TWI660276B - System and method for applying user profile model to score - Google Patents
System and method for applying user profile model to score Download PDFInfo
- Publication number
- TWI660276B TWI660276B TW106142601A TW106142601A TWI660276B TW I660276 B TWI660276 B TW I660276B TW 106142601 A TW106142601 A TW 106142601A TW 106142601 A TW106142601 A TW 106142601A TW I660276 B TWI660276 B TW I660276B
- Authority
- TW
- Taiwan
- Prior art keywords
- score
- factor
- driving
- environmental risk
- data
- Prior art date
Links
Landscapes
- Traffic Control Systems (AREA)
Abstract
一種應用使用者輪廓模型以進行評分的系統,包含儲存裝置及處理裝置。儲存裝置用以儲存環境風險歷史資料及使用者輪廓歷史資料。處理裝置包含用以蒐集對應環境的環境風險因子之環境資料蒐集模組、用以蒐集對應使用者的使用者輪廓因子之因子資料蒐集模組、用以根據環境風險因子與環境風險歷史資料之間的差異計算出環境風險分數之環境風險分析模組、用以根據使用者輪廓因子與使用者輪廓歷史資料之間的差異而計算出差異分數之使用者輪廓分析模組,以及用以將環境風險分數與差異分數計算出評估分數之評估分數計算模組。 A system applying a user profile model for scoring includes a storage device and a processing device. The storage device is used to store environmental risk historical data and user profile historical data. The processing device includes an environmental data collection module for collecting environmental risk factors corresponding to the environment, a factor data collection module for collecting user contour factors corresponding to the user, and a method for determining the relationship between environmental risk factors and historical environmental risk data. Environmental risk analysis module for calculating environmental risk scores, user contour analysis module for calculating difference scores based on differences between user contour factors and user contour history data, and environmental risk analysis The score and difference score are used to calculate an evaluation score calculation module.
Description
本揭示文件係關於一種系統及方法,尤指一種應用使用者輪廓模型以進行評分的系統及方法。 The present disclosure relates to a system and method, and more particularly to a system and method for applying a user profile model for scoring.
許多用來對使用者進行評估時,多是建立一通用模型,但對於經常因人而異的狀況下,通用模型往往並不適用。尤其是在個人行為上常有差異的駕駛疲勞評估上,市面上現有偵測駕駛疲勞行為之商品,多藉由影像、生理資訊來判斷,大部分是實驗階段,其實用性和普遍性仍不足;現有利用車上診斷裝置(on-board diagnostics,OBD)資料分析駕駛行為之商品,多半仍是透過行程結束後才進行異常駕駛行為的檢討,未能即時警示,導致即時性的行車風險問題仍未能有效改善。 Many of them are used to evaluate users by establishing a general model, but for situations that often vary from person to person, the general model is often not applicable. Especially in the evaluation of driving fatigue, which often differs in personal behavior, the existing products that detect driving fatigue on the market are mostly judged by images and physiological information. Most of them are experimental, and their practicality and universality are still insufficient. ; Existing products that use on-board diagnostics (OBD) data to analyze driving behaviors are still mostly reviewed for abnormal driving behaviors after the end of the trip, without real-time warnings, leading to immediate driving risks. Failure to improve effectively.
本揭示文件之一實施例揭示一種應用使用者輪廓模型以進行評分的系統,用以計算使用者於環境的評估分數,系統包含環境資料蒐集模組、因子資料蒐集模組、儲存裝 置及處理裝置。環境資料蒐集模組用以偵測環境風險因子。因子資料蒐集模組用以偵測使用者輪廓因子。儲存裝置用以儲存環境風險歷史資料及使用者輪廓歷史資料。處理裝置用以根據環境風險因子與環境風險歷史資料之間的差異計算出環境風險分數、根據使用者輪廓因子與使用者輪廓歷史資料之間的差異而計算出差異分數,以及將環境風險分數與差異分數計算出評估分數。 An embodiment of the present disclosure discloses a system for scoring a user profile model to calculate a user's assessment score on the environment. The system includes an environmental data collection module, a factor data collection module, and a storage device. And processing equipment. The environmental data collection module is used to detect environmental risk factors. The factor data collection module is used to detect user contour factors. The storage device is used to store environmental risk historical data and user profile historical data. The processing device is configured to calculate an environmental risk score according to the difference between the environmental risk factor and the historical environmental risk data, calculate a difference score according to the difference between the user contour factor and the user contour historical data, and combine the environmental risk score with the The difference score calculates an evaluation score.
本揭示文件之一實施例揭示一種應用使用者輪廓模型以進行評分的方法,其藉由處裡裝置實施,用以計算使用者於環境的評估分數,包含以下步驟,處理裝置根據環境風險因子與環境風險歷史資料之間的差異而計算出環境風險分數;處理裝置根據使用者輪廓因子與使用者輪廓歷史資料之間的差異而計算出差異分數;以及處理裝置根據環境風險分數與差異分數計算出評估分數。 An embodiment of this disclosure document discloses a method for scoring by applying a user profile model, which is implemented by a local device to calculate a user's evaluation score on the environment. The method includes the following steps. The environmental risk score is calculated based on the differences between historical environmental risk data; the processing device calculates a difference score based on the difference between the user profile factor and the user profile historical data; and the processing device calculates based on the environmental risk score and the difference score Evaluate the score.
100‧‧‧應用使用者輪廓模型以進行評分的系統 100‧‧‧ System for applying user contour model for scoring
121‧‧‧環境資料蒐集模組 121‧‧‧Environmental Data Collection Module
122‧‧‧因子資料蒐集模組 122‧‧‧factor data collection module
110‧‧‧儲存裝置 110‧‧‧Storage device
120‧‧‧處理裝置 120‧‧‧Processing device
123‧‧‧環境風險分析模組 123‧‧‧Environmental Risk Analysis Module
124‧‧‧使用者輪廓分析模組 124‧‧‧User Profile Analysis Module
125‧‧‧評估分數計算模組 125‧‧‧Evaluation score calculation module
130‧‧‧通訊電路 130‧‧‧communication circuit
OBD‧‧‧車上診斷裝置 OBD‧‧‧Onboard diagnostic device
126‧‧‧駕駛行為分數計算模組 126‧‧‧Driving Behavior Score Calculation Module
150‧‧‧警示裝置 150‧‧‧ warning device
200‧‧‧應用使用者輪廓模型以進行評分的方法 200‧‧‧ Method for applying user contour model for scoring
D10‧‧‧環境風險歷史資料 D10‧‧‧ Historical Environmental Risk Data
D20‧‧‧駕駛輪廓歷史資料 D20‧‧‧Driving contour history data
DB1‧‧‧天氣狀況資料庫 DB1‧‧‧ Weather Database
DB2‧‧‧路況資料庫 DB2‧‧‧Traffic Database
F10‧‧‧環境風險因子 F10‧‧‧Environmental risk factor
F20‧‧‧駕駛輪廓因子 F20‧‧‧Driving Contour Factor
IN‧‧‧網際網路 IN‧‧‧Internet
SC10‧‧‧環境風險分數 SC10‧‧‧Environmental Risk Score
SC20‧‧‧差異分數 SC20‧‧‧ Difference Score
SC30‧‧‧評估分數 SC30‧‧‧ evaluation score
SC40‧‧‧當前駕駛行為分數 SC40‧‧‧Current driving behavior score
S110~S130‧‧‧步驟 S110 ~ S130‧‧‧step
為讓本揭示內容之上述和其他目的、特徵、優點與實施例能更明顯易懂,所附圖式之說明如下:第1A圖為根據本揭示文件之一實施例所示之應用使用者輪廓模型以進行評分的系統的功能方塊圖。 In order to make the above and other objects, features, advantages, and embodiments of the present disclosure more comprehensible, the description of the drawings is as follows: FIG. 1A is an outline of an application user according to an embodiment of the present disclosure Functional block diagram of a model to score the system.
第1B圖為根據本揭示文件之一實施例所示之應用使用者輪廓模型以進行評分的系統透過通訊電路蒐集環境風險因子及駕駛輪廓因子的功能方塊圖。 FIG. 1B is a functional block diagram of a system for applying a user profile model for scoring according to an embodiment of the present disclosure to collect environmental risk factors and driving profile factors through a communication circuit.
第2圖為根據本揭示文件之一實施例所示之應用使用者輪廓模型以進行評分的方法的流程圖。 FIG. 2 is a flowchart of a method for applying a user profile model for scoring according to an embodiment of the present disclosure.
下文係舉實施例配合所附圖式作詳細說明,以更好地理解本案的態樣,但所提供之實施例並非用以限制本案所涵蓋的範圍,而結構操作之描述非用以限制其執行之順序,任何由元件重新組合之結構,所產生具有均等功效的裝置,皆為本案所涵蓋的範圍。 The following is a detailed description with examples and the attached drawings to better understand the aspect of the case, but the examples provided are not intended to limit the scope covered by the case, and the description of the structural operation is not used to limit it In the order of execution, any structure that reassembles the components and produces a device with equal efficacy is within the scope of this case.
請參照第1A圖、第1B圖及第2圖。第1A圖為根據本揭示文件之一實施例所示之應用使用者輪廓模型以進行評分的系統100的功能方塊圖。第1B圖為根據本揭示文件之一實施例所示之應用使用者輪廓模型以進行評分的系統100透過通訊電路130蒐集環境風險因子F10及駕駛輪廓因子F20的功能方塊圖。第2圖為根據本揭示文件之一實施例所示之應用使用者輪廓模型以進行評分的方法200的流程圖。第2圖可應用於第1A圖及第1B圖之系統100以實施之。本實施例是以應用在駕駛疲勞的評分上來作舉例說明,但並不以此為限。 Please refer to Fig. 1A, Fig. 1B, and Fig. 2. FIG. 1A is a functional block diagram of a system 100 for applying a user profile model for scoring according to an embodiment of the present disclosure. FIG. 1B is a functional block diagram of a system 100 for applying a user profile model for scoring according to an embodiment of the present disclosure to collect an environmental risk factor F10 and a driving profile factor F20 through a communication circuit 130. FIG. 2 is a flowchart of a method 200 for applying a user profile model for scoring according to an embodiment of the present disclosure. FIG. 2 is applicable to the system 100 of FIGS. 1A and 1B for implementation. In this embodiment, an example is applied to the score of driving fatigue, but it is not limited thereto.
系統100用以計算使用者於環境的評估分數SC30。於本實施例中,使用者可為駕駛、環境可為駕駛駕車的路段之環境,以及評估分數SC30係用以評估駕駛的疲勞程度。也就是說,系統100可用於計算駕駛於駕車的路段之環境的疲勞程度。 The system 100 is used to calculate a user's environmental assessment score SC30. In this embodiment, the user may be driving, the environment may be the environment of the road section where the vehicle is driven, and the evaluation score SC30 is used to evaluate the degree of fatigue of driving. That is, the system 100 can be used to calculate the degree of fatigue of the environment driving on the road section of the car.
應用使用者輪廓模型以進行評分的系統100包含儲存裝置110及處理裝置120,其中處理裝置120包含環境資料蒐集模組121、因子資料蒐集模組122、環境風險分析模組123、使用者輪廓分析模組124以及評估分數計算模組125。於第1B圖的實施例中,系統100更包含通訊電路130。 A system 100 for applying a user profile model for scoring includes a storage device 110 and a processing device 120, wherein the processing device 120 includes an environmental data collection module 121, a factor data collection module 122, an environmental risk analysis module 123, and a user profile analysis Module 124 and evaluation score calculation module 125. In the embodiment of FIG. 1B, the system 100 further includes a communication circuit 130.
於一實施例中,儲存裝置110可為硬式磁碟機(hard disk drive,HDD)、固態硬碟(solid state disk,SSD)或容錯式磁碟陣列(redundant array of independent disks,RAID),然儲存裝置110並不以此為限,凡是可作為儲存資料的裝置皆屬於本創作範疇。 In one embodiment, the storage device 110 may be a hard disk drive (HDD), a solid state disk (SSD), or a redundant array of independent disks (RAID). The storage device 110 is not limited to this, and any device that can be used to store data belongs to this creative category.
於一實施例中,處理裝置120可由積體電路如微控制單元(micro controller)、微處理器(microprocessor)、數位訊號處理器(digital signal processor)、特殊應用積體電路(application specific integrated circuit,ASIC)或邏輯電路來分別實施,也可經由市售處理器、電腦、伺服器或具有計算能力的電子裝置來予以執行。 In one embodiment, the processing device 120 may include integrated circuits such as a micro controller, a microprocessor, a digital signal processor, and an application specific integrated circuit. ASIC) or logic circuits, respectively, or they can be implemented by commercially available processors, computers, servers, or electronic devices with computing capabilities.
環境資料蒐集模組121用以對應環境偵測環境風險因子F10。具體來說,環境資料蒐集模組121可以透過通訊電路130連接至網際網路IN以通訊連接至天氣狀況資料庫DB1(例如氣象局)及路況資料庫DB2(例如公路局),並且透過通訊電路130通訊連接車上診斷裝置OBD,並至天氣狀況資料庫DB1、路況資料庫DB2及車上診斷裝置OBD擷取對應駕駛位置的環境風險因子F10。環境風險因子F10可包含天氣狀況 因子、路況因子及車況因子,其中天氣狀況因子可為雨量資料、風速資料及紫外線指數資料等資料;路況因子可為現在位置資料、現在車速資料及特殊事件資料等資料,其中特殊事件例如道路施工事件或車禍事件;車況因子可為車齡資料、日期資料、車型資料及胎壓資料等資料。 The environmental data collection module 121 is used to correspond to the environmental detection environmental risk factor F10. Specifically, the environmental data collection module 121 can be connected to the Internet IN through the communication circuit 130 to communicate with the weather condition database DB1 (such as the Meteorological Bureau) and the road condition database DB2 (such as the Highway Bureau), and through the communication circuit 130 communicates with the on-board diagnostic device OBD, and retrieves the environmental risk factor F10 corresponding to the driving position to the weather condition database DB1, the road condition database DB2, and the on-board diagnostic device OBD. Environmental risk factor F10 may include weather conditions Factors, road condition factors, and vehicle condition factors, where weather condition factors can be rainfall data, wind speed data, and UV index data; road condition factors can be current location data, current vehicle speed data, and special event data, among which special events such as road construction Incident or car accident; vehicle condition factors can be data such as vehicle age data, date data, model data, and tire pressure data.
換句話說,環境資料蒐集模組121可藉由定位裝置的定位資料而獲得駕駛的位置,並根據駕駛的位置連線至外部的天氣狀況資料庫DB1、路況資料庫DB2及車上診斷裝置OBD擷取對應駕駛位置的風險因子。 In other words, the environmental data collection module 121 can obtain the driving position through the positioning data of the positioning device, and connect to the external weather condition database DB1, the road condition database DB2, and the on-board diagnostic device OBD according to the driving position. Retrieve the risk factor corresponding to the driving position.
因子資料蒐集模組122用以對應駕駛偵測駕駛輪廓因子F20。具體來說,因子資料蒐集模組122可連接儲存裝置110,且可以透過通訊電路130通訊連接至車上診斷裝置OBD,並至車上診斷裝置OBD擷取對應駕駛行為的駕駛輪廓因子F20。駕駛輪廓因子F20可包含身體狀況因子、行駛現況因子及行車紀錄因子,身體狀況因子可為性別資料、年齡資料及身體狀況資料等資料。行駛現況因子可為急加速/減速變化量資料、急加速/減速頻率資料、急轉彎資料、換檔資料、怠速時間資料及怠速頻率資料等資料,其中換檔資料包含引擎轉速、車速、正確檔位及離合器及油門的間隔時間。行車紀錄因子可為行車密集程度資料、行車時數資料及現在行車模式資料等資料。 The factor data collection module 122 is used to correspond to the driving detection driving contour factor F20. Specifically, the factor data collection module 122 can be connected to the storage device 110, and can be communicatively connected to the on-board diagnostic device OBD through the communication circuit 130, and retrieve the driving contour factor F20 corresponding to the driving behavior to the on-board diagnostic device OBD. The driving contour factor F20 may include a physical condition factor, a driving condition factor, and a driving record factor. The physical condition factor may be data such as gender data, age data, and physical condition data. The driving status factors can be data such as rapid acceleration / deceleration change data, rapid acceleration / deceleration frequency data, sharp turning data, shift data, idle time data, and idle frequency data, among which the shift data includes engine speed, vehicle speed, and correct gear. Position and clutch and throttle interval. The driving record factor can be data such as driving intensive degree data, driving hours data, and current driving mode data.
換句話說,因子資料蒐集模組122可藉由車上診斷裝置而獲得對應駕駛行為的駕駛輪廓因子F20。 In other words, the factor data collection module 122 can obtain the driving contour factor F20 corresponding to the driving behavior through the on-board diagnostic device.
儲存裝置110用以儲存對應環境的環境風險歷史 資料D10及對應駕駛的駕駛輪廓歷史資料D20。 The storage device 110 is used to store the environmental risk history of the corresponding environment Data D10 and driving contour history data D20 corresponding to driving.
環境風險歷史資料D10可包含天氣狀況歷史資料、路況歷史資料及車況歷史資料。天氣狀況歷史資料可為雨量參考資料、風速參考資料及紫外線指數參考資料等資料;路況歷史資料可為歷史事故位置資料及平均車速資料等資料;車況歷史資料可為車齡參考資料、定檢日期資料、車型參考資料及胎壓參考資料等資料。 The environmental risk historical data D10 may include weather condition historical data, road condition historical data, and vehicle condition historical data. The historical data of weather conditions can be rainfall reference data, wind speed reference data, and UV index reference data; road condition historical data can be historical accident location data and average vehicle speed data; historical condition data can be vehicle age reference data and scheduled inspection dates. Data, vehicle reference and tire pressure reference.
駕駛輪廓歷史資料D20可包含身體狀況歷史資料、行駛現況歷史資料及行車紀錄歷史資料,其中身體狀況歷史資料可為性別參考資料、年齡參考資料及身體狀況參考資料。行駛現況歷史資料可為急加速/減速變化量參考資料、急加速/減速頻率參考資料、歷史急轉彎參考資料、換檔參考資料、怠速時間參考資料及怠速頻率參考資料等資料。行車紀錄歷史資料可為行車密集程度參考資料、行車時數參考資料及行車模式參考資料等資料。 The driving contour history data D20 may include historical data of physical conditions, historical data of driving conditions, and historical data of driving records. The historical data of physical conditions may be gender reference data, age reference data, and physical condition reference data. The driving current historical data can be rapid acceleration / deceleration change amount reference, rapid acceleration / deceleration frequency reference, historical sharp turn reference, shift reference, idle time reference and idle frequency reference. The historical data of driving records can be reference information of driving intensity, driving hours reference and driving mode reference.
應注意的是,前述環境風險因子F10、駕駛輪廓因子F20、環境風險歷史資料D10及駕駛輪廓歷史資料D20僅為示例,並不以此為限。 It should be noted that the aforementioned environmental risk factor F10, driving contour factor F20, environmental risk historical data D10, and driving contour historical data D20 are just examples, and are not limited thereto.
處理裝置120的環境風險分析模組123可按照步驟S110,根據環境風險因子F10與環境風險歷史資料D10之間的差異計算出環境風險分數SC10。處理裝置120的使用者輪廓分析模組124可按照步驟S120,根據駕駛輪廓因子F20與駕駛輪廓歷史資料D20之間的差異計算出差異分數SC20。處理裝置120的評估分數計算模組125可按照步驟S130,將環境風險 分數SC10與差異分數計算出評估分數SC30。 The environmental risk analysis module 123 of the processing device 120 can calculate the environmental risk score SC10 according to the difference between the environmental risk factor F10 and the environmental risk historical data D10 according to step S110. The user profile analysis module 124 of the processing device 120 may calculate a difference score SC20 according to the difference between the driving profile factor F20 and the driving profile history data D20 according to step S120. The evaluation score calculation module 125 of the processing device 120 may perform environmental risk assessment according to step S130. The score SC10 and the difference score are used to calculate an evaluation score SC30.
於一實施例中,處理裝置120的環境風險分析模組123可根據當前的環境風險因子F10與環境風險歷史資料D10所代表的機率分布進行KL(Kullback-Leibler)散度計算,計算出環境風險分數SC10。處理裝置120的使用者輪廓分析模組124根據當前的使用者輪廓因子F20與使用者輪廓歷史資料D20所代表的機率分布進行KL散度計算,計算出差異分數SC20。處理裝置120將乘上一第一權重的該環境風險分數SC10與乘上一第二權重的該差異分數SC20相加後計算出該評估分數SC30。 In one embodiment, the environmental risk analysis module 123 of the processing device 120 may perform a KL (Kullback-Leibler) divergence calculation based on the probability distribution represented by the current environmental risk factor F10 and historical risk data D10 to calculate the environmental risk. Score SC10. The user profile analysis module 124 of the processing device 120 performs a KL divergence calculation based on the current user profile factor F20 and the probability distribution represented by the user profile history data D20 to calculate a difference score SC20. The processing device 120 adds the environmental risk score SC10 multiplied by a first weight and the difference score SC20 multiplied by a second weight to calculate the evaluation score SC30.
KL散度計算是用來評估兩個機率分布的非對稱性,當非對稱性愈大時,KL散度的數值愈大,於本實施例當中,當環境風險因子F10與該環境風險歷史資料D10相差愈大時,得到的KL散度也相應提高,使計算得到的環境風險分數SC10相應提高。同理,當環境風險因子F10與該環境風險歷史資料D10相差愈小時,得到的KL散度也相應降低,使計算得到的環境風險分數SC10相應降低。相似地,使用者輪廓因子F20與使用者輪廓歷史資料D20及差異分數SC20的計算也有相似的變化關係。 The KL divergence calculation is used to evaluate the asymmetry of the two probability distributions. When the asymmetry is larger, the value of the KL divergence is larger. In this embodiment, when the environmental risk factor F10 and the historical environmental risk data The larger the D10 difference, the higher the KL divergence will be, and the calculated environmental risk score SC10 will be increased accordingly. Similarly, when the difference between the environmental risk factor F10 and the historical environmental risk data D10 is smaller, the obtained KL divergence is correspondingly reduced, so that the calculated environmental risk score SC10 is correspondingly reduced. Similarly, the calculation of the user contour factor F20 and the user contour history data D20 and the difference score SC20 have similar changes.
進一步地,環境風險因子F10包含天氣狀況因子、路況因子及車況因子,環境風險分析模組123可根據天氣狀況因子與天氣狀況歷史資料之間的差異計算出天氣狀況分數、根據路況因子與路況歷史資料之間的差異計算出路況分數,以及根據車況因子與車況歷史資料之間的差異計算出車況分數。環 境風險分析模組123再依據天氣狀況分數、路況分數及車況分數而計算出環境風險分數SC10。 Further, the environmental risk factor F10 includes a weather condition factor, a road condition factor, and a vehicle condition factor. The environmental risk analysis module 123 can calculate a weather condition score according to a difference between the weather condition factor and historical weather condition data, and according to the road condition factor and the road condition history. The difference between the data is used to calculate the road condition score, and the vehicle condition score is calculated based on the difference between the vehicle condition factor and the historical condition data. ring The environmental risk analysis module 123 then calculates the environmental risk score SC10 based on the weather condition score, road condition score, and vehicle condition score.
駕駛輪廓因子F20包含身體狀況因子、行駛現況因子及行車紀錄因子,使用者輪廓分析模組124可根據身體狀況因子與身體狀況歷史資料之間的差異計算出身體狀況分數、根據行駛現況因子與歷史行駛現況資料之間的差異計算出行駛現況分數,以及根據行車紀錄因子與歷史行車紀錄資料之間的差異計算出行車紀錄分數。使用者輪廓分析模組124再依據身體狀況分數、行駛現況分數及行車紀錄分數而計算出差異分數SC20。 The driving contour factor F20 includes a physical condition factor, a driving condition factor, and a driving record factor. The user contour analysis module 124 can calculate a physical condition score based on a difference between the physical condition factor and historical condition data, and according to the driving condition factor and history The difference between the driving status data is used to calculate the driving status score, and the driving record score is calculated based on the difference between the driving record factor and the historical driving record data. The user profile analysis module 124 then calculates the difference score SC20 according to the physical condition score, the driving status score, and the driving record score.
以下,將以駕駛駕車於一路段為例來說明駕駛的環境風險分數SC10的計算。為方便說明,天氣狀況因子僅以雨量資料為例,天氣狀況歷史資料僅雨量參考資料為例;路況因子僅以現在位置資料為例,路況歷史資料僅以歷史事故位置資料為例;車況因子僅以車齡資料為例,車況歷史資料僅以車齡參考資料為例。 In the following, the driving of a road section as an example will be used to explain the calculation of the driving environmental risk score SC10. For the convenience of explanation, the weather condition factor only uses the rainfall data as an example, the weather condition historical data only uses the rainfall reference data as an example; the road condition factor only uses the current position data as an example, and the road condition historical data only uses the historical accident position data as an example; Take the vehicle age data as an example, and the vehicle condition historical data is only based on the vehicle age reference data as an example.
首先,環境資料蒐集模組121可連接儲存裝置110,且可以透過通訊電路130通訊連接天氣狀況資料庫DB1、車上診斷裝置OBD(包含定位資料)及儲存裝置110,並分別至天氣狀況資料庫DB1擷取此路段的雨量資料,以及至儲存裝置110擷取雨量參考資料,其中雨量參考資料可為關於雨量與天氣狀況分數的資料,雨量參考資料界定了以1小時40毫米的雨量作為一個基準線,並將對應1小時40毫米的雨量之天氣狀況分數界定為60分。 First, the environmental data collection module 121 can be connected to the storage device 110, and can be connected to the weather condition database DB1, the on-board diagnostic device OBD (including positioning data) and the storage device 110 through the communication circuit 130, and respectively to the weather condition database DB1 retrieves the rainfall data of this section, and retrieves the rainfall reference data to the storage device 110. The rainfall reference data can be the data about the rainfall and weather conditions scores. The rainfall reference data defines the rainfall of 1 hour and 40 mm as a benchmark. Line, and the weather condition score corresponding to rainfall of 1 hour and 40 mm is defined as 60 points.
於此例中,假設此路段的雨量資料中之雨量為1小時50毫米,環境風險分析模組123可將雨量資料中之1小時50毫米的雨量與1小時40毫米的雨量之間的差異進行比較,例如藉由KL散度計算,由於此路段的雨量資料中之雨量較基準線大,且相差1小時10毫米,基於雨量大的路段分數愈高,因而計算出天氣狀況分數為65分,因為相較於雨量小的路段危險程度較高。 In this example, assuming that the rainfall in this section of rainfall data is 1 hour and 50 mm, the environmental risk analysis module 123 can perform the difference between the rainfall of 1 hour and 50 mm and the rainfall of 1 hour and 40 mm in the rainfall data. For comparison, for example, by calculating the KL divergence, the rainfall data in this section is greater than the baseline, and the difference is 1 hour and 10 millimeters. Based on the higher rainfall, the score is higher, so the weather condition score is 65 points. This is because the danger level is higher than that of a section with less rainfall.
環境資料蒐集模組121通訊連接至定位裝置、路況資料庫及儲存裝置110,並分別至路況資料庫擷取現在位置資料,以及至儲存裝置110擷取歷史事故位置資料,其中歷史事故位置資料可為關於歷史事故位置與路況分數的資料,歷史事故位置資料界定了以歷史事故位置為圓心且以離此圓心距離五公里作為半徑畫一個圓,並將對應五公里的距離之路況分數界定為60分。 The environmental data collection module 121 is communicatively connected to the positioning device, the road condition database, and the storage device 110, and respectively retrieves the current position data from the road condition database, and the historical accident position data from the storage device 110. The historical accident position data may be For the historical accident location and road condition score data, the historical accident location data defines a circle with the historical accident location as the center and a distance of five kilometers from the center as a radius, and defines a road condition score corresponding to a distance of five kilometers as 60 Minute.
於此例中,環境風險分析模組123可將現在位置資料與歷史事故位置資料之間的差異進行比較,例如藉由KL散度計算,假設駕駛的現在位置與歷史事故位置相距一公里,由於駕駛的現在位置已經在歷史事故位置的範圍內,基於距離歷史事故位置愈近分數愈高,因而可計算出路況分數為70分,因為距離歷史事故位置愈近表示有愈高的機率發生事故。 In this example, the environmental risk analysis module 123 can compare the difference between the current position data and the historical accident position data, for example, by calculating the KL divergence, assuming that the current position of the driver is one kilometer away from the historical accident position. The current position of the driver is already within the range of the historical accident position. Based on the closer the historical accident position, the higher the score, the road condition score can be calculated as 70 points, because the closer to the historical accident position, the higher the probability of an accident.
環境資料蒐集模組121通訊連接至車況資料庫及儲存裝置110,並分別至車況資料庫擷取車齡資料,以及至儲存裝置110擷取車齡參考資料,其中車齡參考資料可為關於車齡與車況分數的資料,車齡參考資料界定了以十年的車齡作為 一個基準線,並將對應十年的車齡之車況分數界定為60分。 The environmental data collection module 121 is communicatively connected to the vehicle condition database and the storage device 110, and respectively retrieves the vehicle age data from the vehicle condition database and the storage device 110 to retrieve the vehicle age reference data, where the vehicle age reference data can be about the vehicle Age and vehicle condition score data, the vehicle age reference material defines a ten-year vehicle age as A baseline, and the vehicle condition score corresponding to ten years of age is defined as 60 points.
於此例中,假設駕駛所駕駛的車之車齡為二十年,環境風險分析模組123可將車齡資料中之二十年的車齡與車齡參考資料所界定之十年的車齡進行比較,例如藉由KL散度計算,由於車齡已超過基準線且相差十年,基於車齡與基準線相差愈多則分數愈高,因而計算出車況分數為80分,因為車齡愈大表示有愈高的機率發生事故。 In this example, assuming the age of the car being driven is 20 years, the environmental risk analysis module 123 can compare the 20-year car age in the car age data with the ten-year car defined by the car age reference data. Age comparison, for example, calculated by the KL divergence, because the vehicle age has exceeded the baseline and differed by ten years, the more the vehicle age is different from the baseline, the higher the score, so the vehicle condition score is calculated as 80 points because the vehicle age A larger number indicates a higher probability of an accident.
在環境風險分析模組123計算出天氣狀況分數、路況分數及車況分數後,環境風險分析模組123再將天氣狀況分數、路況分數及車況分數各自乘上權重後加總,即可求得環境風險分數SC10。 After the environmental risk analysis module 123 calculates the weather condition score, road condition score, and vehicle condition score, the environmental risk analysis module 123 multiplies the weather condition score, road condition score, and vehicle condition score by weights and then adds up to obtain the environment. Risk score SC10.
舉例來說,可分別將對應天氣狀況分數、路況分數及車況分數的權重設定為0.25、0.5及0.25,因此可得到下列公式:環境風險分數SC10=0.25*(天氣狀況分數)+0.5*(路況分數)+0.25*(車況分數)。進一步地,將天氣狀況分數、路況分數及車況分數分別以60分、65分及70分代入上述公式,因此環境風險分析模組123可計算出環境風險分數SC10為65分。 For example, the weights corresponding to the weather condition score, road condition score, and vehicle condition score can be set to 0.25, 0.5, and 0.25 respectively, so the following formula can be obtained: Environmental risk score SC10 = 0.25 * (weather condition score) + 0.5 * (road condition Score) + 0.25 * (vehicle condition score). Further, the weather condition score, road condition score, and vehicle condition score are substituted into the above formula by 60 points, 65 points, and 70 points, respectively. Therefore, the environmental risk analysis module 123 can calculate the environmental risk score SC10 to be 65 points.
接著,再以駕駛駕車於此路段為例來說明駕駛的差異分數的計算。為方便說明,身體狀況因子以性別資料和年齡資料為例,身體狀況歷史資料以性別參考資料和年齡參考資料為例;行駛現況因子僅以急加速/減速變化量資料為例,行駛現況歷史資料僅以急加速/減速變化量參考資料為例,首先,因子資料蒐集模組122通訊連接至車上診斷裝置及儲存裝置,並分別至車上診斷裝置擷取駕駛的性別資料和年齡資料, 以及至儲存裝置擷取性別參考資料和年齡參考資料,其中性別參考資料和年齡參考資料可為關於性別和年齡與身體狀況分數的資料,性別參考資料界定了男性與女性,年齡參考資料界定了年齡區間值,並將對應性別和年齡之駕駛風險行為機率透過參考資料做為界定依據。 Next, take driving on this road section as an example to illustrate the calculation of the driving difference score. For the convenience of explanation, the physical condition factor uses gender data and age data as examples, and the physical condition historical data uses gender reference data and age reference data as examples; the driving status factor only uses rapid acceleration / deceleration change amount data as an example, driving status historical data Taking only rapid acceleration / deceleration change amount reference data as an example, first, the factor data collection module 122 is communicatively connected to the on-board diagnostic device and the storage device, and separately retrieves the driving gender data and age data from the on-board diagnostic device. And retrieve the gender reference and the age reference from the storage device, wherein the gender reference and the age reference can be data on the gender and the age and physical condition score, the gender reference defines the male and female, and the age reference defines the age Interval values, and the probability of driving risk behavior corresponding to gender and age is defined based on reference data.
於此例中,使用者輪廓分析模組124可將性別和年齡資料與性別和年齡參考資料之間的差異進行比較,例如藉由KL散度計算,假設駕駛的性別為男性且年齡為31-40歲,基於過去參考資料可得相同條件下之駕駛風險發生分數,而對應此駕駛風險發生分數的身體狀況分數為50分,並且此一分數愈低表示駕駛風險發生機率愈低。 In this example, the user profile analysis module 124 can compare the differences between the gender and age data and the gender and age reference data. For example, by calculating the KL divergence, it is assumed that the driving gender is male and the age is 31- At the age of 40, based on past references, a driving risk occurrence score can be obtained under the same conditions, and the physical condition score corresponding to this driving risk occurrence score is 50 points, and the lower the score, the lower the risk of driving risk.
於另一實施例中,身體狀況分數亦會依據特殊身體狀況(例如感冒或發燒)而被調整,其中分數之調整方式,例如可為感冒或發燒增加5分。舉例來說,若身體狀況分數為50分,當駕駛感冒或發燒時,身體狀況分數將增加5分,因此經調整後的身體狀況分數為55分。應注意的是,調整的分數之計算方式僅為示例,然並不以此為限。 In another embodiment, the physical condition score is also adjusted according to a special physical condition (such as a cold or fever). The adjustment method of the score, for example, may increase 5 points for a cold or fever. For example, if the physical condition score is 50 points, when driving a cold or fever, the physical condition score will increase by 5 points, so the adjusted physical condition score is 55 points. It should be noted that the calculation method of the adjusted score is only an example, but it is not limited thereto.
因子資料蒐集模組122通訊連接至定位裝置、車上診斷裝置及儲存裝置,並分別至車上診斷裝置擷取駕駛所駕駛的車之急加速/減速變化量資料,以及至儲存裝置擷取急加速/減速變化量參考資料,其中急加速/減速變化量參考資料可為關於急加速/減速變化量與行駛現況分數的資料,急加速/減速變化量參考資料界定了以20(kph/s)的加速/減速變化量作為一個基準線,並將對應20(kph/s)的加速/減速變化量之行駛 現況分數界定為60分。 The factor data collection module 122 is communicatively connected to the positioning device, the on-board diagnostic device, and the storage device, and separately acquires the rapid acceleration / deceleration change data of the car driven by the on-board diagnostic device, and retrieves the emergency data from the storage device. Acceleration / deceleration change amount reference material, among which the rapid acceleration / deceleration change amount reference material can be information about the rapid acceleration / deceleration change amount and the driving status score, and the rapid acceleration / deceleration change amount reference material defines 20 (kph / s) The acceleration / deceleration change amount is used as a reference line, and driving corresponding to the acceleration / deceleration change amount of 20 (kph / s) The status score is defined as 60 points.
於此例中,假設駕駛所駕駛的車之加速/減速變化量為30(kph/s),使用者輪廓分析模組124可將急加速/減速變化量與急加速/減速變化量參考資料之間的差異進行比較,例如藉由KL散度計算,由於急加速/減速變化量較基準線大,且相差10(kph/s),基於同一路段的急加速/減速變化量愈大分數愈高,因而計算出行駛現況分數為70分,因為代表駕駛行駛於此路段時可能因為疲勞而出現了不尋常的加速/減速變化量。 In this example, assuming that the acceleration / deceleration change of the car being driven is 30 (kph / s), the user profile analysis module 124 can compare the rapid acceleration / deceleration change with the rapid acceleration / deceleration change reference data. Compare the differences between them. For example, calculated by the KL divergence, because the rapid acceleration / deceleration change is larger than the baseline, and the difference is 10 (kph / s), the larger the rapid acceleration / deceleration change based on the same section, the higher the score. Therefore, the driving current score is calculated to be 70 points, because the representative may experience unusual acceleration / deceleration changes due to fatigue when driving on this road section.
於另一實施例中,行駛現況分數的計算方式亦可在同時考慮急加速/減速頻率變化資料、急轉彎資料及換檔資料的情況下而被計算出來。舉例來說,當急加速/減速變化量>12(kph/s)時則為1分,其他則為0.2分;當急轉彎為一秒內有超過10°的方位角改變時則為1分;當換檔資料與換檔歷史資料進行比較(可稱為換檔準確度)時,例如比較引擎轉速、車速、正確檔位、離合器與油門的間隔時間,當有任何一個項目偏離正常範圍,則此項分數為1。因此,若在某一個時段所發生的急加速/減速變化量>12(kph/s)之次數、急轉彎之次數及換檔偏離正常範圍之次數太多時,例如急加速/減速變化量>12(kph/s)之次數為五次則增加的分數為5分,急轉彎之次數為三次則增加的分數為3分,以及換檔偏離正常範圍之次數為二次則增加的分數為2,。則行駛現況分數亦會相應提高,因為代表駕駛行駛於此路段時可能因為疲勞而出現了不尋常的加速/減速變化量、急轉彎之次數及換檔。應注意的是,上述增加的分數可以直接相加、乘上權重後再相加或是以其他方式計算。 In another embodiment, the calculation method of the running status score can also be calculated by considering the rapid acceleration / deceleration frequency change data, sharp turn data, and shift data at the same time. For example, when the acceleration / deceleration change is more than 12 (kph / s), it is 1 point, and the others are 0.2 points; when the sharp turn is more than 10 ° within one second, the azimuth angle is changed to 1 point. ; When the shift data is compared with the shift history data (which can be referred to as shift accuracy), such as comparing engine speed, vehicle speed, correct gear, clutch and throttle interval time, when any item deviates from the normal range, The score for this item is 1. Therefore, if the amount of rapid acceleration / deceleration change> 12 (kph / s), the number of sharp turns, and the number of shifts outside the normal range occur too much in a certain period, such as the amount of rapid acceleration / deceleration change> The number of 12 (kph / s) five times increases the score by 5 points, the number of sharp turns three times increases the score by 3 points, and the number of shifts out of the normal range twice increases the score by 2 . The driving status score will also increase accordingly, because the representative may experience unusual acceleration / deceleration changes, the number of sharp turns, and gear shifts due to fatigue when driving on this road. It should be noted that the above-mentioned increased scores can be directly added, multiplied by weights and then added or calculated in other ways.
因子資料蒐集模組122通訊連接至車上診斷裝置及儲存裝置,並分別至車上診斷裝置擷取駕駛所駕駛的車之行車密集程度資料,以及至儲存裝置擷取行車密集程度參考資料,其中行車密集程度參考資料可為關於行車密集程度與行車紀錄分數的資料,行車密集程度參考資料界定了一週五次的行車趟數作為一個基準線,並將對應一週五次的行車趟數之行車紀錄分數界定為65分。 The factor data collection module 122 is communicatively connected to the on-board diagnostic device and the storage device, and respectively retrieves the driving intensity data of the driving vehicle from the on-board diagnostic device, and retrieves the driving density reference data to the storage device, among which The driving intensity reference can be information about driving intensity and driving record scores. The driving intensity reference defines the number of trips on a Friday as a baseline, and the corresponding number of trips on a Friday The driving record score is defined as 65 points.
於此例中,假設駕駛的行車趟數為一週七次,使用者輪廓分析模組124可將行車密集程度與行車密集程度參考資料之間的差異進行比較,例如藉由KL散度計算,由於此次行車趟數較基準線大,且相差一週二次,基於行車趟數頻率愈高分數愈高,因而計算出行車紀錄分數為80分,因為駕駛的行車趟數頻率高表示駕駛的休息時間被壓縮,而有疲勞駕駛的疑慮,增加發生事故的風險。 In this example, assuming that the number of driving trips is seven times a week, the user profile analysis module 124 can compare the difference between the driving intensity and the driving intensity reference, for example, by calculating the KL divergence. The number of trips this time is larger than the baseline, and the difference is twice a week. Based on the higher the number of trips, the higher the score, the higher the record score is 80 points, because the higher the number of trips driving indicates the rest time of driving. Being compressed while having doubts about fatigue driving increases the risk of accidents.
於另一實施例中,行車紀錄分數亦會依據駕駛的持續行車時數而被調整,其中分數之調整方式,例如可為四小時後每一小時等比例增加0.1分。舉例來說,若行車紀錄分數為80分,當駕駛的平均持續行車時數為六小時時,行車姬路分數將增加0.2分,因此經調整後的行車紀錄分數為80.2分。應注意的是,調整的分數之計算方式僅為示例,然並不以此為限。 In another embodiment, the driving record score will also be adjusted according to the continuous driving hours of the driving. The adjustment method of the score can be, for example, an increase of 0.1 points every four hours. For example, if the driving record score is 80 points, when the average continuous driving time is six hours, the driving Himeji score will increase by 0.2 points, so the adjusted driving record score is 80.2 points. It should be noted that the calculation method of the adjusted score is only an example, but it is not limited thereto.
在使用者輪廓分析模組124計算出身體狀況分數、行駛現況分數及行車紀錄分數後,使用者輪廓分析模組124再將身體狀況分數、行駛現況分數及行車紀錄分數各自乘上權重後加總,即可求得差異分數。 After the user profile analysis module 124 calculates the physical condition score, driving status score, and driving record score, the user profile analysis module 124 multiplies the physical condition score, driving status score, and driving record score by weights and adds up To get the difference score.
舉例來說,可分別將對應身體狀況分數、行駛現況分數及行車紀錄分數的權重設定為0.2、0.7及0.1,因此可得到下列公式:差異分數=0.2*(身體狀況分數)+0.7*(行駛現況分數)+0.1*(行車紀錄分數)。進一步地,將身體狀況分數、行駛現況分數及行車紀錄分數分別以50分、70分及80分代入上述公式,因此使用者輪廓分析模組124可計算出差異分數為67分。 For example, the weights corresponding to the physical condition score, driving condition score, and driving record score can be set to 0.2, 0.7, and 0.1 respectively, so the following formula can be obtained: difference score = 0.2 * (physical condition score) + 0.7 * (driving Status score) + 0.1 * (driving record score). Further, the physical condition score, driving status score, and driving record score are substituted into the above formula by 50 points, 70 points, and 80 points, respectively, so the user profile analysis module 124 can calculate a difference score of 67 points.
評估分數計算模組125用以將環境風險分數SC10與差異分數計算出評估分數SC30。 The evaluation score calculation module 125 is configured to calculate an evaluation score SC30 from the environmental risk score SC10 and the difference score.
詳言之,在環境風險分數SC10及差別分數SC20分別被環境風險分析模組123及使用者輪廓分析模組124計算出來之後,評估分數計算模組125進一步將環境風險分數SC10及差別分數SC20各自乘上第一權重及第二權重後加總,即可求得評估分數SC30。 In detail, after the environmental risk score SC10 and the difference score SC20 are calculated by the environmental risk analysis module 123 and the user profile analysis module 124, respectively, the evaluation score calculation module 125 further separates the environmental risk score SC10 and the difference score SC20, respectively. Multiply the first weight and the second weight and add up to get the evaluation score SC30.
舉例來說,可分別將對應環境風險分數SC10及差別分數SC20的第一權重及第二權重分別設定為0.3及0.7,因此可得到下列公式:評估分數SC30=0.3*(環境風險分數SC10)+0.7*(差別分數SC20)。接著,將環境風險分數SC10及差別分數SC20分別以65分及67分代入上述公式,因此評估分數SC30為66.4分。 For example, the first and second weights corresponding to the environmental risk score SC10 and the difference score SC20 can be set to 0.3 and 0.7, respectively, so the following formula can be obtained: Evaluation score SC30 = 0.3 * (Environmental risk score SC10) + 0.7 * (different score SC20). Next, the environmental risk score SC10 and the difference score SC20 were respectively substituted into the above formula with 65 points and 67 points, so the evaluation score SC30 was 66.4 points.
此外,評估分數計算模組125更用以將所計算出來的評估分數SC30根據疲勞程度分類資料進行分類,藉以判斷對應評估分數SC30的疲勞程度。 In addition, the evaluation score calculation module 125 is further configured to classify the calculated evaluation score SC30 according to the fatigue level classification data, so as to determine the fatigue level corresponding to the evaluation score SC30.
疲勞程度分類資料可為關於評估分數SC30與疲勞程度的資料,疲勞程度分類資料界定了四個等級的疲勞程度,分別是非疲勞程度、輕度疲勞程度、中度疲勞程度及重度疲勞程度, 對應非疲勞程度的評估分數SC30為70分以下,對應輕度疲勞程度的評估分數SC30為70分到85分之間,對應中度疲勞程度的評估分數SC30為86分到97分之間,對應重度疲勞程度的評估分數SC30為98分到100分之間。 Fatigue degree classification data can be data about the assessment score SC30 and fatigue degree. The fatigue degree classification data defines four levels of fatigue degree, namely non-fatigue degree, mild fatigue degree, moderate fatigue degree, and severe fatigue degree. The assessment score SC30 for non-fatigue is below 70, the assessment score SC30 for mild fatigue is between 70 and 85, and the assessment score SC30 for moderate fatigue is between 86 and 97, corresponding The severe fatigue assessment score SC30 is between 98 and 100.
因此,處理裝置120的評估分數計算模組125可根據疲勞程度分類資料而將66.4分的評估分數SC30分類成非疲勞程度。於其他例子中,若評估分數SC30為75分,則評估分數計算模組125可根據疲勞程度分類資料而將75分的評估分數SC30分類成輕度疲勞程度;若評估分數SC30為90分,則評估分數計算模組125可根據疲勞程度分類資料而將90分的評估分數SC30分類成中度疲勞程度;若評估為99分,則評估分數計算模組125可根據疲勞程度分類資料而將99分的評估分數SC30分類成重度疲勞程度。 Therefore, the evaluation score calculation module 125 of the processing device 120 can classify the evaluation score SC30 of 66.4 points into a non-fatigue degree according to the fatigue degree classification data. In other examples, if the evaluation score SC30 is 75 points, the evaluation score calculation module 125 can classify the 75-point evaluation score SC30 into a mild fatigue level based on the fatigue classification data; if the evaluation score SC30 is 90 points, then The evaluation score calculation module 125 can classify the 90-point evaluation score SC30 into moderate fatigue according to the fatigue level classification data; if the evaluation is 99 points, the evaluation score calculation module 125 can classify 99 points based on the fatigue level classification data The SC30 score is classified as severe fatigue.
應注意的是,前述的各種分數的界定及計算方式僅為示例,並不以此為限。 It should be noted that the foregoing definitions and calculation methods of various scores are merely examples, and are not limited thereto.
於一實施例中,系統100更包含警示裝置150,用以判斷當評估分數SC30大於閾值時,發出警示。舉例來說,假設閾值係以非疲勞程度的評估分數SC30作為基準線,也就是說,將閾值設定為70分,若評估分數SC30大於70分,則表示駕駛的疲勞程度屬於輕疲勞程度,此時警示裝置150將發出警示以通知駕駛,藉以避免發生交通事故。 In one embodiment, the system 100 further includes a warning device 150 for determining that when the evaluation score SC30 is greater than a threshold value, a warning is issued. For example, suppose the threshold is based on the non-fatigue evaluation score SC30 as the baseline, that is, the threshold is set to 70 points. If the evaluation score SC30 is greater than 70 points, it indicates that the degree of fatigue of driving is a light fatigue. The warning device 150 will issue a warning to inform the driver to avoid a traffic accident.
藉此,系統100即可依據評估分數即時判斷駕駛的駕車狀況,以給予對應的指示。舉例來說,若評估分數SC10屬於非疲勞程度,不給予警示;若評估分數SC10屬於重度疲勞程度,則 發出警示。 Thereby, the system 100 can judge the driving condition of the driver in real time according to the evaluation score to give corresponding instructions. For example, if the assessment score SC10 is a non-fatigue degree, no warning is given; if the assessment score SC10 is a severe fatigue degree, then Issue a warning.
於一實施例中,系統100的處理裝置120更包含駕駛行為分數計算模組126,用以根據評估分數SC30以及歷史駕駛行為分數計算當前駕駛行為分數SC40,其中歷史駕駛行為分數指的是駕駛過去每一趟駕車的評估分數SC30之統計分數。在當前駕駛行為分數SC40被計算出來之後,當前駕駛行為分數SC40將被紀錄,並取代舊的歷史駕駛行為分數,而成為新的歷史駕駛行為分數。 In an embodiment, the processing device 120 of the system 100 further includes a driving behavior score calculation module 126 for calculating the current driving behavior score SC40 according to the evaluation score SC30 and the historical driving behavior score, wherein the historical driving behavior score refers to driving past SC30 is a statistical score of the evaluation score of each trip. After the current driving behavior score SC40 is calculated, the current driving behavior score SC40 will be recorded and replace the old historical driving behavior score to become the new historical driving behavior score.
此外,統計分數的方式將對應新的評估分數SC30來決定。舉例來說,若此趟的評估分數SC30為70分以下(非疲勞程度),駕駛行為分數計算模組126將此趟的評估分數SC30與歷史駕駛行為分數之平均,以計算出當前駕駛行為分數SC40,例如此趟的評估分數SC30為68分,歷史駕駛行為分數為70分,因此當前駕駛行為分數SC40為兩個分數的平均,即69分;若此趟的評估分數SC30為70分到97分之間(輕度疲勞程度或中度疲勞程度),駕駛行為分數計算模組126將此趟的評估分數SC30與歷史駕駛行為分數之平均後,再加上5分,以計算出當前駕駛行為分數SC40,例如此趟的評估分數SC30為90分,歷史駕駛行為分數為69分,因此當前駕駛行為分數SC40為兩個分數的平均後再加上5分,即79.5分;若此趟的評估分數SC30為98分到100分之間,駕駛行為分數計算模組126將此趟的評估分數SC30與歷史駕駛行為分數之平均後,再加上10分,例如此趟的評估分數SC30為99分,歷史駕駛行為分數為79.5分,因此當前駕駛行為分數SC40為兩個分數的平均後再加上10分,即99.25 分。 In addition, the method of counting the scores will be determined corresponding to the new assessment score SC30. For example, if the evaluation score SC30 of this trip is less than 70 (non-fatigue), the driving behavior score calculation module 126 averages the evaluation score SC30 of this trip and the historical driving behavior score to calculate the current driving behavior score. SC40, for example, the evaluation score SC30 of this trip is 68 points, and the historical driving behavior score is 70 points, so the current driving behavior score SC40 is the average of two points, that is, 69 points; if the evaluation score SC30 of this trip is 70 to 97 Between points (mild fatigue or moderate fatigue), the driving behavior score calculation module 126 averages the evaluation score SC30 of this trip with the historical driving behavior score, and then adds 5 points to calculate the current driving behavior. Score SC40, for example, the evaluation score SC30 of this trip is 90 points, and the historical driving behavior score is 69 points, so the current driving behavior score SC40 is the average of the two scores plus 5 points, which is 79.5 points; if the evaluation of this trip The score SC30 is between 98 and 100. The driving behavior score calculation module 126 adds the average of the evaluation score SC30 and the historical driving behavior score to 10 points. For example, the evaluation score SC30 of this trip is 99 points, the historical driving behavior score is 79.5, so the current driving behavior score SC40 is the average of the two scores plus 10 points, which is 99.25 Minute.
藉此,系統100即可依據當前駕駛行為分數SC40來判斷駕駛的駕車狀況,以給予對應的指示。舉例來說,若當前駕駛行為分數SC40屬於非疲勞程度,則給予駕駛可繼續駕駛的指示;若當前駕駛行為分數SC40屬於重度疲勞程度,則給予駕駛不可繼續駕駛的指示。 In this way, the system 100 can judge the driving situation of the driver according to the current driving behavior score SC40 to give a corresponding instruction. For example, if the current driving behavior score SC40 belongs to a non-fatigue level, an instruction is given to the driver to continue driving; if the current driving behavior score SC40 belongs to a severe fatigue level, the driving is instructed not to continue driving.
進一步地,駕駛行為分數計算模組126更用以建立駕駛行為模型,以根據駕駛行為模型計算當前駕駛行為分數SC40,其中,駕駛行為模型根據類神經網路、機器學習、深度學習或矩陣分解而建立。 Further, the driving behavior score calculation module 126 is further configured to establish a driving behavior model to calculate the current driving behavior score SC40 according to the driving behavior model. The driving behavior model is based on a neural network, machine learning, deep learning, or matrix decomposition. set up.
此外,評估分數計算模組125所計算出來的評估分數SC30更可被儲存至儲存裝置110,以供參考。 In addition, the evaluation score SC30 calculated by the evaluation score calculation module 125 can be further stored in the storage device 110 for reference.
綜上所述,本揭示文件之應用使用者輪廓模型以進行評分的系統及方法藉由環境資料蒐集模組、因子資料蒐集模組、儲存裝置、環境風險分析模組、使用者輪廓分析模組及評估分數計算模組,以根據駕駛長久以來的駕駛行為判斷駕駛的駕車狀況,且亦可以即時判斷駕駛的駕車裝況,並給予對應的指示。 In summary, the system and method for applying a user profile model for scoring in this disclosure document includes an environmental data collection module, a factor data collection module, a storage device, an environmental risk analysis module, and a user profile analysis module. And evaluation score calculation module to judge the driving condition of the driver based on long-term driving behavior, and also to judge the driving condition of the driver in real time and give corresponding instructions.
雖然本案已以實施例揭露如上,然其並非用以限定本案,任何所屬技術領域中具有通常知識者,在不脫離本案之精神和範圍內,當可作些許之更動與潤飾,故本案之保護範圍當視後附之申請專利範圍所界定者為準。 Although this case has been disclosed as above with examples, it is not intended to limit this case. Any person with ordinary knowledge in the technical field can make some modifications and retouching without departing from the spirit and scope of this case. Therefore, the protection of this case The scope shall be determined by the scope of the attached patent application.
Claims (16)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
TW106142601A TWI660276B (en) | 2017-12-05 | 2017-12-05 | System and method for applying user profile model to score |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
TW106142601A TWI660276B (en) | 2017-12-05 | 2017-12-05 | System and method for applying user profile model to score |
Publications (2)
Publication Number | Publication Date |
---|---|
TWI660276B true TWI660276B (en) | 2019-05-21 |
TW201926075A TW201926075A (en) | 2019-07-01 |
Family
ID=67348000
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
TW106142601A TWI660276B (en) | 2017-12-05 | 2017-12-05 | System and method for applying user profile model to score |
Country Status (1)
Country | Link |
---|---|
TW (1) | TWI660276B (en) |
Families Citing this family (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
TWI727485B (en) * | 2019-11-05 | 2021-05-11 | 關貿網路股份有限公司 | Smart scheduling analysis system with instantly collecting driving behavior data and physiological information |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US7659827B2 (en) * | 2006-05-08 | 2010-02-09 | Drivecam, Inc. | System and method for taking risk out of driving |
US8508353B2 (en) * | 2009-01-26 | 2013-08-13 | Drivecam, Inc. | Driver risk assessment system and method having calibrating automatic event scoring |
TW201528198A (en) * | 2013-10-09 | 2015-07-16 | Resmed Sensor Technologies Ltd | Fatigue monitoring and management system and the method thereof |
TWI511092B (en) * | 2013-03-27 | 2015-12-01 | 原相科技股份有限公司 | Driving safety monitoring apparatus and method thereof for human-driven vehicle |
-
2017
- 2017-12-05 TW TW106142601A patent/TWI660276B/en active
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US7659827B2 (en) * | 2006-05-08 | 2010-02-09 | Drivecam, Inc. | System and method for taking risk out of driving |
US8508353B2 (en) * | 2009-01-26 | 2013-08-13 | Drivecam, Inc. | Driver risk assessment system and method having calibrating automatic event scoring |
TWI511092B (en) * | 2013-03-27 | 2015-12-01 | 原相科技股份有限公司 | Driving safety monitoring apparatus and method thereof for human-driven vehicle |
TW201528198A (en) * | 2013-10-09 | 2015-07-16 | Resmed Sensor Technologies Ltd | Fatigue monitoring and management system and the method thereof |
Also Published As
Publication number | Publication date |
---|---|
TW201926075A (en) | 2019-07-01 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
EP3730375B1 (en) | Method and system for analysing the control of a vehicle | |
WO2020244288A1 (en) | Method and apparatus for evaluating truck driving behaviour based on gps trajectory data | |
Castignani et al. | An evaluation study of driver profiling fuzzy algorithms using smartphones | |
US20160203560A1 (en) | Driver assessment and recommendation system in a vehicle | |
US9082072B1 (en) | Method for applying usage based data | |
US20190228596A1 (en) | In-Vehicle Monitoring and Reporting Apparatus for Vehicles | |
CN107153914B (en) | System and method for evaluating automobile operation risk | |
US20220114560A1 (en) | Predictive maintenance | |
Jovanis et al. | Analysis of naturalistic driving event data: Omitted-variable bias and multilevel modeling approaches | |
CN102717765A (en) | Fatigue driving detection method and anti-fatigue driving auxiliary device | |
JP2007213324A (en) | Driving evaluation support system and method for calculating driving evaluation data | |
WO2011004372A1 (en) | Driver profiling | |
Rahman et al. | Iot based smart helmet and accident identification system | |
WO2019125532A1 (en) | Programmatic ally identifying a personality of an autonomous vehicle | |
US20200364800A1 (en) | Systems and method for calculating liability of a driver of a vehicle | |
CN107918826A (en) | The driver's evaluation and dispatching method that a kind of driving environment perceives | |
CN116911610A (en) | Method and system for monitoring, evaluating and early warning of driving safety risk of transport vehicle | |
TWI660276B (en) | System and method for applying user profile model to score | |
US11879810B2 (en) | Predicting tire imbalance and/or wheel misalignment | |
CN116453345B (en) | Bus driving safety early warning method and system based on driving risk feedback | |
CN114997756B (en) | Vehicle residual value evaluation system and method based on cockpit data | |
CN115871682A (en) | Method and system for monitoring, evaluating and early warning safe driving behavior of driver | |
CN111882382A (en) | Shared automobile data storage management method based on data and risk association model | |
WO2024055346A1 (en) | Vehicle status monitoring model training method, application method, apparatus, and vehicle | |
WO2017114892A1 (en) | System and method for calculating at least one risk index related to the future driving activity of a user |