TWI812344B - Driving threat analysis and control system based on driving state of advanced driver assist system and method thereof - Google Patents

Driving threat analysis and control system based on driving state of advanced driver assist system and method thereof Download PDF

Info

Publication number
TWI812344B
TWI812344B TW111126267A TW111126267A TWI812344B TW I812344 B TWI812344 B TW I812344B TW 111126267 A TW111126267 A TW 111126267A TW 111126267 A TW111126267 A TW 111126267A TW I812344 B TWI812344 B TW I812344B
Authority
TW
Taiwan
Prior art keywords
driving
vehicle
advanced
assistance system
lane departure
Prior art date
Application number
TW111126267A
Other languages
Chinese (zh)
Other versions
TW202404389A (en
Inventor
張本杰
洪偉晉
吳羿龍
Original Assignee
國立雲林科技大學
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 國立雲林科技大學 filed Critical 國立雲林科技大學
Priority to TW111126267A priority Critical patent/TWI812344B/en
Application granted granted Critical
Publication of TWI812344B publication Critical patent/TWI812344B/en
Publication of TW202404389A publication Critical patent/TW202404389A/en

Links

Landscapes

  • Hybrid Electric Vehicles (AREA)
  • Devices For Executing Special Programs (AREA)
  • Traffic Control Systems (AREA)

Abstract

A driving threat analysis and control system based on driving state of advanced driver assist system and method thereof system and a method thereof are provided. Vehicle cloud based on cloud computing and mobile edge computing to minimize and avoid dangerous and unstable driving or self-driving vehicle is provided. Using CAP between two independent vehicles of target vehicle and preceding vehicle to improve ACC is prone to a large number of state transitions by collection of big data prediction of driving state information and 5G eV2X. Driving status of high-threat areas is analyzed by using 3-level cloud computing mechanism to reduce driving threats and realize active and safe driving of self-driving vehicles and assisted driving vehicles. Therefore, the efficiency of Avoiding and preventing automated driving collisions and ensuring the safety of fleet vehicles while driving autonomously may be achieved.

Description

基於先進駕駛輔助系統中行車狀態的駕駛威脅分析控制系統及其方法Driving threat analysis control system and method based on driving status in advanced driving assistance systems

一種分析控制系統及其方法，尤其是指一種基於先進駕駛輔助系統中行車狀態的駕駛威脅分析控制系統及其方法。An analysis control system and a method thereof, in particular, a driving threat analysis control system and a method based on the driving status in an advanced driving assistance system.

在5G側鏈（sidelink）中，eV2X增強型蜂巢式車聯網被指定用來支持5G 車聯網的通訊，蜂巢式車聯網被定義來應用於自動駕車以及分成5個級別的自動駕駛輔助系統，並期望最後能實現自主的安全駕駛。In the 5G side chain (sidelink), eV2X enhanced cellular Internet of Vehicles is designated to support 5G Internet of Vehicles communications. Cellular Internet of Vehicles is defined to be applied to autonomous driving and autonomous driving assistance systems divided into five levels, and It is hoped that autonomous and safe driving will eventually be achieved.

由於車聯網以及先進駕駛輔助系統的各種應用，更進一步的全自動駕車會針對不同服務品質需求以及不同駕駛狀態來生成不同種類的流量，而不同類型的網路切片（例如：emergency、eV2X、uRLLC、eMBB和mMTC…等）則動態形成服務功能鏈（Service Function Chaining，SFCs）。進而期望實現在超可靠低延遲的即時通訊以分享車輛狀態於自適應循行控制、車道偏離警告/車道偏離輔助以及車輛隊列的先進駕駛輔助系統。Due to the various applications of the Internet of Vehicles and advanced driving assistance systems, further fully autonomous driving will generate different types of traffic according to different service quality requirements and different driving conditions, and different types of network slices (such as emergency, eV2X, uRLLC , eMBB and mMTC... etc.) dynamically form service function chains (Service Function Chaining, SFCs). It is further expected to achieve ultra-reliable and low-latency instant messaging to share vehicle status in advanced driving assistance systems such as adaptive cruising control, lane departure warning/lane departure assist, and vehicle platooning.

綜上所述，可知先前技術中長期以來一直存在現有自動無人駕駛車隊仍存在駕駛碰撞事故發生的問題，因此有必要提出改進的技術手段，來解決此一問題。To sum up, it can be seen that in the previous technology, there has been a long-standing problem of driving collision accidents in existing autonomous driverless fleets. Therefore, it is necessary to propose improved technical means to solve this problem.

有鑒於先前技術存在現有自動無人駕駛車隊仍存在駕駛碰撞事故發生的問題，本發明遂揭露一種基於先進駕駛輔助系統中行車狀態的駕駛威脅分析控制系統及其方法，其中：In view of the problem of driving collision accidents in existing automatic driverless fleets in the prior art, the present invention discloses a driving threat analysis and control system and method based on the driving status of the advanced driving assistance system, wherein:

本發明所揭露的基於先進駕駛輔助系統中行車狀態的駕駛威脅分析控制系統，適用於5G通訊的行車裝置，其包含：定義與分類模組、門檻值設定模組、門檻值計算模組以及控制訊息傳輸模組。The driving threat analysis and control system based on the driving status in the advanced driving assistance system disclosed by the present invention is suitable for 5G communication driving devices. It includes: a definition and classification module, a threshold setting module, a threshold calculation module and a control module. Message transmission module.

定義與分類模組是對每個先進駕駛輔助系統（Advanced Driver Assist System，ADAS）基於檢測與收集到相鄰車輛的訊息進行定義並分類行車狀態；門檻值設定模組是設定目標車輛以及前方車輛之間的最小安全距離，最小安全距離、目標車輛以及前方車輛的跟車資訊（vehicle-following）設定為對應先進駕駛輔助系統類型的相鄰車輛的動態門檻值（dynamic threshold）；門檻值計算模組是將動態門檻值提供給車道偏離警告（Lane Departure Warning，LDW）/車道偏離輔助（Lane Departure Assist，LDA），將多車道映射到單車道並確定邏輯距離以使用相同的演算法互換目標車輛以及前方車輛以判斷出車道偏離警告/車道偏離輔助的最佳動態門檻值；及控制訊息傳輸模組是透過5G增強型蜂巢式車聯網通訊並利用群播發送控制訊息給車隊的其他車輛，藉此控制車隊中所有車輛的速度。The definition and classification module defines and classifies the driving status of each Advanced Driver Assist System (ADAS) based on the detection and collection of information from adjacent vehicles; the threshold setting module sets the target vehicle and the vehicle ahead. The minimum safe distance, the minimum safe distance, the target vehicle and the vehicle-following information of the vehicle in front are set as the dynamic threshold of adjacent vehicles corresponding to the advanced driving assistance system type; the threshold calculation model The group provides dynamic thresholds to Lane Departure Warning (LDW)/Lane Departure Assist (LDA), maps multiple lanes to single lanes and determines logical distances to interchange target vehicles using the same algorithm and the vehicle in front to determine the optimal dynamic threshold of lane departure warning/lane departure assist; and the control message transmission module communicates through 5G enhanced cellular car networking and uses multicast to send control messages to other vehicles in the fleet. This controls the speed of all vehicles in the convoy.

本發明所揭露的基於先進駕駛輔助系統中行車狀態的駕駛威脅分析控制方法，其包含下列步驟：The driving threat analysis and control method based on the driving status in the advanced driving assistance system disclosed by the present invention includes the following steps:

首先，行車裝置對每個先進駕駛輔助系統基於檢測與收集到相鄰車輛的訊息進行定義並分類行車狀態；接著，行車裝置設定目標車輛以及前方車輛之間的最小安全距離，最小安全距離、目標車輛以及前方車輛的跟車資訊設定為對應先進駕駛輔助系統類型的相鄰車輛的動態門檻；接著，行車裝置將動態門檻值提供給車道偏離警告/車道偏離輔助，將多車道映射到單車道並確定邏輯距離以使用相同的演算法互換目標車輛以及前方車輛以判斷出車道偏離警告/車道偏離輔助的最佳動態門檻值；最後，行車裝置透過5G增強型蜂巢式車聯網通訊並利用群播發送控制訊息給車隊的其他車輛，藉此控制車隊中所有車輛的速度。First, the driving device defines and classifies the driving status of each advanced driving assistance system based on the detection and collection of information from adjacent vehicles; then, the driving device sets the minimum safe distance between the target vehicle and the vehicle ahead, the minimum safe distance, the target The following information of the vehicle and the vehicle in front is set as the dynamic threshold of adjacent vehicles corresponding to the advanced driving assistance system type; then, the driving device provides the dynamic threshold value to the lane departure warning/lane departure assist, mapping multiple lanes to a single lane and Determine the logical distance to use the same algorithm to exchange the target vehicle and the vehicle in front to determine the optimal dynamic threshold for lane departure warning/lane departure assistance; finally, the driving device communicates through 5G enhanced cellular car networking and uses multicast to send Control messages to other vehicles in the convoy, thereby controlling the speed of all vehicles in the convoy.

本發明所揭露的系統及方法如上，與先前技術之間的差異在於基於雲端運算和行動邊緣運算的車載雲端以最大限度地減少和避免危險與不穩定的駕駛或自駕車，並透過駕駛狀態訊息的收集的大數據預測和通過5G增強型蜂巢式車聯網通訊的方式在目標車輛與前方車輛這兩個獨立車輛之間使用CAP來改善ACC容易產生大量狀態轉換的問題，運用3級別雲端運算機制分析高威脅區域的行車狀態，達到確實減少駕駛威脅以及實現自駕車輛和輔助駕駛車輛的主動安全駕駛。The system and method disclosed in the present invention are as above. The difference between the system and the previous technology is that the vehicle cloud is based on cloud computing and mobile edge computing to minimize and avoid dangerous and unstable driving or self-driving, and through driving status information The collected big data is predicted and used through 5G enhanced cellular Internet of Vehicles communication to use CAP between the two independent vehicles of the target vehicle and the vehicle in front to improve the problem that ACC is prone to a large number of state transitions, using a 3-level cloud computing mechanism Analyze the driving status of high-threat areas to truly reduce driving threats and realize active and safe driving of self-driving vehicles and assisted driving vehicles.

透過上述的技術手段，本發明可以達成避免和防止自動駕駛碰撞發生與確保車隊車輛自動駕駛時安全的技術功效。Through the above technical means, the present invention can achieve the technical effects of avoiding and preventing autonomous driving collisions and ensuring the safety of fleet vehicles during autonomous driving.

以下將配合圖式及實施例來詳細說明本發明的實施方式，藉此對本發明如何應用技術手段來解決技術問題並達成技術功效的實現過程能充分理解並據以實施。The embodiments of the present invention will be described in detail below with reference to the drawings and examples, so that the implementation process of how to apply technical means to solve technical problems and achieve technical effects of the present invention can be fully understood and implemented accordingly.

以下首先要說明本發明所揭露的基於先進駕駛輔助系統中行車狀態的駕駛威脅分析控制系統，並請參考「第1圖」所示，「第1圖」繪示為本發明基於先進駕駛輔助系統中行車狀態的駕駛威脅分析控制系統的系統方塊圖。The following will first describe the driving threat analysis and control system based on the driving status of the advanced driving assistance system disclosed in the present invention. Please refer to "Figure 1". "Figure 1" illustrates the advanced driving assistance system based on the present invention. System block diagram of the driving threat analysis and control system in the driving state.

本發明所揭露的基於先進駕駛輔助系統中行車狀態的駕駛威脅分析控制系統，適用於5G通訊的行車裝置10，其包含：定義與分類模組11、門檻值設定模組12、門檻值計算模組13以及控制訊息傳輸模組14。The driving threat analysis and control system based on the driving status in the advanced driving assistance system disclosed by the present invention is suitable for the driving device 10 of 5G communication. It includes: a definition and classification module 11, a threshold setting module 12, and a threshold calculation module. Group 13 and control message transmission module 14.

定義與分類模組11是對每個先進駕駛輔助系統（Advanced Driver Assist System，ADAS）基於檢測與收集到相鄰車輛的訊息進行定義並分類行車狀態；門檻值設定模組12是設定目標車輛以及前方車輛之間的最小安全距離，最小安全距離、目標車輛以及前方車輛的跟車資訊（vehicle-following）設定為對應先進駕駛輔助系統類型的相鄰車輛的動態門檻值（dynamic threshold）；門檻值計算模組13是將動態門檻值提供給車道偏離警告（Lane Departure Warning，LDW）/車道偏離輔助（Lane Departure Assist，LDA），將多車道映射到單車道並確定邏輯距離以使用相同的演算法互換目標車輛以及前方車輛以判斷出車道偏離警告/車道偏離輔助的最佳動態門檻值；及控制訊息傳輸模組14是透過5G增強型蜂巢式車聯網通訊並利用群播發送控制訊息給車隊的其他車輛，藉此控制車隊中所有車輛的速度。The definition and classification module 11 is to define and classify the driving status of each Advanced Driver Assist System (ADAS) based on the information detected and collected from adjacent vehicles; the threshold setting module 12 is to set the target vehicle and The minimum safe distance between the vehicle in front, the minimum safe distance, the target vehicle and the following information of the vehicle in front (vehicle-following) are set as the dynamic threshold of adjacent vehicles corresponding to the advanced driving assistance system type; the threshold value Calculation module 13 provides dynamic thresholds to Lane Departure Warning (LDW)/Lane Departure Assist (LDA), maps multi-lanes to single lanes and determines logical distances using the same algorithm Interchange the target vehicle and the vehicle in front to determine the optimal dynamic threshold value of lane departure warning/lane departure assist; and the control message transmission module 14 uses 5G enhanced cellular car networking communication and uses multicast to send control messages to the fleet. other vehicles, thereby controlling the speed of all vehicles in the convoy.

行車裝置10更包含：評估與分析模組15、流量切片選取模組16、行車狀態轉換模組17以及流量生成模組18。The driving device 10 further includes: an evaluation and analysis module 15 , a traffic slice selection module 16 , a driving state conversion module 17 and a traffic generation module 18 .

評估與分析模組15是依據先進駕駛輔助系統的行車狀態以及駕駛威脅（AAT）機制的自適應巡航控制、車道偏離警告以及協同式自適應巡航控制（Cooperative Adaptive Cruise Control，CACC）與自動隊列行駛（Autonomous Platooning）系統（CAP）進行評估其著色以及分析效率；流量切片選取模組16依據不同先進駕駛輔助系統對車輛的行車狀態變化使用不同的流量切片優先級；行車狀態轉換17是於先進駕駛輔助系統的行車狀態從狀態i更改為狀態j時，先進駕駛輔助系統即時轉換行車狀態；及流量生成模組18是於先進駕駛輔助系統、自適應巡航控制、車道偏離警告以及CAP的動態門檻值的行車狀態變為紅色危險或是黃色警告時，透過5G增強型蜂巢式車聯網生成uRLLC-Dangerous或是uRLLC-Warning的流量。The evaluation and analysis module 15 is based on the driving status and driving threat (AAT) mechanism of the advanced driver assistance system, adaptive cruise control, lane departure warning, cooperative adaptive cruise control (CACC) and automatic platooning (Autonomous Platooning) system (CAP) to evaluate its coloring and analysis efficiency; the traffic slice selection module 16 uses different traffic slice priorities according to different advanced driving assistance systems for vehicle driving state changes; the driving state transition 17 is based on advanced driving When the driving state of the assistance system changes from state i to state j, the advanced driving assistance system immediately switches to the driving state; and the traffic generation module 18 is the dynamic threshold value of the advanced driving assistance system, adaptive cruise control, lane departure warning and CAP When the driving status changes to red danger or yellow warning, uRLLC-Dangerous or uRLLC-Warning traffic is generated through 5G enhanced cellular car networking.

本發明是對行車狀態進行分析並提出了幾種不同的先進駕駛輔助系統對不同行車威脅進行著色以及分類。接著，把已分析完的先進駕駛輔助系統行車狀態即時的向自身車輛以及有可能會有潛在行車威脅的相鄰車輛進行訊息通告。通告的傳輸模式可以分為是廣播、組播或單播模式。藉此可以有效避免以及防止駕駛碰撞的事故發生，並能更進一步的適用在5級別（five levels）的先進駕駛輔助系統以及自動無人駕駛的車輛上（Active Safe Driving，ASD），並為每輛車輛形成自適應主動安全駕駛（Adaptive Active Safe Driving，AASD）。The present invention analyzes the driving status and proposes several different advanced driving assistance systems to color and classify different driving threats. Then, the analyzed driving status of the advanced driving assistance system is notified in real time to the own vehicle and adjacent vehicles that may pose potential driving threats. The transmission mode of the announcement can be divided into broadcast, multicast or unicast mode. This can effectively avoid and prevent driving collision accidents, and can be further applied to five levels of advanced driving assistance systems and autonomous driverless vehicles (Active Safe Driving, ASD), and for each vehicle. The vehicle forms Adaptive Active Safe Driving (AASD).

考慮在先進駕駛輔助系統中最有用的幾項功能，例如，自適應循行控制（Adaptive Cruise Control，ACC）、車道偏離警告（Lane Departure Warning，LDW）/車道偏離輔助（Lane Departure Assist，LDA）、盲點偵測系統（Blind Spot Detection，BSD）以及協同式自適應巡航控制（Cooperative Adaptive Cruise Control，CACC）與自動隊列行駛（Autonomous Platooning）系統（CAP），其中自適應巡航控制（ACC）、車道偏離警告助以及盲點偵測系統配備不同類型的感測器（例如：超聲波雷達、長/中/近程雷達、攝影機、雷射雷達（LIDAR）…等）並且擴展牛頓運動定律的基本演算法。此外，協同式自適應巡航控制與自動隊列行駛系統則整合了配備兩種上述不同類型感測器以及無線網絡介面的多台自動無人駕駛的車輛。Consider the most useful features in advanced driver assistance systems, such as Adaptive Cruise Control (ACC), Lane Departure Warning (LDW)/Lane Departure Assist (LDA) , Blind Spot Detection (BSD), Cooperative Adaptive Cruise Control (CACC) and Autonomous Platooning (CAP), among which adaptive cruise control (ACC), lane Departure warning assist and blind spot detection systems are equipped with different types of sensors (for example: ultrasonic radar, long/medium/short range radar, camera, lidar (LIDAR)...etc.) and extend the basic algorithm of Newton's laws of motion. In addition, cooperative adaptive cruise control and automatic platooning systems integrate multiple autonomous vehicles equipped with two different types of sensors and wireless network interfaces.

為了分析在不同行車狀態下的先進駕駛輔助系統種類，本發明提出了行車威脅著色分析，其中先對每個先進駕駛輔助系統定義了其行車狀態並將其分為不同的行車狀態，行車狀態例如是：危險（紅色）、警告（黃色）以及安全（綠色）…等，在此僅為舉例說明之，並不以此侷限本發明的應用範疇，而這些被分析的訊息則是基於檢測並收集到相鄰車輛的先進駕駛輔助系統訊息。在紅色危險區域（或範圍）以及黃色警告區域之間的駕駛動態門檻值（driving dynamic threshold）以 表示，而黃色警告區域以及綠色安全區域之間的駕駛動態門檻值則用 表示。具體而言，當先進駕駛輔助系統的行車狀態值超過動態門檻值 時，就表示車輛進入紅色危險的行車狀態；當先進駕駛輔助系統的行車狀態值在動態門檻值 之間時就表示車輛正處於黃色警告的行車狀態；以及當先進駕駛輔助系統的行車狀態值低於動態門檻值 時，則表示車輛處於綠色安全的行車狀態。 In order to analyze the types of advanced driving assistance systems in different driving states, the present invention proposes a driving threat coloring analysis, in which the driving state of each advanced driving assistance system is first defined and divided into different driving states. The driving states are, for example, They are: danger (red), warning (yellow), safety (green), etc. These are only examples and do not limit the scope of application of the present invention. The analyzed messages are based on detection and collection. Advanced driver assistance system messages to adjacent vehicles. The driving dynamic threshold between the red danger zone (or range) and the yellow warning zone is given by represents, and the driving dynamics threshold between the yellow warning area and the green safety area is expressed by express. Specifically, when the driving status value of the advanced driving assistance system exceeds the dynamic threshold When , it means that the vehicle has entered a red dangerous driving state; when the driving state value of the advanced driving assistance system is at the dynamic threshold When it is between, it means that the vehicle is in the driving state of yellow warning; and when the driving state value of the advanced driving assistance system is lower than the dynamic threshold value , it means the vehicle is in a green and safe driving state.

由於不同的先進駕駛輔助系統會有不同的功能，因此每種類型的先進駕駛輔助系統都應需要顯示各自的動態門檻值 。值得注意的是，因為駕駛任何類型車輛的參數都是動態變化的，因此動態門檻值以及先進駕駛輔助系統都不會有一個標準的數值。當先進駕駛輔助系統的行車狀態變成“紅色危險”或“黃色警告”的話，則車輛就會透過5G增強型蜂巢式車聯網（enhanced Vehicle to Everything，eV2X）生成“uRLLC-Dangerous”或是“uRLLC-Warning”的流量。 Since different advanced driver assistance systems have different functions, each type of advanced driver assistance system should display its own dynamic threshold. . It is worth noting that because the parameters for driving any type of vehicle change dynamically, there is no standard value for dynamic thresholds and advanced driving assistance systems. When the driving status of the advanced driving assistance system changes to "Red Danger" or "Yellow Warning", the vehicle will generate "uRLLC-Dangerous" or "uRLLC" through 5G Enhanced Cellular Vehicle Networking (enhanced Vehicle to Everything, eV2X) -Warning" traffic.

依據先進駕駛輔助系統的行車狀態對駕駛威脅進行著色和分類以確定行車狀態之後，當目標車輛檢測到並確定為“紅色危險”的行車狀態時，目標車輛就需要進行減速，直到行車狀態變為“黃色警告”甚至變為“綠色安全”的行車狀態。相同地，當目標車輛確定為“黃色警告”的行車狀態時，車輛就需要進行減速直到行車狀態變為“綠色安全”的行車狀態。透過採取上述措施，依據先進駕駛輔助系統的行車狀態對駕駛威脅進行著色和分類就可以有效地確保目標車輛時時處於最安全的行車狀態。After coloring and classifying driving threats according to the driving status of the advanced driver assistance system to determine the driving status, when the target vehicle detects and determines the driving status as "red danger", the target vehicle needs to slow down until the driving status changes to "Yellow warning" has even changed to "green and safe" driving status. Similarly, when the target vehicle is determined to be in a "yellow warning" driving state, the vehicle needs to decelerate until the driving state changes to a "green and safe" driving state. By taking the above measures and coloring and classifying driving threats according to the driving status of the advanced driving assistance system, it can effectively ensure that the target vehicle is in the safest driving status at all times.

自適應巡航控制是從巡航控制（Cruise Control，CC）擴展而來並被開發成能自主的控制固定速度來確保行車間的安全，避免撞到前面的車輛。自適應巡航控制已被廣泛地安裝在那些擁有先進駕駛輔助系統的車輛中，但是這些自適應巡航控制的參數或細節因為在5級別的先進駕駛輔助系統中並未被標準化，因此不同的自適應巡航控制會表現出不同的參數。因此，為了讓自適應巡航控制能實現本發明所提出的動態著色機制，本發明提出了自主確定最優的動態門檻值 的自適應巡航控制演算法。首先，為了確定自適應巡航控制的動態門檻值 ，假設目標車輛 與前方車輛 之間的最小安全距離為 ，該 則被用於作為自適應巡航控制動態門檻值的 。在自適應巡航控制中，如果這兩輛車的車距小於相同設定速度下的車距的話，則自適應巡航控制的行車狀態就會變為“危險”的紅色區並且目標車將會撞上前車。 Adaptive cruise control is extended from cruise control (Cruise Control, CC) and developed to autonomously control a fixed speed to ensure safety on the road and avoid hitting the vehicle in front. Adaptive cruise control has been widely installed in vehicles with advanced driver assistance systems, but the parameters or details of these adaptive cruise controls are not standardized in Level 5 advanced driver assistance systems, so different adaptive Cruise control will exhibit different parameters. Therefore, in order to enable adaptive cruise control to implement the dynamic coloring mechanism proposed by the present invention, the present invention proposes to independently determine the optimal dynamic threshold Adaptive cruise control algorithm. First, in order to determine the dynamic threshold of adaptive cruise control , assuming that the target vehicle with the vehicle in front The minimum safe distance between , the is used as the dynamic threshold for adaptive cruise control . In adaptive cruise control, if the distance between the two vehicles is less than the distance between the two vehicles at the same set speed, the driving status of the adaptive cruise control will change to the "dangerous" red zone and the target car will hit The car in front.

動態門檻值 即 如下列公式所示： dynamic threshold Right now As shown in the following formula:

其中， 表示駕駛員所需的反應距離（即先進駕駛輔助系統的1至3級別）或自動駕駛（ASD）車輛的檢測距離（即先進駕駛輔助系統的4至5級別）； 表示煞車距離所需的時間t； 為反應距離，且 如下列公式所示： in, Indicates the required reaction distance of the driver (i.e., levels 1 to 3 of advanced driver assistance systems) or the detection distance of an autonomous driving (ASD) vehicle (i.e., levels 4 to 5 of advanced driver assistance systems); Represents the time t required for the braking distance; is the reaction distance, and As shown in the following formula:

當自適應巡航控制的反應時間或檢測時間 （例如：0.01秒，在此僅為舉例說明之，並不以此侷限本發明的應用範疇）乘以目標車輛的速度 即表示為 。 When adaptive cruise control reaction time or detection time (For example: 0.01 seconds, this is only an example and does not limit the application scope of the present invention) times the speed of the target vehicle That is expressed as .

即為將兩個相鄰車輛之間的車輛間安全距離設置為 ；由於不同的道路表現出不同的摩擦係數（ ），因此導致不同的摩擦力（用 表示），當 是重力的加速度常數，煞車距離 如下列公式所示： That is, the inter-vehicle safety distance between two adjacent vehicles is set to ; Since different roads exhibit different coefficients of friction ( ), thus resulting in different friction forces (using means), when is the acceleration constant of gravity, the braking distance As shown in the following formula:

故動態門檻值 的最小安全距離 即可表示如下： Therefore, the dynamic threshold the minimum safe distance It can be expressed as follows:

在決定自適應巡航控制的動態門檻值 之後，即可根據跟車（vehicle-following）資訊來決定動態門檻值 。目標車輛 與其前方車輛 行駛的穩定性與動態門檻值 有關，例如：相對速度 以及相對加速度 。基於粒子軌跡運動學（Kinematics of a particle trajectory），在目標車輛 減速並將相對速度調整為0即 之後，可以如下確定調整時間即 如下： In determining the dynamic threshold for adaptive cruise control Afterwards, the dynamic threshold can be determined based on vehicle-following information. . target vehicle with the vehicle in front of it Driving stability and dynamic thresholds Relevant, for example: relative speed and relative acceleration . Based on Kinematics of a particle trajectory, in the target vehicle Slow down and adjust the relative speed to 0 i.e. After that, the adjustment time can be determined as follows: as follows:

調整距離 如下公式所示： Adjust distance As shown in the following formula:

動態門檻值 即 如下公式所示： dynamic threshold Right now As shown in the following formula:

在自適應巡航控制中當車輛間的距離小於 且大於 時，則目標車輛就會進入黃色警告區域。綜上所述，確定的自適應巡航控制的行車狀態將被檢測並分類為： In adaptive cruise control, when the distance between vehicles is less than and greater than , the target vehicle will enter the yellow warning area. To sum up, the determined driving status of adaptive cruise control will be detected and classified as:

具體而言，假設目標車輛 的速度為 ，前方車輛的速度為 ；則兩車之間的距離就會是 、 且 ，決定調整時間（ ）為： 。 Specifically, assume that the target vehicle The speed is , the speed of the vehicle ahead is ; then the distance between the two cars will be , and , determine the adjustment time ( )for: .

自適應巡航控制中的動態門檻值 以及 的計算如下所示： Dynamic thresholds in adaptive cruise control as well as The calculation is as follows:

當在 的情況下，距離 會介在 以及 之間，所以目標車輛 就會進入黃色警告區域。然後，車輛 會啟動類型為“uRLLC-Warning”的流量，來透過5G增強型蜂巢式車聯網PC5介面以即時與相鄰車輛共享即時自適應巡航控制的行車狀態，或透過5G服務基地台（Generation Node B，gNB）新無線傳輸（New Radio，NR）-Uu介面與車載雲端（VCC）/行動邊緣運算（Multi-access Edge Computing，MEC）/GCC雲運算共享即時自適應巡航控制的行車狀態。 Dangzai In the case of distance Will be involved as well as between, so the target vehicle It will enter the yellow warning area. Then, the vehicle Traffic of type "uRLLC-Warning" will be activated to share the driving status of real-time adaptive cruise control with adjacent vehicles through the 5G enhanced cellular car networking PC5 interface, or through the 5G service base station (Generation Node B, gNB) New Radio (NR)-Uu interface and vehicle cloud (VCC)/mobile edge computing (Multi-access Edge Computing, MEC)/GCC cloud computing share the driving status of real-time adaptive cruise control.

在車道偏離警告/車道偏離輔助中，目標車輛 必須在切換到左車道或是右車道前打開方向信號燈。有了先進駕駛輔助系統的車道偏離警告/車道偏離輔助之後，若目標車輛要切換的車道上有一些車輛，則目標車輛就會收到車道偏離警告警告的聲音、光線訊息或是輕微的方向盤震動以避免發生撞擊。 In Lane Departure Warning/Lane Departure Assist, the target vehicle The direction signal must be turned on before switching to the left or right lane. With the lane departure warning/lane departure assistance of the advanced driving assistance system, if there are some vehicles in the lane that the target vehicle wants to switch to, the target vehicle will receive a lane departure warning sound, light message or slight steering wheel vibration. to avoid collision.

一般情況下，當目標車輛不須切換行駛車道或是駕駛（或是4級別/5級別先進駕駛輔助系統的自動駕駛車輛（Autonomous Self-Driving vehicle））忘記打開方向信號燈，車道偏離警告仍然會在上述的情況發生時啟動並改善自動安全駕駛的安全性，然而在相同的情況下，車道偏離輔助系統將不會啟動，即車道偏離輔助會認為目標車輛不需要改變行駛車道，取決於車道偏離警告以及車道偏離輔助主要功能性的不同。Under normal circumstances, when the target vehicle does not need to switch lanes or the driver (or an Autonomous Self-Driving vehicle with level 4/5 advanced driving assistance systems) forgets to turn on the direction signal, the lane departure warning will still be on. When the above situation occurs, it activates and improves the safety of automatic driving. However, in the same situation, the lane departure assist system will not activate, that is, the lane departure assist will think that the target vehicle does not need to change the driving lane, depending on the lane departure warning. And the main functional differences of lane departure assist.

車道偏離警告/車道偏離輔助動態著色機制以自主的決定最佳動態門檻值 給車道偏離警告/車道偏離輔助。為了有效地簡化車道偏離警告/車道偏離輔助演算法，多條的車道被映射到單一車道與確定邏輯距離，目標車輛 在車道 與後方車輛 在車道 的實際車距記做 大於邏輯距離 。為了最大化車道偏離警告/車道偏離輔助安全性，較短的邏輯距離 會再映射到單一車道模型中被捨棄。邏輯距離 如下列公式所示： Lane departure warning/lane departure assist dynamic coloring mechanism to autonomously determine the optimal dynamic threshold Gives Lane Departure Warning/Lane Departure Assist. To effectively simplify the lane departure warning/lane departure assist algorithm, multiple lanes are mapped to a single lane with a determined logical distance to the target vehicle. in driveway with vehicles behind in driveway The actual distance between vehicles is recorded as greater than logical distance . To maximize Lane Departure Warning/Lane Departure Assist safety, shorter logical distance Will be remapped to the single lane model and discarded. logical distance As shown in the following formula:

其中， 記做車道 在道路 的車道寬度。 in, mark as lane on the road lane width.

車道偏離警告/車道偏離輔助中目標車輛 在後方車輛 的前方，車道偏離警告/車道偏離輔助將目標車輛 以及後方車輛 使用相同的演算法互換以判斷最佳的動態門檻值 與 給車道偏離警告/車道偏離輔助。即在車道偏離警告/車道偏離輔助後方車輛的 被重新記為 且前方車輛 被重新記為 。最佳的動態門檻值 與 給車道偏離警告/車道偏離輔助結果如下列公式所示： Target vehicle in Lane Departure Warning/Lane Departure Assist vehicle behind ahead, Lane Departure Warning/Lane Departure Assist will target the vehicle and rear vehicles Use the same algorithm interchange to determine the best dynamic threshold and Gives Lane Departure Warning/Lane Departure Assist. That is, in lane departure warning/lane departure assist, the vehicle behind was re-recorded as And the vehicle in front was re-recorded as . optimal dynamic threshold and The result of giving Lane Departure Warning/Lane Departure Assist is as shown in the following formula:

其中， 表示調整距離， 由下列公式所示： in, Represents the adjustment distance, It is shown by the following formula:

其中， 與 分別表示為後方車輛 的速度（即重新記為 ）在車道 與前車 （即重新記為 或目標車輛）在車道 ，後方車輛的速度 （即 ）在車道 可以藉由相對速度方程式，即 ，被目標車輛 （即 或目標車輛）在車道 決定。 in, and Represented as rear vehicles respectively speed (i.e. re-recorded as ) in the driveway with the car in front (i.e. re-recorded as or target vehicle) in the lane , the speed of the vehicle behind (Right now ) in the driveway It can be obtained by the relative velocity equation, that is , the target vehicle (Right now or target vehicle) in the lane Decide.

判定車道偏離警告/車道偏離輔助的行車狀態檢測與分類如下列所示:The driving status detection and classification to determine lane departure warning/lane departure assist are as follows:

具體而言，假設目標車輛的速度 （或 ）在車道 是 ；後方車輛 （或 ）在車道 是 ；兩車間距離 是 ； ，調整時間（ ）即 。 Specifically, assume that the speed of the target vehicle (or ) in the driveway yes ;Vehicles behind (or ) in the driveway yes ;Distance between two workshops yes ; , adjust time ( )Right now .

對於車道偏離警告/車道偏離輔助動態門檻值 與 如下列公式所示： For lane departure warning/lane departure assist dynamic thresholds and As shown in the following formula:

。 .

在實施例中， ，距離 是在 與 之間，即 ，則目標車輛 進入黃色警戒區域。因此目標車輛 透過5G增強型蜂巢式車聯網PC5介面或透過5G gNB NR-Uu介面到VCC/MEC/GCC雲端運算向附近車輛發送“uRLLC-Warning”型態的流量以共享這個在時間t的即時車道偏離警告/車道偏離輔助的行車狀態。 In an embodiment, ,distance is in and between, that is , then the target vehicle Enter the yellow alert area. Therefore the target vehicle Send "uRLLC-Warning" type traffic to nearby vehicles through the 5G enhanced cellular car network PC5 interface or through the 5G gNB NR-Uu interface to VCC/MEC/GCC cloud computing to share this real-time lane departure warning at time t /Driving status of lane departure assist.

在CAP裡一個車隊是由最前排的車輛帶領著隨後的其他車輛動態形成的，先進駕駛輔助系統的CAP被用來避免因非同步加速或煞車而產生的行駛脈衝。領頭車輛透過5G增強型蜂巢式車聯網通訊並利用群播發送控制訊息給車隊其他車輛，藉此控制所有車輛成員的速度，然後車輛成員會同步的增加或減少速度。因此，在CAP裡先進駕駛輔助系統中的自適應巡航控制被車隊領頭車輛負責操作，而先進駕駛輔助系統中的CAP則是由車隊裡的每一台車輛來操作。特別的是，首先，車隊領頭車輛 採用自適應巡航控制來保障車隊裡的車輛之間能在“Green Safe”的行車狀態裡安全行駛。車隊領頭車輛 以及前排車輛的車輛間距應該大於動態門檻值 ，車隊領頭車輛的速度 如下列公式所示： In CAP, a convoy is dynamically formed by the vehicle in the front leading the other vehicles that follow. The CAP of the advanced driving assistance system is used to avoid driving pulses caused by asynchronous acceleration or braking. The lead vehicle communicates through 5G enhanced cellular telematics and uses multicast to send control messages to other vehicles in the fleet to control the speed of all vehicle members, and then the vehicle members will increase or decrease their speed simultaneously. Therefore, in CAP, the adaptive cruise control in the advanced driver assistance system is operated by the lead vehicle in the fleet, while the CAP in the advanced driver assistance system is operated by every vehicle in the fleet. What’s special is that, first of all, the leading vehicle in the convoy Adaptive cruise control is used to ensure that vehicles in the fleet can drive safely in a "Green Safe" driving state. Convoy lead vehicle And the distance between vehicles in the front row should be greater than the dynamic threshold. , the speed of the leading vehicle in the convoy As shown in the following formula:

每一個車隊成員採用CAP來維持自己與前方車輛之間的穩定行駛速度並透過5G增強型蜂巢式車聯網通訊的方式來達到同步加速與煞車控制。在接收到由車隊領頭車輛 發送的群播車隊訊息，車隊成員車輛 會先利用下列公式以確定車隊領頭車輛 以及此成員車輛 的加速度差 ： Each team member uses CAP to maintain a stable driving speed between themselves and the vehicle in front and achieve synchronous acceleration and braking control through 5G enhanced cellular telematics communication. Upon receiving the vehicle from the convoy lead Group broadcast fleet messages sent to fleet member vehicles The following formula will first be used to determine the lead vehicle in the convoy and this member's vehicle The acceleration difference :

其中， 為加速時的速度偏差率並設定成常數為0.4 ；在時間 時，車隊成員 會根據前一輛車隊成員 在時間 的速度來決定自己的速度，如下列公式所示： in, is the speed deviation rate during acceleration and is set to a constant of 0.4 ;at time When, team members will be based on the previous team members in time to determine your own speed, as shown in the following formula:

其中， 為定位上的控制增益並設定為0.45 ； 為速度偏差上的控制增益並設定為0.25（非單位）；以及 為車隊成員車輛 在時間 時的車輛間距。 in, is the control gain on positioning and is set to 0.45 ; is the control gain on the speed deviation and is set to 0.25 (non-unit); and Vehicles for fleet members in time distance between vehicles.

車隊成員車輛 在時間 時的相對速度可以由下列公式所決定： Fleet member vehicles in time The relative speed at can be determined by the following formula:

其中， 為車隊領頭車輛 與成員車輛 之間的加速度差；以及 為車隊同步週期（例如：1 sec，在此僅為舉例說明之，並不以此侷限本發明的應用範疇）。 in, Lead vehicle for the convoy with member vehicles the acceleration difference between; and is the fleet synchronization period (for example: 1 sec, this is only an example and does not limit the application scope of the present invention).

車隊成員車輛 的動態門檻值 即 已被決定如下列公式所示： Fleet member vehicles dynamic threshold Right now has been determined as shown in the following formula:

成員 的動態門檻值 即 已被決定如下列公式所示： member dynamic threshold Right now has been determined as shown in the following formula:

其中， 為車隊成員 之間的調整距離。 in, for team members the adjustment distance between them.

最後，CAP的行車狀態被決定並分類如下：Finally, the CAP driving status is determined and classified as follows:

具體而言，假設車隊領頭車輛 的速度為 ；（目標）車隊成員車輛 的速度為 ；前一輛成員車輛 的速度為 ；而兩者成員車輛的間距為 ； ，而 。 Specifically, assume that the lead vehicle in the convoy The speed is ;(Target) Fleet member vehicle The speed is ;Previous member vehicle The speed is ; and the distance between the two member vehicles is ; ,and .

藉由前述的公式即可決定車隊領頭車輛 以及成員車輛 的加速度差如下： The leading vehicle of the team can be determined by the aforementioned formula. and member vehicles The acceleration difference is as follows:

車隊成員 決定在時間 裡的速度如下列公式： Team members decide on time The speed here is as follows:

車隊成員車輛 在時間 的相對速度如下列公式所決定： Fleet member vehicles in time The relative speed is determined by the following formula:

對於CAP的動態門檻值 與 如下列公式所示： Dynamic threshold for CAP and As shown in the following formula:

；以及 ;as well as

由於成員車輛的間距為 ，即 ，因此目標車隊的成員車輛 進入“Yellow Warning”範圍並在時間 調整速度至 。 Since the distance between member vehicles is ,Right now , so the member vehicles of the target fleet Enter the "Yellow Warning" range and at the time adjust speed to .

自適應巡航控制、車道偏離警告/車道偏離輔助以及先進駕駛輔助系統之CAP的顏色分動態門檻值 與 ，各動態門檻值21請參考「第2圖」所示，「第2圖」繪示為本發明基於ADAS行車狀態的ACC、LDW/LDA與CAP的著色分析圖。 Color-coded dynamic thresholds for CAP for adaptive cruise control, lane departure warning/lane departure assist, and advanced driver assistance systems and , please refer to "Figure 2" for each dynamic threshold value 21. "Figure 2" shows the coloring analysis diagram of ACC, LDW/LDA and CAP based on the ADAS driving status of the present invention.

接著，對先進駕駛輔助系統的行車狀態以及駕駛威脅（AAT）機制的自適應巡航控制、車道偏離輔助以及CAP進行評估其著色以及分析效率。Next, the coloring and analysis efficiency of the advanced driving assistance system's driving status and driving threat (AAT) mechanisms such as adaptive cruise control, lane departure assist, and CAP are evaluated.

在實施例中，針對在70至420的不同車輛數量下，比較上述建議具有動態門檻值 以及 著色分析的先進駕駛輔助系統AAT機制（即AAT_ADAS_Coloring）以及與需要人類操控的1級別至3級別先進駕駛輔助系統（即具有人類的先進駕駛輔助系統），比較先進駕駛輔助系統中“Red_Dangerous”、“Yellow_Warning”以及“Green_Safe”三種行車狀態的性能指標22，如「第3A圖」至「第3C圖」所示，「第3A圖」繪示為本發明ACC行車狀態圖；「第3B圖」繪示為本發明LDW/LDA行車狀態圖；「第3C圖」繪示為本發明CAP行車狀態圖。 In an embodiment, the above suggestions are compared with dynamic thresholds for different vehicle numbers from 70 to 420 as well as The advanced driving assistance system AAT mechanism of coloring analysis (i.e., AAT_ADAS_Coloring) and the level 1 to level 3 advanced driving assistance systems that require human control (i.e., the advanced driving assistance system with humans), compare "Red_Dangerous" and "Red_Dangerous" in advanced driving assistance systems The performance indicators 22 of the three driving states of "Yellow_Warning" and "Green_Safe" are shown in "Figure 3A" to "Figure 3C". "Figure 3A" shows the ACC driving state diagram of the present invention; "Figure 3B" shows It shows the LDW/LDA driving status diagram of the present invention; "Figure 3C" shows the CAP driving status diagram of the present invention.

在「第3A圖」中，不同車輛數量下具有“AAT_ADAS_Coloring”以及“Human with ADAS”的先進駕駛輔助系統自適應巡航控制的行車狀態數量，所有行車狀態都會隨車輛數量的增加而增加。自適應巡航控制中“AAT_ADAS_Coloring”所產生的“Red_Dangerous”行車狀態數量最少，遠低於具有“Human with ADAS”的自適應巡航控制，其原因為自適應巡航控制中“AAT_ADAS_Coloring”可以透過分析動態門檻值 以及 來有效地避免“Red_Dangerous”的情況。反過來說，儘管傳統的自適應巡航控制先進駕駛輔助系統也會產生較少數量的“Red_Dangerous”，但它會受到靜態自適應巡航控制控制的影響。 In "Figure 3A", the number of driving states of the advanced driving assistance system adaptive cruise control with "AAT_ADAS_Coloring" and "Human with ADAS" under different numbers of vehicles. All driving states will increase as the number of vehicles increases. The number of "Red_Dangerous" driving states generated by "AAT_ADAS_Coloring" in adaptive cruise control is the smallest, which is much lower than the adaptive cruise control with "Human with ADAS". The reason is that "AAT_ADAS_Coloring" in adaptive cruise control can analyze the dynamic threshold value as well as to effectively avoid the "Red_Dangerous" situation. Conversely, traditional adaptive cruise control ADAS will be affected by static adaptive cruise control, although it will also produce a smaller amount of "Red_Dangerous".

自適應巡航控制中“AAT_ADAS_Coloring”所產生的“Yellow_Warning”行車狀態數量與“Human with ADAS”相比較少，自適應巡航控制中“AAT_ADAS_Coloring”所產生的“Green_Safe”行車狀態數量與“Human with ADAS”相比較多，即自適應巡航控制中“AAT_ADAS_Coloring”能明顯地達到更高的安全駕駛。The number of "Yellow_Warning" driving states generated by "AAT_ADAS_Coloring" in adaptive cruise control is less than that of "Human with ADAS". The number of "Green_Safe" driving states generated by "AAT_ADAS_Coloring" in adaptive cruise control is less than that of "Human with ADAS" In comparison, "AAT_ADAS_Coloring" in adaptive cruise control can obviously achieve higher safety driving.

在「第3B圖」中，不同車輛數量下具有“AAT_ADAS_Coloring”以及“Human with ADAS”的先進駕駛輔助系統車道偏離警告/車道偏離輔助行車狀態數量，所有行車狀態都會隨車輛數量的增加而增加。車道偏離警告/車道偏離輔助中“AAT_ADAS_Coloring”所產生的“Red_Dangerous”行車狀態數量最少，遠低於具有“Human with ADAS”的車道偏離警告/車道偏離輔助。儘管傳統的車道偏離警告/車道偏離輔助先進駕駛輔助系統所產生的“Red_Dangerous”行車狀態數量較少，但會受到靜態車道偏離警告/車道偏離輔助控制的影響。In "Figure 3B", the number of advanced driving assistance system lane departure warning/lane departure assist driving states with "AAT_ADAS_Coloring" and "Human with ADAS" under different numbers of vehicles. All driving states will increase as the number of vehicles increases. "AAT_ADAS_Coloring" in Lane Departure Warning/Lane Departure Assist produces the smallest number of "Red_Dangerous" driving states, which is much lower than Lane Departure Warning/Lane Departure Assist with "Human with ADAS". Although the traditional lane departure warning/lane departure assist advanced driving assistance system generates a smaller number of "Red_Dangerous" driving states, it will be affected by the static lane departure warning/lane departure assist control.

車道偏離警告/車道偏離輔助中“AAT_ADAS_Coloring”所產生的“Yellow_Warning”行車狀態數量與“Human with ADAS”相比較少，車道偏離警告/車道偏離輔助中“AAT_ADAS_Coloring”所產生的“Green_Safe”行車狀態數量與“Human with ADAS”相比較多，即車道偏離警告/車道偏離輔助的“AAT_ADAS_Coloring”可以明顯地提高行駛安全性。The number of "Yellow_Warning" driving states generated by "AAT_ADAS_Coloring" in lane departure warning/lane departure assistance is smaller than that in "Human with ADAS". The number of "Green_Safe" driving states generated by "AAT_ADAS_Coloring" in lane departure warning/lane departure assistance is smaller. Compared with "Human with ADAS", that is, "AAT_ADAS_Coloring" of lane departure warning/lane departure assist can significantly improve driving safety.

在「第3C圖」中，不同車輛數量下具有“AAT_ADAS_Coloring”以及“Human with ADAS”的先進駕駛輔助系統CAP（vehicle platooning）行車狀態數量，所有行車狀態都會隨車輛數量的增加而增加。CAP中“AAT_ADAS_Coloring”所產生的行車狀態中“Red_Dangerous”以及“Yellow_Warning”這兩個行車狀態所產生的數量會比較少，但相對的會導致較多的“Green-Safe”行車狀態。In "Figure 3C", the number of driving states of the advanced driving assistance system CAP (vehicle platooning) with "AAT_ADAS_Coloring" and "Human with ADAS" is shown under different numbers of vehicles. All driving states will increase as the number of vehicles increases. Among the driving states generated by "AAT_ADAS_Coloring" in CAP, the number of "Red_Dangerous" and "Yellow_Warning" driving states will be relatively small, but it will result in relatively more "Green-Safe" driving states.

在CAP車隊中，透過在加速以及煞車中使用同步速度控制，使得車輛之間的距離比沒有車隊的車輛要短得多。因此車隊車輛行駛能更加穩定、高效以及安全。所以車隊的“Red_Dangerous”以及“Yellow_Warning”行車狀態的行車狀態會明顯地低於沒有車隊的自適應巡航控制以及車道偏離警告/車道偏離輔助。因此像這種有更加良好性能結果的CAP車隊機制在將來肯定值得在先進駕駛輔助系統中應用以實現許多優勢，像是包括高穩定性、高效、高安全性、低駕駛成本、較短的車間距離以及高速道路通行量等。In a CAP platoon, by using synchronized speed control during acceleration and braking, the distance between vehicles is much shorter than without a platoon. As a result, fleet vehicles can drive more stably, efficiently and safely. Therefore, the driving status of the "Red_Dangerous" and "Yellow_Warning" driving states of the fleet will be significantly lower than the adaptive cruise control and lane departure warning/lane departure assist of the fleet without the fleet. Therefore, a CAP fleet mechanism like this with better performance results is definitely worth applying in advanced driver assistance systems in the future to achieve many advantages, including high stability, efficiency, high safety, low driving costs, and short workshops Distance and highway traffic volume, etc.

針對自適應巡航控制，車道偏離輔助以及CAP提出的AAT機制裡的行車狀態改變進行分析以及著色，利用其結果來呈現行駛的行車狀態。並產生高優先級以及低優先級的切片流量分析結果以作為車聯網在5G增強型蜂巢式車聯網 SPS隨機訪問機制的實驗參數。先進駕駛輔助系統驅動層中，車聯網的車輛通訊透過5G增強型蜂巢式車聯網介面來分享先進駕駛輔助系統中像自適應巡航控制、車道偏離警告或CAP等不同模式的行車狀態分析結果。在先進駕駛輔助系統著色威脅分析層中，檢測每個先進駕駛輔助系統的行車狀態並將其分為三個優先級（或類型）：“Red_Dangerous”、“Yellow_Warning”以及 “Green_Safe”，先進駕駛輔助系統的行車狀態會不時的做出變化，並且不同先進駕駛輔助系統對於車輛新的行車狀態改變會使用不同的流量切片優先級（或類型）來呈現。Analyze and color the driving state changes in adaptive cruise control, lane departure assist and the AAT mechanism proposed by CAP, and use the results to present the driving state. And generate high-priority and low-priority slice traffic analysis results as experimental parameters for the SPS random access mechanism of the Internet of Vehicles in 5G enhanced cellular Internet of Vehicles. In the driver layer of the advanced driving assistance system, the vehicle communication of the Internet of Vehicles uses the 5G enhanced cellular Internet of Vehicles interface to share the driving status analysis results of different modes in the advanced driving assistance system, such as adaptive cruise control, lane departure warning or CAP. In the advanced driving assistance system coloring threat analysis layer, the driving status of each advanced driving assistance system is detected and divided into three priorities (or types): "Red_Dangerous", "Yellow_Warning" and "Green_Safe", advanced driving assistance The driving status of the system will change from time to time, and different advanced driving assistance systems will use different traffic slice priorities (or types) to present new driving status changes of the vehicle.

在先進駕駛輔助系統著色行車狀態改變層中，當行車狀態從狀態 更改為狀態 時，就會發生先進駕駛輔助系統的即時行車狀態轉換。具體而言，當先進駕駛輔助系統為自適應巡航控制時，假設行車狀態從“Yellow_Warning”改變成“Red_Dangerous”的話，它就會被分類為高優先級流量；當先進駕駛輔助系統為CAP時，假設行車狀態從“Yellow_Warning”改變成“Green_Safe”的話，它就會被分類為低優先級流量。 In the advanced driving assistance system colored driving state change layer, when the driving state changes from state change to status , an instant driving state transition of the advanced driving assistance system will occur. Specifically, when the advanced driving assistance system is adaptive cruise control, assuming that the driving status changes from "Yellow_Warning" to "Red_Dangerous", it will be classified as high-priority traffic; when the advanced driving assistance system is CAP, Assuming that the driving status changes from "Yellow_Warning" to "Green_Safe", it will be classified as low-priority traffic.

請參考「第4A圖」至「第4D圖」所示，「第4A圖」繪示為本發明ACC的高低優先級狀態轉移次數圖；「第4B圖」繪示為本發明CAP的高低優先級狀態轉移次數圖；「第4C圖」繪示為本發明方向信號開啟時LDW的高低優先級狀態轉移次數圖；「第4D圖」繪示為本發明方向信號關閉時LDW的高低優先級狀態轉移次數圖。Please refer to "Figure 4A" to "Figure 4D". "Figure 4A" shows the number of high and low priority state transitions of the ACC of the present invention; "Figure 4B" shows the high and low priority of the CAP of the present invention. Figure 4C shows the number of high- and low-priority state transitions of the LDW when the direction signal is turned on according to the present invention; Figure 4D shows the high- and low-priority states of the LDW when the direction signal is turned off according to the present invention. Transfer count graph.

「第4A圖」至「第4C圖」分別示意先進駕駛輔助系統中自適應巡航控制、車道偏離警告以及CAP的高優先級以及低優先級的切片流量著色行車狀態轉變23的評估，每一個先進駕駛輔助系統在產生高優先級流量的行車狀態改變數量上一定會多於低優先級流量的數量。"Figure 4A" to "Figure 4C" respectively illustrate the evaluation of adaptive cruise control, lane departure warning and CAP's high-priority and low-priority slice traffic coloring driving state transition 23 in the advanced driver assistance system. Each advanced The number of driving state changes generated by the driving assistance system for high-priority traffic will definitely be greater than the number of low-priority traffic.

在「第4A圖」以及「第4B圖」中，CAP產生的行車狀態轉換數量不管在高優先級流量以及低優先級流量中都遠低於自適應巡航控制，其原因是CAP對車隊的車輛表現出同步的加速以及煞車，進而導致穩定的車隊行駛以及較少的行車狀態轉換，CAP能有效地帶來了高度穩定、安全以及高效的駕駛。In "Figure 4A" and "Figure 4B", the number of driving state transitions generated by CAP is much lower than that of adaptive cruise control in both high-priority traffic and low-priority traffic. The reason is that CAP has a negative impact on fleet vehicles. Demonstrating synchronized acceleration and braking, resulting in stable fleet driving and fewer driving state transitions, CAP can effectively bring about highly stable, safe and efficient driving.

在「第4C圖」以及「第4D圖」中，示意出方向信號開啟時的車道偏離警告的行車狀態轉變23次數明顯少於方向信號關閉時的車道偏離警告的行車狀態轉變23次數。自適應巡航控制產生的行車狀態轉換數量比帶有信號開啟時以及關閉時的車道偏離警告的行車狀態轉換數量更多，這是因為跟隨前方車輛的這個行為容易受到前方車輛動作影響。In "Figure 4C" and "Figure 4D", it is shown that the number of driving state transitions 23 of the lane departure warning when the direction signal is on is significantly less than the number of driving state transitions 23 of the lane departure warning when the direction signal is off. Adaptive cruise control generates a higher number of driving state transitions than lane departure warning with signal on and off, because this behavior of following the vehicle in front is easily influenced by the movement of the vehicle in front.

導致自適應巡航控制容易與帶有信號開啟以及關閉的CAP以及車道偏離警告之間產生大量行車狀態轉換，並導致較高的行駛威脅可能性以及不安全性。值得注意的是，可以透過5G增強型蜂巢式車聯網通訊的方式在目標車輛與前方車輛這兩個獨立車輛之間使用CAP來改善自適應巡航控制容易產生大量行車狀態轉換的問題。This results in a large number of driving state transitions between adaptive cruise control, CAP with signals on and off, and lane departure warning, and leads to a higher possibility of driving threats and unsafety. It is worth noting that CAP can be used between two independent vehicles, the target vehicle and the vehicle ahead, through 5G enhanced cellular telematics communication to improve the problem that adaptive cruise control is prone to produce a large number of driving state transitions.

接著，以下將說明本發明的運作方法，並請同時參考「第5A圖」以及「第5B圖」所示，「第5A圖」以及「第5B圖」繪示為本發明基於先進駕駛輔助系統中行車狀態的駕駛威脅分析控制方法的方法流程圖。Next, the operation method of the present invention will be described below, and please refer to "Figure 5A" and "Figure 5B" at the same time. "Figure 5A" and "Figure 5B" illustrate the advanced driving assistance system based on the present invention. Method flow chart of the driving threat analysis and control method in the driving state.

基於先進駕駛輔助系統中行車狀態的駕駛威脅分析控制方法，適用於5G通訊的行車裝置，其包含下列步驟：The driving threat analysis and control method based on the driving status in the advanced driving assistance system is suitable for 5G communication driving devices, which includes the following steps:

首先，行車裝置對每個先進駕駛輔助系統基於檢測與收集到相鄰車輛的訊息進行定義並分類行車狀態（步驟101）；接著，行車裝置設定目標車輛以及前方車輛之間的最小安全距離，最小安全距離、目標車輛以及前方車輛的跟車資訊設定為對應先進駕駛輔助系統類型的相鄰車輛的動態門檻（步驟102）；接著，行車裝置將動態門檻值提供給車道偏離警告/車道偏離輔助，將多車道映射到單車道並確定邏輯距離以使用相同的演算法互換目標車輛以及前方車輛以判斷出車道偏離警告/車道偏離輔助的最佳動態門檻值（步驟103）；最後，行車裝置透過5G增強型蜂巢式車聯網通訊並利用群播發送控制訊息給車隊的其他車輛，藉此控制車隊中所有車輛的速度（步驟104）。First, the driving device defines and classifies the driving status of each advanced driving assistance system based on the information detected and collected from adjacent vehicles (step 101); then, the driving device sets the minimum safe distance between the target vehicle and the vehicle in front, the minimum The safety distance, the target vehicle and the following information of the vehicle in front are set as the dynamic threshold of the adjacent vehicle corresponding to the advanced driving assistance system type (step 102); then, the driving device provides the dynamic threshold value to the lane departure warning/lane departure assistance, Map multiple lanes to a single lane and determine the logical distance to use the same algorithm to interchange the target vehicle and the vehicle in front to determine the optimal dynamic threshold for lane departure warning/lane departure assistance (step 103); finally, the driving device passes 5G The enhanced cellular telematics communicates and uses multicast to send control messages to other vehicles in the fleet, thereby controlling the speed of all vehicles in the fleet (step 104).

基於先進駕駛輔助系統中行車狀態的駕駛威脅分析控制方法更包含下列步驟：The driving threat analysis and control method based on the driving status in the advanced driving assistance system further includes the following steps:

首先，行車裝置依據先進駕駛輔助系統的行車狀態以及駕駛威脅機制的自適應巡航控制、車道偏離警告以及協同式自適應巡航控制與自動隊列行駛系統行評估其著色以及分析效率（步驟105）；接著，行車裝置依據不同先進駕駛輔助系統對車輛的行車狀態變化使用不同的流量切片優先級（步驟106）；接著，行車裝置於先進駕駛輔助系統的行車狀態從狀態i更改為狀態j時， 先進駕駛輔助系統即時轉換行車狀態（步驟107）；最後，行車裝置於先進駕駛輔助系統、自適應巡航控制、車道偏離警告以及CAP的動態門檻值的行車狀態變為紅色危險或是黃色警告時，透過5G增強型蜂巢式車聯網生成uRLLC-Dangerous或是uRLLC-Warning的流量（步驟108）。First, the driving device evaluates its coloring and analysis efficiency based on the driving status of the advanced driving assistance system and the driving threat mechanism of adaptive cruise control, lane departure warning, cooperative adaptive cruise control and automatic platooning system (step 105); then , the driving device uses different traffic slice priorities according to different advanced driving assistance systems for changes in the vehicle's driving status (step 106); then, when the driving status of the advanced driving assistance system changes from state i to state j, the driving device performs advanced driving The auxiliary system immediately switches the driving state (step 107); finally, when the driving state of the advanced driving assistance system, adaptive cruise control, lane departure warning and CAP's dynamic threshold changes to red danger or yellow warning, the driving device uses 5G The enhanced cellular Internet of Vehicles generates uRLLC-Dangerous or uRLLC-Warning traffic (step 108).

綜上所述，可知本發明與先前技術之間的差異在於基於雲端運算和行動邊緣運算的車載雲端以最大限度地減少和避免危險與不穩定的駕駛或自駕車，並透過駕駛狀態訊息的收集的大數據預測和通過5G增強型蜂巢式車聯網通訊的方式在目標車輛與前方車輛這兩個獨立車輛之間使用CAP來改善ACC容易產生大量狀態轉換的問題，運用3級別雲端運算機制分析高威脅區域的行車狀態，達到確實減少駕駛威脅以及實現自駕車輛和輔助駕駛車輛的主動安全駕駛。In summary, it can be seen that the difference between the present invention and the prior art lies in the vehicle cloud based on cloud computing and mobile edge computing to minimize and avoid dangerous and unstable driving or self-driving, and through the collection of driving status information Big data prediction and 5G-enhanced cellular Internet of Vehicles communication are used to use CAP between two independent vehicles, the target vehicle and the vehicle in front, to improve the problem that ACC is prone to a large number of state transitions, and use a 3-level cloud computing mechanism to analyze high The driving status of the threat area can truly reduce driving threats and enable active and safe driving of self-driving vehicles and assisted driving vehicles.

藉由此一技術手段可以來解決先前技術所存在現有自動無人駕駛車隊仍存在駕駛碰撞事故發生的問題，進而達成避免和防止自動駕駛碰撞發生與確保車隊車輛自動駕駛時安全的技術功效。This technical means can solve the problem of driving collision accidents in existing autonomous driverless fleets caused by previous technologies, thereby achieving the technical effect of avoiding and preventing autonomous driving collisions and ensuring the safety of fleet vehicles during autonomous driving.

雖然本發明所揭露的實施方式如上，惟所述的內容並非用以直接限定本發明的專利保護範圍。任何本發明所屬技術領域中具有通常知識者，在不脫離本發明所揭露的精神和範圍的前提下，可以在實施的形式上及細節上作些許的更動。本發明的專利保護範圍，仍須以所附的申請專利範圍所界定者為準。Although the embodiments disclosed in the present invention are as above, the described contents are not used to directly limit the patent protection scope of the present invention. Anyone with ordinary knowledge in the technical field to which the present invention belongs may make slight changes in the form and details of the implementation without departing from the spirit and scope of the disclosure of the present invention. The patent protection scope of the present invention must still be defined by the attached patent application scope.

10:行車裝置 11:定義與分類模組 12:門檻值設定模組 13:門檻值計算模組 14:控制訊息傳輸模組 15:評估與分析模組 16:流量切片選取模組 17:行車狀態轉換模組 18:流量生成模組 21:動態門檻值 22:性能指標 23:行車狀態轉變 步驟 101:行車裝置對每個先進駕駛輔助系統基於檢測與收集到相鄰車輛的訊息進行定義並分類行車狀態 步驟 102:行車裝置設定目標車輛以及前方車輛之間的最小安全距離，最小安全距離、目標車輛以及前方車輛的跟車資訊設定為對應先進駕駛輔助系統類型的相鄰車輛的動態門檻 步驟 103:行車裝置將動態門檻值提供給車道偏離警告/車道偏離輔助，將多車道映射到單車道並確定邏輯距離以使用相同的演算法互換目標車輛以及前方車輛以判斷出車道偏離警告/車道偏離輔助的最佳動態門檻值 步驟 104:行車裝置透過5G增強型蜂巢式車聯網通訊並利用群播發送控制訊息給車隊的其他車輛，藉此控制車隊中所有車輛的速度 步驟 105:行車裝置依據先進駕駛輔助系統的行車狀態以及駕駛威脅機制的自適應巡航控制、車道偏離警告以及協同式自適應巡航控制與自動隊列行駛系統行評估其著色以及分析效率 步驟 106:行車裝置依據不同先進駕駛輔助系統對車輛的行車狀態變化使用不同的流量切片優先級 步驟 107:行車裝置於先進駕駛輔助系統的行車狀態從狀態i更改為狀態j時， 先進駕駛輔助系統即時轉換行車狀態 步驟 108:行車裝置於先進駕駛輔助系統、自適應巡航控制、車道偏離警告以及CAP的動態門檻值的行車狀態變為紅色危險或是黃色警告時，透過5G增強型蜂巢式車聯網生成uRLLC-Dangerous或是uRLLC-Warning的流量 10: Driving device 11:Definition and classification module 12: Threshold setting module 13: Threshold calculation module 14:Control message transmission module 15: Evaluation and Analysis Module 16: Traffic slice selection module 17: Driving state conversion module 18: Traffic generation module 21:Dynamic threshold 22:Performance indicators 23: Driving status change Step 101: The driving device defines and classifies the driving status of each advanced driving assistance system based on the information detected and collected from adjacent vehicles. Step 102: The driving device sets the minimum safe distance between the target vehicle and the vehicle in front. The minimum safe distance, the following information of the target vehicle and the vehicle in front are set as the dynamic threshold of adjacent vehicles corresponding to the advanced driving assistance system type. Step 103: The driving device provides the dynamic threshold value to the lane departure warning/lane departure assist, maps the multi-lane to a single lane and determines the logical distance to use the same algorithm to interchange the target vehicle and the vehicle in front to determine the lane departure warning/lane Departure from assist's optimal dynamic threshold Step 104: The driving device communicates through 5G enhanced cellular Internet of Vehicles and uses multicast to send control messages to other vehicles in the fleet, thereby controlling the speed of all vehicles in the fleet. Step 105: The driving device evaluates its coloring and analysis efficiency based on the driving status of the advanced driver assistance system and the driving threat mechanism of adaptive cruise control, lane departure warning, cooperative adaptive cruise control, and automatic platooning systems. Step 106: The driving device uses different traffic slicing priorities according to the changes in the vehicle's driving status based on different advanced driving assistance systems. Step 107: When the driving device changes the driving state of the advanced driving assistance system from state i to state j, the advanced driving assistance system immediately switches to the driving state. Step 108: When the driving status of the advanced driving assistance system, adaptive cruise control, lane departure warning and CAP dynamic threshold changes to red danger or yellow warning, the driving device generates uRLLC-Dangerous through 5G enhanced cellular car networking Or uRLLC-Warning traffic

第1圖繪示為本發明基於先進駕駛輔助系統中行車狀態的駕駛威脅分析控制系統的系統方塊圖。 第2圖繪示為本發明基於ADAS行車狀態的ACC、LDW/LDA與CAP的著色分析圖。 第3A圖繪示為本發明ACC行車狀態圖。 第3B圖繪示為本發明LDW/LDA行車狀態圖。 第3C圖繪示為本發明CAP行車狀態圖。 第4A圖繪示為本發明ACC的高低優先級狀態轉移次數圖。 第4B圖繪示為本發明CAP的高低優先級狀態轉移次數圖。 第4C圖繪示為本發明方向信號開啟時LDW的高低優先級狀態轉移次數圖。 第4D圖繪示為本發明方向信號關閉時LDW的高低優先級狀態轉移次數圖。 第5A圖以及第5B圖繪示為本發明基於先進駕駛輔助系統中行車狀態的駕駛威脅分析控制方法的方法流程圖。 Figure 1 is a system block diagram of the driving threat analysis and control system based on the driving status in the advanced driving assistance system of the present invention. Figure 2 shows the color analysis diagram of ACC, LDW/LDA and CAP based on ADAS driving status according to the present invention. Figure 3A shows a driving status diagram of the ACC according to the present invention. Figure 3B shows a driving state diagram of the LDW/LDA of the present invention. Figure 3C shows a driving status diagram of the CAP of the present invention. Figure 4A is a diagram showing the number of high and low priority state transitions of the ACC of the present invention. Figure 4B is a diagram showing the number of high and low priority state transitions of the CAP of the present invention. Figure 4C shows the number of high and low priority state transitions of the LDW when the direction signal of the present invention is turned on. Figure 4D shows the number of high and low priority state transitions of the LDW when the direction signal is turned off according to the present invention. Figures 5A and 5B illustrate a method flow chart of the driving threat analysis and control method based on the driving status in the advanced driving assistance system of the present invention.

10:行車裝置 10: Driving device

11:定義與分類模組 11:Definition and classification module

12:門檻值設定模組 12: Threshold setting module

13:門檻值計算模組 13: Threshold calculation module

14:控制訊息傳輸模組 14:Control message transmission module

15:評估與分析模組 15: Evaluation and Analysis Module

16:流量切片選取模組 16: Traffic slice selection module

17:行車狀態轉換模組 17: Driving state conversion module

18:流量生成模組 18: Traffic generation module

Claims (10)

一種基於先進駕駛輔助系統中行車狀態的駕駛威脅分析控制系統，適用於5G通訊的行車裝置，其包含：一定義與分類模組，對每個先進駕駛輔助系統(Advanced Driver Assist System，ADAS)基於檢測與收集到相鄰車輛的訊息進行定義並分類行車狀態；一門檻值設定模組，設定目標車輛以及前方車輛之間的最小安全距離，再將最小安全距離、目標車輛以及前方車輛的跟車資訊(vehicle-following)設定為對應先進駕駛輔助系統的動態門檻值(dynamic threshold)；一門檻值計算模組，將所述動態門檻值提供給車道偏離警告(Lane Departure Warning，LDW)/車道偏離輔助(Lane Departure Assist，LDA)，將多車道映射到單車道並確定邏輯距離以使用相同的演算法互換目標車輛以及前方車輛以判斷出車道偏離警告/車道偏離輔助的最佳動態門檻值；及一控制訊息傳輸模組，透過5G增強型蜂巢式車聯網通訊並利用群播發送控制訊息給車隊的其他車輛，藉此控制車隊中所有車輛的速度。 A driving threat analysis and control system based on the driving status of the advanced driving assistance system, suitable for 5G communication driving devices, which includes: a definition and classification module for each advanced driving assistance system (Advanced Driver Assist System, ADAS) based on Detect and collect information from adjacent vehicles to define and classify the driving status; a threshold setting module sets the minimum safe distance between the target vehicle and the vehicle in front, and then sets the minimum safe distance, the target vehicle and the following vehicle in front Information (vehicle-following) is set to correspond to the dynamic threshold of the advanced driving assistance system; a threshold calculation module provides the dynamic threshold to lane departure warning (Lane Departure Warning, LDW)/lane departure Lane Departure Assist (LDA), which maps multiple lanes to a single lane and determines the logical distance to use the same algorithm to interchange the target vehicle and the vehicle ahead to determine the optimal dynamic threshold for lane departure warning/lane departure assistance; and A control message transmission module uses 5G enhanced cellular Internet of Vehicles communication and multicast to send control messages to other vehicles in the fleet, thereby controlling the speed of all vehicles in the fleet. 如請求項1所述的基於先進駕駛輔助系統中行車狀態的駕駛威脅分析控制系統，其中所述行車裝置更包含：一評估與分析模組，依據先進駕駛輔助系統的行車狀態以及駕駛威脅(AAT)機制的自適應巡航控制、車道偏離警告以及協 同式自適應巡航控制(Cooperative Adaptive Cruise Control，CACC)與自動隊列行駛(Autonomous Platooning)系統(CAP)進行評估其著色以及分析效率；一流量切片選取模組，依據不同先進駕駛輔助系統對車輛的行車狀態變化使用不同的流量切片優先級；一行車狀態轉換模組，於先進駕駛輔助系統的行車狀態從狀態i更改為狀態j時，先進駕駛輔助系統即時轉換行車狀態；及一流量生成模組，於先進駕駛輔助系統、自適應巡航控制、車道偏離警告以及CAP的動態門檻值的行車狀態變為紅色危險或是黃色警告時，透過5G增強型蜂巢式車聯網生成uRLLC-Dangerous或是uRLLC-Warning的流量。 The driving threat analysis and control system based on the driving status of the advanced driving assistance system as described in claim 1, wherein the driving device further includes: an evaluation and analysis module, based on the driving status of the advanced driving assistance system and the driving threat (AAT) ) mechanism of adaptive cruise control, lane departure warning and assistance The same type of Cooperative Adaptive Cruise Control (CACC) and Autonomous Platooning (CAP) systems are used to evaluate their coloring and analysis efficiency; a traffic slice selection module is used to evaluate the vehicle's performance based on different advanced driving assistance systems. Driving state changes use different traffic slice priorities; a driving state conversion module, when the driving state of the advanced driving assistance system changes from state i to state j, the advanced driving assistance system immediately converts the driving state; and a traffic generation module , when the driving status of the advanced driving assistance system, adaptive cruise control, lane departure warning and CAP's dynamic threshold changes to red danger or yellow warning, uRLLC-Dangerous or uRLLC- is generated through 5G enhanced cellular Internet of Vehicles. Warning traffic. 如請求項1所述的基於先進駕駛輔助系統中行車狀態的駕駛威脅分析控制系統，其中所述動態門檻值包含有α以及β，動態門檻值的α為目標車輛與前方車輛之間的最小安全距離
Figure 111126267-A0305-02-0028-3
，動態門檻值的α由
Figure 111126267-A0305-02-0028-26
計算得到，動態門檻值的β由
Figure 111126267-A0305-02-0028-2
計算得到，其中d resp (t)表示駕駛員所需的反應距離(即先進駕駛輔助系統的1至3級別)或自動駕駛(ASD)車輛的檢測距離(即先進駕駛輔助系統的4至5級別)；d brake (t)表示煞車距離所需的時間t；d resp (t)為反應距離，d resp (t)由
Figure 111126267-A0305-02-0028-4
計算得到。 The driving threat analysis and control system based on the driving status in the advanced driving assistance system as described in claim 1, wherein the dynamic threshold value includes α and β, and the dynamic threshold value α is the minimum safety between the target vehicle and the vehicle ahead. distance
Figure 111126267-A0305-02-0028-3
, the dynamic threshold α is given by
Figure 111126267-A0305-02-0028-26
It is calculated that the β of the dynamic threshold is given by
Figure 111126267-A0305-02-0028-2
Calculated, where d resp ( t ) represents the required reaction distance of the driver (i.e., levels 1 to 3 of the advanced driver assistance system) or the detection distance of the autonomous driving (ASD) vehicle (i.e., the level 4 to 5 of the advanced driver assistance system) ); d brake ( t ) represents the time t required for the braking distance; d resp ( t ) is the reaction distance, d resp ( t ) is given by
Figure 111126267-A0305-02-0028-4
calculated. 如請求項1所述的基於先進駕駛輔助系統中行車狀態的駕駛威脅分析控制系統，其中所述邏輯距離由
Figure 111126267-A0305-02-0029-5
計算得到，其中，Wr，l記做車道l在道路r的車道寬度。 The driving threat analysis and control system based on the driving status in the advanced driving assistance system as described in claim 1, wherein the logical distance is represented by
Figure 111126267-A0305-02-0029-5
It is calculated, where W r, l is recorded as the lane width of lane l on road r. 如請求項1所述的基於先進駕駛輔助系統中行車狀態的駕駛威脅分析控制系統，其中車道偏離警告/車道偏離輔助的所述最佳的動態門檻值α與β如下列公式：
Figure 111126267-A0305-02-0029-6
 其中，
Figure 111126267-A0305-02-0029-7
表示調整距離，
Figure 111126267-A0305-02-0029-8
由
Figure 111126267-A0305-02-0029-9
計算得到，
Figure 111126267-A0305-02-0029-11
與
Figure 111126267-A0305-02-0029-12
分別表示為後方車輛V jm 的速度在車道l+1與前車V n 在車道l，後方車輛的速度V m 在車道l+1由相對速度方程式
Figure 111126267-A0305-02-0029-13
目標車輛V n 於車道l決定。 The driving threat analysis and control system based on the driving status in the advanced driving assistance system as described in claim 1, wherein the optimal dynamic thresholds α and β of lane departure warning/lane departure assistance are as follows:
Figure 111126267-A0305-02-0029-6
 in,
Figure 111126267-A0305-02-0029-7
Represents the adjustment distance,
Figure 111126267-A0305-02-0029-8
Depend on
Figure 111126267-A0305-02-0029-9
Calculated,
Figure 111126267-A0305-02-0029-11
and
Figure 111126267-A0305-02-0029-12
Expressed respectively as the speed of the rear vehicle V jm in lane l+1 and the front vehicle V n in lane l, the speed of the rear vehicle V m in lane l+1 is given by the relative speed equation
Figure 111126267-A0305-02-0029-13
The target vehicle V n is determined on the lane l. 一種基於先進駕駛輔助系統中行車狀態的駕駛威脅分析控制方法，適用於5G通訊的行車裝置，其包含下列步驟：所述行車裝置對每個先進駕駛輔助系統(Advanced Driver Assist System，ADAS)基於檢測與收集到相鄰車輛的訊息進行定義並分類行車狀態；所述行車裝置設定目標車輛以及前方車輛之間的最小安全距離，再將最小安全距離、目標車輛以及前方車輛的跟車資訊 (vehicle-following)設定為對應先進駕駛輔助系統的動態門檻值(dynamic threshold)；所述行車裝置將所述動態門檻值提供給車道偏離警告(Lane Departure Warning，LDW)/車道偏離輔助(Lane Departure Assist，LDA)，將多車道映射到單車道並確定邏輯距離以使用相同的演算法互換目標車輛以及前方車輛以判斷出車道偏離警告/車道偏離輔助的最佳動態門檻值；及所述行車裝置透過5G增強型蜂巢式車聯網通訊並利用群播發送控制訊息給車隊的其他車輛，藉此控制車隊中所有車輛的速度。 A driving threat analysis and control method based on driving status in advanced driving assistance systems, suitable for 5G communication driving devices, which includes the following steps: the driving device detects each Advanced Driver Assist System (ADAS) based on Define and classify the driving status with the information collected from adjacent vehicles; the driving device sets the minimum safe distance between the target vehicle and the vehicle ahead, and then combines the minimum safety distance, the following information of the target vehicle and the vehicle ahead (vehicle-following) is set to correspond to the dynamic threshold of the advanced driving assistance system; the driving device provides the dynamic threshold to Lane Departure Warning (LDW)/Lane Departure Assist (Lane Departure Assist (LDA), which maps multiple lanes to a single lane and determines the logical distance to use the same algorithm to interchange the target vehicle and the vehicle in front to determine the optimal dynamic threshold for lane departure warning/lane departure assist; and the driving device Control the speed of all vehicles in the fleet by sending control messages to other vehicles in the fleet through 5G enhanced cellular telematics communication and multicast. 如請求項6所述的基於先進駕駛輔助系統中行車狀態的駕駛威脅分析控制方法，其中所述基於先進駕駛輔助系統中行車狀態的駕駛威脅分析控制方法方法更包含：所述行車裝置依據先進駕駛輔助系統的行車狀態以及駕駛威脅(AAT)機制的自適應巡航控制、車道偏離警告以及協同式自適應巡航控制(Cooperative Adaptive Cruise Control，CACC)與自動隊列行駛(Autonomous Platooning)系統(CAP)進行評估其著色以及分析效率；所述行車裝置依據不同先進駕駛輔助系統對車輛的行車狀態變化使用不同的流量切片優先級；所述行車裝置於先進駕駛輔助系統的行車狀態從狀態i更改為狀態j時，先進駕駛輔助系統即時轉換行車狀態；及 所述行車裝置於先進駕駛輔助系統、自適應巡航控制、車道偏離警告以及CAP的動態門檻值的行車狀態變為紅色危險或是黃色警告時，透過5G增強型蜂巢式車聯網生成uRLLC-Dangerous或是uRLLC-Warning的流量。 The driving threat analysis and control method based on the driving status in the advanced driving assistance system as described in claim 6, wherein the driving threat analysis and control method based on the driving status in the advanced driving assistance system further includes: the driving device is based on the advanced driving Evaluate the driving status of the assistance system and the driving threat (AAT) mechanism of adaptive cruise control, lane departure warning, Cooperative Adaptive Cruise Control (CACC) and Autonomous Platooning (CAP) systems Its coloring and analysis efficiency; the driving device uses different traffic slice priorities according to different advanced driving assistance systems for vehicle driving state changes; the driving device changes when the driving state of the advanced driving assistance system changes from state i to state j , the advanced driving assistance system instantly switches driving status; and When the driving status of the advanced driving assistance system, adaptive cruise control, lane departure warning and CAP's dynamic threshold changes to red danger or yellow warning, the driving device generates uRLLC-Dangerous or yellow warning through 5G enhanced cellular Internet of Vehicles. It is uRLLC-Warning traffic. 如請求項6所述的基於先進駕駛輔助系統中行車狀態的駕駛威脅分析控制方法，其中所述動態門檻值包含有α以及β，動態門檻值的α為目標車輛與前方車輛之間的最小安全距離
Figure 111126267-A0305-02-0031-14
，動態門檻值的α由
Figure 111126267-A0305-02-0031-15
計算得到，動態門檻值的β由
Figure 111126267-A0305-02-0031-16
計算得到，其中d resp (t)表示駕駛員所需的反應距離(即先進駕駛輔助系統的1至3級別)或自動駕駛(ASD)車輛的檢測距離(即先進駕駛輔助系統的4至5級別)；d brake (t)表示煞車距離所需的時間t；d resp (t)為反應距離，d resp (t)由
Figure 111126267-A0305-02-0031-17
計算得到。 The driving threat analysis and control method based on the driving status in the advanced driving assistance system as described in claim 6, wherein the dynamic threshold value includes α and β, and the dynamic threshold value α is the minimum safety between the target vehicle and the vehicle ahead. distance
Figure 111126267-A0305-02-0031-14
, the dynamic threshold α is given by
Figure 111126267-A0305-02-0031-15
It is calculated that the β of the dynamic threshold is given by
Figure 111126267-A0305-02-0031-16
Calculated, where d resp ( t ) represents the required reaction distance of the driver (i.e., levels 1 to 3 of the advanced driver assistance system) or the detection distance of the autonomous driving (ASD) vehicle (i.e., the level 4 to 5 of the advanced driver assistance system) ); d brake ( t ) represents the time t required for the braking distance; d resp ( t ) is the reaction distance, d resp ( t ) is given by
Figure 111126267-A0305-02-0031-17
calculated. 如請求項6所述的基於先進駕駛輔助系統中行車狀態的駕駛威脅分析控制方法，其中所述邏輯距離由
Figure 111126267-A0305-02-0031-18
計算得到，其中，Wr，l記做車道l在道路r的車道寬度。 The driving threat analysis and control method based on the driving status in the advanced driving assistance system as described in claim 6, wherein the logical distance is represented by
Figure 111126267-A0305-02-0031-18
It is calculated, where W r, l is recorded as the lane width of lane l on road r. 如請求項6所述的基於先進駕駛輔助系統中行車狀態的駕駛威脅分析控制方法，其中車道偏離警告/車道偏離輔助的所述最佳的動態門檻值α與β如下列公式：
Figure 111126267-A0305-02-0031-19
 其中，
Figure 111126267-A0305-02-0032-20
表示調整距離，
Figure 111126267-A0305-02-0032-23
由
Figure 111126267-A0305-02-0032-24
計算得到，
Figure 111126267-A0305-02-0032-21
與
Figure 111126267-A0305-02-0032-22
分別表示為後方車輛V jm 的速度在車道l+1與前車V n 在車道l，後方車輛的速度V m 在車道l+1由相對速度方程式
Figure 111126267-A0305-02-0032-25
目標車輛V n 於車道l決定。 The driving threat analysis control method based on the driving state in the advanced driving assistance system as described in claim 6, wherein the optimal dynamic thresholds α and β of lane departure warning/lane departure assistance are as follows:
Figure 111126267-A0305-02-0031-19
 in,
Figure 111126267-A0305-02-0032-20
Represents the adjustment distance,
Figure 111126267-A0305-02-0032-23
Depend on
Figure 111126267-A0305-02-0032-24
Calculated,
Figure 111126267-A0305-02-0032-21
and
Figure 111126267-A0305-02-0032-22
Expressed respectively as the speed of the rear vehicle V jm in lane l+1 and the front vehicle V n in lane l, the speed of the rear vehicle V m in lane l+1 is given by the relative speed equation
Figure 111126267-A0305-02-0032-25
The target vehicle V n is determined on the lane l.
TW111126267A 2022-07-13 2022-07-13 Driving threat analysis and control system based on driving state of advanced driver assist system and method thereof TWI812344B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
TW111126267A TWI812344B (en) 2022-07-13 2022-07-13 Driving threat analysis and control system based on driving state of advanced driver assist system and method thereof

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
TW111126267A TWI812344B (en) 2022-07-13 2022-07-13 Driving threat analysis and control system based on driving state of advanced driver assist system and method thereof

Publications (2)

Publication Number Publication Date
TWI812344B true TWI812344B (en) 2023-08-11
TW202404389A TW202404389A (en) 2024-01-16

Family

ID=88585824

Family Applications (1)

Application Number Title Priority Date Filing Date
TW111126267A TWI812344B (en) 2022-07-13 2022-07-13 Driving threat analysis and control system based on driving state of advanced driver assist system and method thereof

Country Status (1)

Country Link
TW (1) TWI812344B (en)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150057907A1 (en) * 2013-08-22 2015-02-26 Honda Research Institute Europe Gmbh Consistent behavior generation of a predictive advanced driver assistant system
TW201544377A (en) * 2014-05-27 2015-12-01 Univ Nat Yunlin Sci & Tech Regional planning algorithms and trajectory optimization avoidance system of automated vehicle domain-wide risk analysis
TW201830345A (en) * 2017-02-13 2018-08-16 國立臺東大學 Vehicle condition notification system, vehicle condition notification method and cloud server
KR20200130881A (en) * 2019-04-25 2020-11-23 한국전자통신연구원 Method and apparatus for setting the distance between vehicles according to the situation of the platooning driving
US20220198936A1 (en) * 2020-12-22 2022-06-23 Locomation, Inc. Shared control for vehicles travelling in formation

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150057907A1 (en) * 2013-08-22 2015-02-26 Honda Research Institute Europe Gmbh Consistent behavior generation of a predictive advanced driver assistant system
TW201544377A (en) * 2014-05-27 2015-12-01 Univ Nat Yunlin Sci & Tech Regional planning algorithms and trajectory optimization avoidance system of automated vehicle domain-wide risk analysis
TW201830345A (en) * 2017-02-13 2018-08-16 國立臺東大學 Vehicle condition notification system, vehicle condition notification method and cloud server
KR20200130881A (en) * 2019-04-25 2020-11-23 한국전자통신연구원 Method and apparatus for setting the distance between vehicles according to the situation of the platooning driving
US20220198936A1 (en) * 2020-12-22 2022-06-23 Locomation, Inc. Shared control for vehicles travelling in formation

Also Published As

Publication number Publication date
TW202404389A (en) 2024-01-16

Similar Documents

Publication Publication Date Title
US10203699B1 (en) Selective remote control of ADAS functionality of vehicle
US11218850B2 (en) Adaptive early warning method based on vehicle-to-everything and apparatus thereof
WO2019085904A1 (en) Cellular network-based auxiliary driving method and traffic control unit
US11468774B2 (en) Systems and methods for cooperative ramp merge
JP4905034B2 (en) Travel control device and travel control method
US9280899B2 (en) Dynamic safety shields for situation assessment and decision making in collision avoidance tasks
US20200290638A1 (en) Method and system for vehicular communication and safety monitoring of driving environment to provide early warnings in real-time
EP3196089A1 (en) Vehicle safety assist system, vehicle comprising a vehicle safety assist system and a method for providing driver warning or performing autonomous braking
US20210197827A1 (en) Method for coordinating a vehicle group, evaluation unit, vehicle and vehicle group
WO2023142431A1 (en) Decision control method for v2x-based adas in front vehicle shielding scene
JP2019156270A (en) Vehicle controller, vehicle control method and program
US11679762B2 (en) Active rear collision avoidance apparatus and method
CN111081009A (en) Vehicle formation driving system based on Internet of vehicles and control method
US20210197826A1 (en) Method for coordinating a vehicle group, evaluation unit, vehicle and vehicle group
JP2019156269A (en) Vehicle controller, vehicle control method and program
WO2021051959A1 (en) Vehicle control method, device, controller and intelligent vehicle
CN116552559A (en) System and method for detecting abnormal behavior based on fused data in automatic driving system
JP6413636B2 (en) Travel control device
KR20210077052A (en) System and method for controlling operation of autonomous vehicle
Lombard et al. On the cooperative automatic lane change: Speed synchronization and automatic “courtesy”
JP6394152B2 (en) Vehicle control device
TWI812344B (en) Driving threat analysis and control system based on driving state of advanced driver assist system and method thereof
KR20210020500A (en) Apparatus for Controlling Remote Vehicles According to Priority and Method Thereof
CN113548049B (en) Intelligent vehicle driving behavior decision method and system based on finite-state machine
WO2021169248A1 (en) Method and device for controlling vehicle