehicle is driving. Combined with the number of lane changes, it is helpful to more accurately analyze whether the lane is changed to the emergency lane; in addition, the number of times the turn signal is used is negatively correlated with the emergency lane occupation evaluation index. Without considering the case where the turn signal is not used, as the number of times the turn signal is used increases, the probability that the vehicle is in the driving lane is greater, and the possibility of driving in the emergency lane is smaller (when the vehicle is driving in the emergency lane, the emergency road is usually unobstructed and the number of times the turn signal is used is reduced), and the emergency lane occupation evaluation index is smaller. The higher the frequency of lane departure alarm is, the positive correlation is with the emergency lane occupation evaluation. As the frequency of lane departure alarm increases, the emergency lane occupation evaluation index also increases. It means that the more times the turn signal is used, the smaller the probability of the vehicle driving in the emergency lane, and vice versa. The greater the frequency of lane departure alarm, the more frequently the vehicle deviates from the lane, and the higher the probability of the vehicle deviating to the emergency lane. Generally speaking, the number of lane changes is directly related to the number of road channels. On multi-lane roads (such as highways or urban expressways), drivers have more opportunities to change lanes, so the number of lane changes is often higher, and turn signals should be used when changing lanes. Therefore, in theory, the number of lane changes and the number of times the turn signal is used should be close. By analyzing the evaluation indicators for occupying the emergency lane, it is helpful to detect whether the vehicle occupies the emergency lane in a timely manner, thereby guiding the driver to make corrections in time to form good driving habits, and also urge the driver to strictly abide by traffic regulations.

[0037] Specifically, the congestion weight is a value determined based on the average road congestion index in the preset database, which reflects the importance of the average road congestion level to the evaluation of emergency lane occupation behavior indicators. In practical applications, the weight value that matches the current average road congestion index can be directly retrieved from the preset database. This matching is based on a predefined mapping relationship, that is, a mapping set is established between the average road congestion index and the weight corresponding to the emergency lane occupation evaluation index. By inputting the average road congestion index obtained in real time into this mapping set, the corresponding weight value can be found. In this example, the mapping relationship is a strict one-to-one relationship, and the weight value is limited to the range of 0 to 1.

[0038] Specifically, in this example, the sum of the congestion weight and the lane change weight is 1.

[0039] Furthermore, the specific process of verifying the emergency lane occupancy is as follows: obtaining the emergency lane occupancy threshold from the preset database, and the emergency lane occupancy threshold is used to determine the degree of compliance of the vehicle occupying the emergency lane; comparing the emergency lane occupancy evaluation index with the emergency lane occupancy threshold: if the emergency lane occupancy evaluation index is less than the emergency lane occupancy threshold, the emergency lane occupancy verification is not performed; if the emergency lane occupancy evaluation index is not less than the emergency lane occupancy threshold, the vehicle vision system is activated to verify whether the vehicle is located in the emergency lane. If the vehicle is located in the emergency lane, the driver is immediately reminded to exit the emergency lane, otherwise the emergency lane occupancy verification is exited.

[0040] In this embodiment, the emergency lane occupation verification process obtains the emergency lane occupation threshold through a preset database, and uses the vehicle vision system to perform intelligent verification, thereby realizing automatic identification and processing of illegal emergency lane occupation behaviors; this process not only improves the standardization of emergency lane use, enhances road safety, and improves the efficiency and accuracy of verification; at the same time, the entire verification process is automated, reducing the links of manual judgment and intervention, reducing misjudgments and missed judgments caused by human factors, and improving the fairness and objectivity of the verification.

[0041] Specifically, the threshold value for occupying the emergency lane is obtained from a preset database. In a specific embodiment, the road data corresponding to the situation of occupying the emergency lane in the historical data is substituted into the specific restriction expression of the evaluation index for occupying the emergency lane and the corresponding data set is obtained, and the result of the mean operation of the data set is recorded as the threshold value for occupying the emergency lane.

[0042] Furthermore, the specific restriction expression of the full driving behavior index is as follows: ; Where n is the number of the ETC gantry, , Indicates the total number of ETC gantries. Indicates the number of kilometers traveled. represents the real-time driving behavior evaluation index of the vehicle arriving at the nth ETC gantry, represents the whole driving behavior index, and e represents a natural constant.

[0043] In this embodiment, the algorithm combines the mileage and the real-time driving behavior evaluation index for comprehensive analysis to obtain the full-journey driving behavior index, in which the mileage is processed as the base. The larger the mileage, the larger the base and the base is greater than 1. In addition, when the real-time driving behavior evaluation index of each ETC gantry is larger, the result of the mean operation of the real-time driving behavior evaluation index of all ETC gantries is larger, and the full-journey driving behavior index is larger, indicating that the vehicle driving behavior is more standardized. By evaluating the driving behavior of the driver based on the mileage, the vehicle driving behavior evaluation method is more reasonable, and by analyzing the real-time driving behavior evaluation index of each ETC gantry, the vehicle's driving behavior is analyzed more comprehensively, thereby giving the driver more accurate driving feedback.

[0044] Specifically, remember , The real-time driving average is combined with the mileage to obtain the data change table of the full-trip driving behavior index, as shown in Table 1: Table 1 Data changes of the whole driving behavior index

[0045] It can be seen from Table 1 that as the mileage and the real-time driving mean increase, the full-time driving behavior index also increases. For example, when the mileage increases from 60 in the first row to 300 in the fifth row, the real-time driving mean increases from 0.5 in the first row to 0.9 in the fifth row, the full-time driving behavior index also increases from 1.475 in the first row to 2.013 in the fifth row, indicating that the mileage and the real-time driving mean are both positively correlated with the full-time driving behavior index. The analysis of the full-time driving behavior index is conducive to a more comprehensive and accurate assessment of vehicle driving behavior and to standardize the driving habits of drivers.

[0046] Furthermore, the specific steps for determining whether fatigue driving exists are as follows: Step 1, obtain the travel time and the number of service area stops from the traffic data. If the number of service area stops is 0, execute step 3, otherwise execute step 2; Step 2, record the ratio of the travel time to the number of service area stops as the travel time and execute step 3; Step 3, compare the travel time with the preset safe driving time. If the travel time is not less than the preset safe driving time, it is determined that the driver is fatigued driving, otherwise there is no fatigue driving. The preset safe driving time is generally set to 4 hours.

[0047] In this embodiment, the preset safe driving time is set according to the provisions of traffic rules, and the specific steps of judging whether there is fatigue driving are determined by comprehensively considering the travel time and the number of stops at the service area, and using a scientific judgment method, the driver's fatigue driving situation can be accurately and timely identified. When fatigue driving is determined, an early warning can be issued in time to remind the driver to take a rest and ensure driving safety. The method is simple to operate and easy to promote, which helps to prevent traffic accidents caused by fatigue driving and improve the level of road traffic safety.

[0048] Furthermore, the specific content of the driving feedback is as follows: obtaining a full-journey evaluation interval from a preset database, the full-journey evaluation interval including a first evaluation interval and a second evaluation interval; comparing the full-journey driving behavior index with the full-journey evaluation interval: if the full-journey driving behavior index is within the first evaluation interval, the driver's driving behavior evaluation level is recorded as qualified, and the score is visualized on the vehicle's central control display screen; if the full-journey driving behavior index is within the second evaluation interval, the driver's driving behavior evaluation level is recorded as unqualified, and driving feedback is provided on the vehicle's central control display screen, the driving feedback including score visualization and giving the driver relevant driving advice.

[0049] In this embodiment, the first evaluation interval represents the range of qualified driving behavior index throughout the whole process, and the second evaluation interval represents the range of unqualified driving behavior index throughout the whole process; by setting the whole evaluation interval and evaluating the whole driving behavior index, the driver can be prompted to pay more attention to the standardization of driving behavior during driving, thereby reducing the risk of traffic accidents; at the same time, the score visualization and driving suggestions in the driving feedback can intuitively display the driver's driving behavior performance and remind the driver to pay attention to improvement, thereby enhancing the driver's driving safety awareness. This method not only improves the standardization of driving behavior and enhances driving safety awareness, but also realizes personalized driving guidance and improves the driving experience.

[0050] Specifically, the full-trip evaluation interval is obtained from a preset database. In a specific embodiment, all real-time driving behavior evaluation indicators and the corresponding travel mileage are substituted into the specific restriction expression of the full-trip driving behavior index to obtain the corresponding data set, and the minimum value, maximum value and median value of the data set are obtained, and the value range from the minimum value to the median value is recorded as the second evaluation interval, and the value range from the median value to the maximum value is recorded as the first evaluation interval.

[0051] The vehicle driving behavior evaluation system based on ETC data of the Internet of Things provided by the embodiment of the present application includes: a real-time driving evaluation module, an emergency lane evaluation module, a fatigue driving judgment module and a full-course driving evaluation module; wherein the real-time driving evaluation module is used to obtain the real-time driving data of the vehicle passing through each ETC gantries after entering the highway, obtain the real-time driving behavior evaluation index according to the real-time driving data and judge whether to issue a driving warning, the real-time driving behavior evaluation index is used to quantify the degree of compliance between the real-time driving behavior of the vehicle on the highway and the standard driving behavior, and the driving warning is used to warn the driver to pay attention to restraining driving behavior; the emergency lane evaluation module is used to obtain the occupied lane by obtaining the road data and combining the real-time driving data Use the emergency lane assessment index to determine whether to conduct emergency lane occupation verification based on the emergency lane occupation assessment index. The emergency lane occupation assessment index is used to quantify the degree of compliance of the vehicle occupying the emergency lane. The emergency lane occupation verification is used to detect the actual situation of the vehicle to verify whether it occupies the emergency lane; the fatigue driving judgment module is used to obtain the vehicle's high-speed driving data based on ETC, and determine whether there is fatigue driving based on the traffic data; the full-time driving assessment module is used to obtain the full-time driving behavior index based on the real-time driving behavior assessment index and traffic data and provide driving feedback. The full-time driving behavior index is used to comprehensively quantify the degree of driving behavior standardization of the driver. The driving feedback includes score visualization and giving driving suggestions.

[0052] In this embodiment, the system uses Internet of Things technology and ETC data to achieve real-time monitoring, evaluation and feedback of driving behavior, improve the efficiency and intelligence level of traffic management, and provide data support for traffic management departments, which helps to formulate more scientific and reasonable traffic management measures.

[0053] To summarize, the embodiment of the present application obtains a real-time driving behavior evaluation index through the acquired real-time driving data and determines whether to issue a driving warning, then obtains road data and combines it with the real-time driving data to obtain an emergency lane occupation evaluation index and determines whether to perform an emergency lane occupation verification, then determines whether there is fatigue driving based on the acquired traffic data, and finally obtains a full-course driving behavior index based on the real-time driving behavior evaluation index and the traffic data and performs driving feedback, thereby more accurately evaluating the vehicle's driving behavior, and further achieving a more accurate evaluation of the vehicle's driving behavior, effectively solving the problem of inaccurate data in the prior art when evaluating high-speed vehicle driving behavior based on ETC data.

[0054] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0055] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0056] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0057] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0058] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0059] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A vehicle driving behavior evaluation method based on ETC data of the Internet of Things, characterized in that: The following steps are involved: Acquire real-time driving data of the vehicle passing through each ETC gantries after entering the highway, obtain real-time driving behavior evaluation indicators based on the real-time driving data, and determine whether to issue a driving warning. The real-time driving behavior evaluation indicators are used to quantify the degree of compliance between the real-time driving behavior of the vehicle on the highway and the standard driving behavior; Acquire road data and combine it with real-time driving data to obtain an emergency lane occupation assessment index, and determine whether to conduct emergency lane occupation verification based on the emergency lane occupation assessment index, wherein the emergency lane occupation assessment index is used to quantify the compliance degree of the vehicle occupying the emergency lane; Obtain high-speed traffic data of vehicles based on ETC, and determine whether there is fatigue driving based on the traffic data; The full-course driving behavior index is obtained based on the real-time driving behavior evaluation index and traffic data, and driving feedback is performed. The full-course driving behavior index is used to comprehensively quantify the degree of driving behavior standardization of the driver.

2. The vehicle driving behavior evaluation method based on ETC data of the Internet of Things as claimed in claim 1, characterized in that: The real-time driving data includes vehicle detection speed, longitudinal acceleration change times, lateral acceleration change times, abnormal braking times and abnormal throttle times; The road data includes the number of lane changes, the number of road channels, the number of times turn signals are used, the frequency of lane departure warnings, and the average road congestion index; The average road congestion index represents the average of the road congestion indexes of the road section between two adjacent ETC gantries; The travel data includes travel time, travel kilometres and number of stops at service areas.

3. The vehicle driving behavior evaluation method based on ETC data of the Internet of Things as claimed in claim 2, characterized in that: The specific process of obtaining the real-time driving behavior evaluation index is as follows: Numbering the ETC gantries and obtaining reference driving data from a preset database, wherein the reference driving data includes a maximum speed limit and a minimum speed limit; Processing the vehicle detection speed and the reference driving data to obtain a vehicle detection speed evaluation index, wherein the vehicle detection speed evaluation index is used to quantify the degree of standardization of the vehicle detection speed; The real-time driving behavior evaluation index is obtained by processing the sum of the longitudinal acceleration change times, the lateral acceleration change times, the abnormal braking times and the abnormal throttle times and performing an average operation on the vehicle detection speed evaluation index.

4. The vehicle driving behavior evaluation method based on ETC data of the Internet of Things as claimed in claim 3, characterized in that: The specific process of determining whether to perform a driving warning is as follows: Acquire a real-time driving evaluation threshold from a preset database, wherein the real-time driving evaluation threshold is used to determine the degree of compliance between the real-time driving behavior of the vehicle on the highway and the standard driving behavior; Compare the real-time driving behavior evaluation metric with the real-time driving evaluation threshold: If the real-time driving behavior evaluation index is not less than the real-time driving evaluation threshold, no driving warning will be issued; If the real-time driving behavior evaluation index is less than the real-time driving evaluation threshold, a driving warning is issued, and the driving warning includes a voice warning and a central control warning; The voice warning means that the driver is warned to pay attention to driving habits and informed of the hidden dangers of current driving behavior through voice broadcast; The central control warning means that the real-time driving behavior evaluation indicators are visualized and informed to the driver through the car's central control display screen.

5. The vehicle driving behavior evaluation method based on ETC data of the Internet of Things as claimed in claim 2, characterized in that: The specific process of obtaining the emergency lane occupation evaluation index is as follows: Acquire an emergency lane evaluation weight from a preset database, wherein the emergency lane evaluation weight includes a congestion weight and a lane change weight; The driving lanes are numbered, and a lane change index is obtained based on the number of road channels and the number of lane changes, wherein the lane change index is used to quantify the probability of the vehicle changing lanes to the emergency lane; The emergency lane occupation evaluation index is obtained based on the number of times the turn signal is used, the frequency of lane departure alarms, the average road congestion index, the emergency lane evaluation weight, and the lane change index.

6. The vehicle driving behavior evaluation method based on ETC data of the Internet of Things as claimed in claim 5, characterized in that: The specific process of verifying the occupation of the emergency lane is as follows: Obtaining an emergency lane occupation threshold from a preset database, wherein the emergency lane occupation threshold is used to determine the compliance degree of the vehicle occupying the emergency lane; Compare the emergency lane occupation evaluation index with the emergency lane occupation threshold: If the emergency lane occupation evaluation index is less than the emergency lane occupation threshold, the emergency lane occupation verification will not be performed; If the emergency lane occupation evaluation index is not less than the emergency lane occupation threshold, the vehicle vision system will be enabled to verify whether the vehicle is in the emergency lane. If the vehicle is in the emergency lane, the driver will be immediately reminded to exit the emergency lane. Otherwise, the emergency lane occupation verification will be exited.

7. The vehicle driving behavior evaluation method based on ETC data of the Internet of Things as claimed in claim 2, characterized in that: The specific limiting expression of the full driving behavior index is as follows: ； Where n is the number of the ETC gantry, , Indicates the total number of ETC gantries, Indicates the number of kilometers traveled. represents the real-time driving behavior evaluation index of the vehicle arriving at the nth ETC gantry, represents the whole driving behavior index, and e represents a natural constant.

8. The vehicle driving behavior evaluation method based on ETC data of the Internet of Things as claimed in claim 1, characterized in that: The specific steps of determining whether there is fatigue driving are as follows: Step 1: Obtain the travel time and the number of stops in the service area from the travel data. If the number of stops in the service area is 0, proceed to step 3; otherwise, proceed to step 2. Step 2: record the ratio of the travel time to the number of stops at the service area as the travel time and execute step 3; Step three, compare the travel time with the preset safe driving time. If the travel time is not less than the preset safe driving time, it is determined that the driver is driving fatigued, otherwise there is no fatigue driving.

9. The vehicle driving behavior evaluation method based on ETC data of the Internet of Things as claimed in claim 7, characterized in that: The specific content of the driving feedback is as follows: Acquire a full evaluation interval from a preset database, wherein the full evaluation interval includes a first evaluation interval and a second evaluation interval; Compare the full driving behavior index with the full assessment interval: If the whole driving behavior index is within the first evaluation interval, the driver's driving behavior evaluation level is recorded as qualified, and the score is visualized on the vehicle's central control display screen; If the entire driving behavior index is within the second evaluation interval, the driver's driving behavior evaluation level is recorded as unqualified, and driving feedback is provided on the vehicle's central control display screen, wherein the driving feedback includes score visualization.

10. Vehicle driving behavior evaluation system based on ETC data of the Internet of Things, including: Real-time driving assessment module, emergency lane assessment module, fatigue driving judgment module and full-course driving assessment module; The real-time driving evaluation module is used to obtain the real-time driving data of the vehicle passing through each ETC gantries after entering the highway, obtain the real-time driving behavior evaluation index based on the real-time driving data and determine whether to issue a driving warning. The real-time driving behavior evaluation index is used to quantify the degree of compliance between the real-time driving behavior of the vehicle on the highway and the standard driving behavior; The emergency lane assessment module is used to obtain road data and combine it with real-time driving data to obtain an emergency lane occupation assessment index, and determine whether to perform emergency lane occupation verification based on the emergency lane occupation assessment index. The emergency lane occupation assessment index is used to quantify the compliance degree of the vehicle occupying the emergency lane; The fatigue driving judgment module is used to obtain the high-speed driving traffic data of the vehicle based on ETC, and judge whether there is fatigue driving according to the traffic data; The full-course driving evaluation module is used to obtain a full-course driving behavior index and provide driving feedback based on real-time driving behavior evaluation indicators and traffic data. The full-course driving behavior index is used to comprehensively quantify the degree of driving behavior standardization of the driver.

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