A vehicle guiding method based on ACC and green wave traffic

Abstract

The application relates to the technical field of automatic driving, in particular to a vehicle guiding method based on ACC and green wave traffic, which comprises the following steps: judging whether a vehicle enters a green wave traffic scene or not; guiding vehicle speed calculation according to the traffic light information sent by a road test RSU of the green wave traffic; judging whether there is an obstacle vehicle in front; and making decisions of emergency braking, cruising, early warning and gentle braking; through the vehicle guiding method, single vehicle sensing and V2X sensing are fused, the monitoring distance and area are increased relative to the single vehicle ACC function, the change state of the traffic light at the front intersection can be perceived earlier, the perception accuracy is improved, the missing judgment rate and the misjudgment rate are reduced, and the passing efficiency is improved and the traffic safety is ensured through more kinds of V2X sensing information.

Description

Technical Field

[0001] This invention relates to the field of autonomous driving technology, and specifically to a vehicle guidance method based on ACC and green wave traffic. Background Technology

[0002] Safe driving is the primary and essential need for car users. During driving, vehicle collisions are a major cause of traffic accidents. Although existing technologies attempt to address this issue, such as enabling adaptive cruise control (ACC) in a single vehicle to follow the car in front, they cannot promptly integrate real-time intersection information with vehicle-side data. This results in the inability to obtain accurate real-time status information, or the acquisition of incorrect status information, leading to lower traffic efficiency.

[0003] Achieving intelligent driving faces numerous core technological bottlenecks, with environmental perception technology being the most critical and a major constraint on the practical application of intelligent driving systems. In the field of intelligent connected vehicles, the integration of vehicles, roads, and smart city networks is a current cross-industry development trend. The development and maturity of cloud platform technologies combining "intelligence," "connectivity," and "big data" are the technological foundation and guarantee for realizing "intelligent vehicles+".

[0004] Intelligent driving technology is one of the core technology areas of intelligent connected vehicles. Among these, environmental perception and control decision-making are the core technological bottlenecks of intelligent driving systems. Currently, in the field of intelligent driving technology, the system's environmental perception capability is far from mature, representing a bottleneck within a bottleneck and a key constraint on achieving intelligent driving. Single-vehicle perception (onboard sensors) and vehicle-to-everything (V2X) communication each have their limitations; only by combining the two can breakthroughs and leaps in intelligent perception technology be achieved. This represents the most feasible system solution, technical route, and direction for intelligent driving at present. In other words, realizing the environmental perception capability that empowers intelligent driving requires the integration of onboard sensors and V2X information technology, thereby greatly enhancing the vehicle's perception capabilities and ultimately significantly improving the functionality, performance, and safety reliability of intelligent driving. Simultaneously, the widespread application of V2X can significantly reduce the cost of single-vehicle intelligent perception.

[0005] Developing intelligent connected vehicles based on vehicle-to-everything (V2X) technology to achieve intelligent driving technology and solve the problems of extremely complex and ever-changing scenarios is a long road and a long process. Although achieving fully autonomous driving is the development direction of intelligent connected vehicle technology, this is a long-term goal, and widespread commercial application still has a long way to go. Market demand is the decisive factor driving technological progress and implementation. Recently, the industry has begun to reach a consensus that using V2X technology to solve problems such as driving safety in key dangerous scenarios, traffic congestion, and improving traffic efficiency is the most important market demand and the biggest pain point in safe driving in transportation. This is a problem that needs to be gradually solved over the next few decades. In other words, solving driving safety problems in key dangerous scenarios is the most critical goal at present, and promoting the industrialization of the technology is also essential.

[0006] Green wave traffic is a relatively new transportation mode that has emerged in recent years. A "green wave" system involves installing a series of automatically controlled, interconnected traffic signals at intersections. These signals, equipped with roadside units (RSUs) that display vehicle speeds, ensure that vehicles maintain or exceed these speeds, allowing traffic on the main road to reach each intersection sequentially and encounter a green light. This "green wave" system reduces vehicle stops at intersections, improving average travel speed and traffic capacity. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to provide a vehicle guidance method based on ACC and green wave traffic.

[0008] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0009] A vehicle guidance method based on ACC and green wave traffic, including

[0010] Scene recognition determines whether the vehicle has entered the green wave traffic scene; if so, continue; otherwise, end.

[0011] The system makes decisions based on traffic light information from the roadside RSU (Roadside Unit) of the green wave traffic system. It calculates the guide speed and determines whether there are obstructing vehicles ahead. If not, it cruises at the guide speed. If so, it determines whether the guide speed is greater than the speed of the vehicle in front. If so, it follows and determines whether the relative distance is less than the AEB (Autonomous Emergency Braking) threshold. If so, it performs emergency braking; otherwise, it continues to follow. If the guide speed is less than or equal to the speed of the vehicle in front, it cruises at the guide speed. After cruises at the guide speed, it determines whether the distance to the stop line is less than the mild braking threshold. If not, it continues to drive normally. If so, it determines whether the current light is red. If not, it continues to drive normally; if so, it issues a red light warning and performs mild braking.

[0012] The vehicle takes corresponding actions based on the decisions made.

[0013] Preferably, in scene recognition, it is determined whether the vehicle has entered the green wave traffic scene; if so, manual confirmation is required before continuing.

[0014] Preferably, the cruise includes ACC adaptive cruise control;

[0015] The ACC adaptive cruise control uses the LQR model algorithm, with the desired distance d from the vehicle in front. des The formula for calculating d is: des =v e t h +d0;

[0016] Wherein, the v e Vehicle current speed, t h d0 represents the headway of the vehicle and indicates the safe distance threshold.

[0017] The formula for calculating the distance error Δd is: Δd = dd des ;

[0018] Wherein, d is the actual distance to the vehicle in front;

[0019] The formula for calculating relative velocity Δv is: Δv = v p -v e ;

[0020] Wherein, the v p The speed of the vehicle in front;

[0021] The actual acceleration of the vehicle, a e The first-order inertial element between the desired acceleration and the desired acceleration is represented as:

[0022]

[0023] Where Ts is a constant in the first-order inertial element, and a des For the desired acceleration;

[0024] Let the desired acceleration a des For control parameters, distance error Δd, relative velocity Δv, and vehicle acceleration a are used. e For state variables, solve the simultaneous formulas.

[0025]

[0026] Therefore, the state equation can be obtained as follows:

[0027]

[0028] make

[0029]

[0030] The state equation can then be rewritten as:

[0031]

[0032] Based on the principle of state feedback, a suitable state feedback control law is designed, and the cost function of the system is defined as follows:

[0033]

[0034] x is a state variable. u represents the control variable;

[0035] The Q matrix is ​​defined as follows:

[0036]

[0037] Let matrix R be: R = 1;

[0038] Q is the weight matrix of the performance index function for the state variables, and it is a diagonal matrix; R is the weight matrix of the control variables, and it is a diagonal matrix.

[0039] Preferably, when testing the values ​​of Q and R for the first time;

[0040] K was calculated using MATLAB's lqr library;

[0041] If the discrete LQR algorithm is used for solving the problem, the continuous-time system state equations need to be discretized. A method for discretizing the continuous-time system state equations using an approximate discretization approach is as follows:

[0042] x(k+1)=Gx(k)+Hu(k)

[0043] Where G = I + AT s H = BT s I is the identity matrix, T s It is the sampling time;

[0044] K was calculated using MATLAB's dlqr library.

[0045] Preferably, the cruise includes constant speed cruise based on the guide vehicle speed;

[0046] The cruise control system includes calculating the vehicle's desired acceleration based on the deviation between the driver's set speed and the actual vehicle speed, and then accelerating or decelerating the vehicle according to the desired acceleration.

[0047] Preferably,

[0048] The PID control algorithm is used to realize the cruise control, which includes calculating the vehicle's desired acceleration based on the deviation between the driver's set speed and the actual vehicle speed, and the vehicle accelerating or decelerating according to the desired acceleration.

[0049]

[0050] Where u(t) is the desired acceleration; e(t) is the vehicle speed; K P T represents the proportional gain of the controller; i T represents the integral time of the controller; d This represents the derivative time of the controller.

[0051] Preferably, the guide vehicle speed calculation includes

[0052] When the current light is green, the green wave guide speed at that moment is:

[0053] Where S is the distance between the vehicle and the stop line, and T is the remaining time of the green light;

[0054] When the current light is green, the green wave guide speed at that moment is:

[0055] Where S is the distance between the vehicle and the stop line, T is the remaining time of the yellow light, and X is the remaining time of the red light;

[0056] When the current light is green, the green wave guide speed at that moment is:

[0057] Where S is the distance between the vehicle and the stop line, and T is the remaining time of the red light;

[0058] If the calculated guide speed is greater than the road speed limit and the vehicle's maximum speed, the vehicle needs to brake and stop at the stop line; if the maximum guide speed is very low, the vehicle should maintain its original speed and then brake and stop.

[0059] Preferably, the distance s traveled by the gentle braking to the stop line is... br The calculation formula is:

[0060] Where v is the vehicle speed and a is the deceleration;

[0061] The distance s relative to the stop line when starting braking and decelerating mbr The calculation formula is s mbr =s br +v rel *t RBR ;

[0062] Among them, v rel The relative speed of the vehicle to the target vehicle or the target stop line, t RBR Braking response time;

[0063] The warning indicates that the vehicle can safely stop under gentle braking conditions, and the time it takes for the vehicle to decelerate to a complete stop under the warning condition.

[0064] Among them, v sv For vehicle speed, a sv For deceleration;

[0065] The distance d that the vehicle travels to a complete stop stop The calculation formula is:

[0066]

[0067] Among them, t SVD This refers to the driver's reaction time.

[0068] Gentle braking at red lights includes

[0069] The vehicle comes to a stop at the parking line with gentle braking. The formula for calculating the distance S0 traveled during this process is as follows:

[0070]

[0071] Where v0 is the initial vehicle speed and a1 is the deceleration rate during gentle braking.

[0072] Preferably, if another vehicle cuts in during the decision-making process, resulting in an emergency braking situation, the formula for calculating the vehicle's travel distance S2 during this process is as follows:

[0073]

[0074] Where a2 is the emergency braking deceleration.

[0075] Preferably, the process for determining when other vehicles cut in is as follows: After GNN multi-target matching and tracking, it is necessary to filter the CIPV ahead. The filtering strategy is the nearest dangerous vehicle to identify vehicles cutting in from the front.

[0076] The beneficial effects of this invention are as follows: By integrating single-vehicle perception and V2X perception, the vehicle guidance method of this application increases the monitoring distance and area compared to single-vehicle ACC function, enabling earlier detection of traffic light changes at intersections ahead; it improves the accuracy and precision of perception, reducing the rate of missed judgments and false judgments; and by using more types of information perceived through V2X, it improves traffic efficiency and ensures traffic safety. Attached Figure Description

[0077] Figure 1 This is an application scenario diagram of a vehicle guidance method based on ACC and green wave traffic, which is a specific embodiment of the present invention.

[0078] Figure 2The speed limit provisions in the "Regulations for the Implementation of the Road Traffic Safety Law of the People's Republic of China";

[0079] Figure 3 This is a schematic diagram of the ROI of a green wave traffic guidance scenario for a vehicle guidance method based on ACC and green wave traffic, which is a specific embodiment of the present invention.

[0080] Figure 4 The flowchart below shows a specific embodiment of the present invention: a regional scope 1 for a vehicle guidance method based on ACC and green wave traffic.

[0081] Figure 5 The flowchart of the area range perception fusion algorithm of a vehicle guidance method based on ACC and green wave traffic according to a specific embodiment of the present invention is shown.

[0082] Figure 6 This is a schematic diagram of the detectable area of ​​a camera, forward-facing millimeter-wave radar, and roadside unit in a vehicle guidance method based on ACC and green wave traffic according to a specific embodiment of the present invention.

[0083] Figure 7 This is a schematic diagram of the decision-making logic for a green wave traffic scenario of a vehicle guidance method based on ACC and green wave traffic, which is a specific embodiment of the present invention.

[0084] Figure 8 This is a schematic diagram of the parking line distance in a green wave traffic scenario, illustrating a specific embodiment of the vehicle guidance method based on ACC and green wave traffic according to a specific implementation of the present invention.

[0085] Figure 9 This is a schematic diagram of traffic light phase information for a vehicle guidance method based on ACC and green wave traffic according to a specific embodiment of the present invention (from left to right: green light, yellow light, red light, green light, yellow light).

[0086] Figure 10 The diagram below shows an adaptive following decision-making process for a vehicle guidance method based on ACC and green wave traffic, which is a specific embodiment of the present invention.

[0087] Figure 11 This is a schematic diagram illustrating an adaptive following method for vehicle guidance based on ACC and green wave traffic, according to a specific embodiment of the present invention.

[0088] Figure 12 This is a schematic diagram of a PID control model for cruise control of a vehicle guidance method based on ACC and green wave traffic, which is a specific embodiment of the present invention. Detailed Implementation

[0089] To explain in detail the technical content, objectives, and effects of the present invention, the following description is provided in conjunction with the embodiments and accompanying drawings.

[0090] Example

[0091] A vehicle guidance method based on ACC and green wave traffic, with application scenarios such as... Figure 1 This is a schematic diagram of a green wave guidance scenario;

[0092] driving speed

[0093] The driving speeds used in this embodiment refer to the speed limits stipulated in the "Regulations for the Implementation of the Road Traffic Safety Law of the People's Republic of China". Figure 2 As shown, it can be modified as needed; it is used only as an example in this application.

[0094] According to the Road Traffic Safety Law, the speed limit on urban roads is 30-50-70 km / h. Some urban public transport operators may have internal speed limit regulations, but there are no publicly available, unified standards. Common speed limits for buses are generally 40 km / h on urban roads and 60 km / h on urban expressways.

[0095] To protect the legitimate rights and interests of citizens, motor vehicle drivers must comply with speed limits when passing through intersections. For roads with only one lane for motor vehicles in the same direction, the speed limit in urban areas is 50 km / h. Therefore, based on the above speed limit regulations and simplified calculations, the maximum guiding speed for passing through intersections is tentatively set at 50 km / h.

[0096] Braking response time

[0097] According to Clause 7.14.1.2 of GB7258, "the time from pressing the brake pedal to the response time of the most unfavorable brake chamber should be less than or equal to 0.6s". To ensure safety to the greatest extent, the relevant design in this application assumes a braking response time of 0.6s, which can be modified as needed. This application is only used as an example.

[0098] The overall conjugate energy of the scene

[0099] The ACC green wave passage function can be set to either on or off, and its on / off state can be controlled by an external switch or via the HMI touchscreen. When the switch is in the on state, the function designed by this system is activated.

[0100] Ideally, the controlled vehicle approaches the traffic light-controlled intersection from a distance and enters the area that the Roadside Utility Unit (RSU) can broadcast. The roadside communication equipment transmits traffic light data and real-time status information. The system on the vehicle can parse the real-time status and countdown information of the traffic lights, and calculate the guiding speed for passing through the intersection without stopping during the current or next green light by combining the positioning and driving status information. If the driver has already pressed the "Longitudinal Control" button, the system will automatically control the vehicle to cruise at the guiding speed. When there is an obstacle vehicle ahead or an obstacle vehicle enters the lane, the system obtains information such as the position, speed, and acceleration of the obstacle vehicle ahead through vehicle-side fusion sensing, and follows the vehicle ahead for adaptive cruise control. When the intersection is red and the guiding speed is no longer sufficient to pass through, the system automatically controls the vehicle to brake gently and stop before the stop line. When the green light turns on again, the vehicle then proceeds through the intersection.

[0101] To achieve the overall functionality of the "ACC green wave passage" scenario, the system needs to have the following features:

[0102] 1. Scene recognition

[0103] The scene recognition in this scenario is divided into two parts. First, it identifies whether the vehicle has entered the scene. Second, it reminds the driver whether to press the switch to activate the green wave speed guidance for this scene.

[0104] For ACC green wave traffic scenarios, vehicles obtain the traffic light information ahead from the road attribute information in the RSI information sent by the RSU from the OBU. When the traffic light information is parsed, it is determined that the vehicle is in an "ACC green wave vehicle guidance scenario".

[0105] The triggering of this scenario differs from other scenarios. In this scenario, the system will only execute the green wave speed and ACC following decision after the driver presses the "Green Wave Traffic Mode" button.

[0106] 2. Fusion Perception - Detection of Vehicles Ahead Obstacles

[0107] It can use the fusion of onboard radar and camera sensors to detect obstacle vehicles ahead and obtain information such as their position, speed and acceleration.

[0108] 3. Decision-making

[0109] Based on the current status and countdown information of traffic lights issued by the road test RSU, combined with the vehicle's current speed and distance from the stop line, the guide speed is calculated. Then, combined with the vehicle-side fusion perception of obstacles ahead, the system determines whether to make decisions such as green wave passage, gentle braking, or emergency braking, and determines the driver takeover and corresponding exit mechanisms.

[0110] 4. Control Execution

[0111] Control vehicle actions based on decision results.

[0112] Perception system part

[0113] ROI region analysis

[0114] The ROI (Region of Interest) analysis only considers the scenario of a single intersection. When multiple intersections with green wave traffic are involved, this can be extended as a reference. In this scenario, the ROI analysis considers two aspects: guiding speed and braking distance. The calculation of guiding speed mainly relies on traffic light status information sent by the roadside RSU (Roadside Unit). Currently, roadside communication coverage reaches 200-300 meters. The braking distance considerations are mainly to prevent collisions with obstructing vehicles ahead and to ensure a gentle stop at the stop line when the light is red.

[0115] (1) Gently stop at a red light

[0116] The system's general coverage distance should meet the following requirements: When a vehicle is traveling at 50 km / h, upon receiving a red traffic light ahead, the system should automatically control the vehicle to gently decelerate and stop before the stop line. The distance the vehicle travels during this process is:

[0117]

[0118] Where v0 is the initial vehicle speed, t SVD , t RBR These represent the driver's reaction time and the braking system's response time, respectively, with a1 being the deceleration rate during gentle braking.

[0119] Take v0 = 50 km / h, t SVD =0.5s,t RBR =0.6s, a1=0.2g, so S0=63.4m at this time.

[0120] When there are no obstructing vehicles ahead, at this distance, the system can bring the vehicle to a stop before the stop line by gently braking.

[0121] (2) Emergency braking caused by obstruction vehicle cutting in.

[0122] The system's maximum coverage distance should meet the following requirements: When the vehicle is traveling at its maximum speed, after the system detects an obstacle vehicle ahead, it automatically controls the vehicle to decelerate to a stop at its maximum speed. The distance the vehicle travels during this process is:

[0123]

[0124] Where v0 is the initial vehicle speed, t RBR a1 represents the braking system response time, and a2 represents the emergency braking deceleration.

[0125] Take v0 = 50 km / h, t RBR =0.6s, a2=0.5g, so S2=34.4m at this time.

[0126] Note: When the vehicle braking system has a pre-fill function, that is, when the braking pressure is established in advance, the effect of t2 can be eliminated, and the braking distance can be further shortened.

[0127] The lateral coverage area mainly targets the 3.6m wide lane area, detecting and identifying obstructed vehicles and their motion parameters within the area.

[0128] Overall coverage area such as Figure 3 As shown, the area between the two black blocks in S0 (between the V2X communication area and the S2 area) is the gentle parking area under red light conditions, and S2 is the emergency braking area where the obstacle vehicle cuts in.

[0129] Analysis of Fusion Perception Algorithm

[0130] 1. Sensors from different regions of the ROI are involved in the analysis.

[0131] Gentle braking area when the light is on.

[0132] Area range: This area is 300 meters. Within this range, the information of concern is the traffic light phase information and countdown information. Vehicles need to recognize the phase and countdown to calculate the guide speed.

[0133] Sensors involved: At this stage, only information transmitted from the roadside signal controller to the RSU is needed, which is then received and processed by the vehicle-mounted OBU.

[0134] When there is an obstacle vehicle ahead, the area

[0135] Area range: Within 110 meters of the single vehicle's sensing range.

[0136] Sensors involved: vehicle-mounted forward-facing camera, forward-facing millimeter-wave radar.

[0137] 2. Perceptual fusion algorithm

[0138] Area 1: This area does not have a specific algorithm for perception fusion; the vehicle only needs to receive signals from the roadside traffic signal controller. The flowchart is as follows. Figure 4 As shown.

[0139] Region Scope 2: This region is primarily used to detect suddenly appearing obstacle vehicles ahead, acquiring their position, speed, acceleration, and other information as accurately as possible to achieve more accurate speed tracking. Unlike other scenarios, after GNN multi-object matching tracking, it is necessary to filter the CIPVs ahead using the nearest dangerous vehicle selection strategy to identify vehicles cutting in front. Through identification and tracking, the information is transmitted to the decision module for further tracking. The specific flowchart is as follows... Figure 5 As shown.

[0140] Sensor combination selection

[0141] Commonly used vehicle-mounted cameras and forward-facing millimeter-wave radars can detect areas such as... Figure 6 As shown in the figure, the distances at which the vehicle-mounted camera can achieve full coverage of the lane are 68m and 14m, respectively. This distance falls within the coverage range of approximately 70m for typical cameras detecting vehicles.

[0142] For the design of the fusion perception algorithm in the green wave traffic scenario, the required perception signals and their sources are summarized as follows:

[0143] Table 1. Sensor Selection Requirements for the Algorithm Module

[0144]

[0145]

[0146] Final sensor combination selection

[0147] Based on the sensor selection requirements of each part of this application, the required sensors are summarized in the table below:

[0148] Table 2 Sensor List

[0149]

[0150] Overall parameter index analysis

[0151] This section analyzes sensor selection based on factors such as ROI and algorithms. It then analyzes and demonstrates the performance of each indicator for the green wave traffic scenario and existing sensors.

[0152] Table 3. Performance Requirements for the Perception Fusion Module in Green Wave Scenarios

[0153]

[0154]

[0155] The existing sensor and fusion performance indicators, including the vehicle camera (Magic Vision Camera MIT500) and vehicle radar (Continental 408 millimeter-wave radar), are as follows:

[0156] Table 4. Specifications of the Magic Vision Camera

[0157]

[0158]

[0159] Table 5 408 Radar Specifications

[0160]

[0161] Table 6 Performance Indicators After Fusion

[0162]

[0163]

[0164] Table 7. Results of Indicator Analysis

[0165]

[0166]

[0167] Decision control analysis

[0168] 1. Scene Judgment

[0169] For ACC green wave traffic scenarios, vehicles obtain the traffic light information ahead from the road attribute information in the RSI information sent by the RSU from the OBU. When the traffic light information is parsed, it is determined that the vehicle is in an "ACC green wave vehicle guidance scenario".

[0170] 2. Decision-making logic for green wave traffic scenarios

[0171] like Figure 7 As shown, the green wave traffic decision-making process mainly includes two parts: one is to execute the green wave adaptive cruise control function according to the green wave speed when the light is green, and the other is to gently brake to a stop before the stop line when the light is red. Emergency braking is a standard function of AEB and remains in the active state.

[0172] First, the guiding speed is calculated based on the current status of the traffic lights, the countdown information, the vehicle's speed, and the distance between the vehicle and the stop line.

[0173] When there are no obstructing vehicles ahead, or when obstructing vehicles exist but their speed exceeds the guided speed of the controlled vehicle, guided speed cruise will be initiated. In this state, there are two scenarios: either the vehicle can pass through the intersection at the green wave guided speed, or it cannot pass through the intersection and needs to brake gently to the stop line. When the distance between the vehicle and the stop line is less than the gentle braking trigger distance, and the current light is red, the controlled vehicle will receive a red light warning signal and begin to decelerate and stop with a small amount of deceleration. When the distance to the stop line is greater than the gentle braking distance threshold, or the current light is green, the controlled vehicle will proceed at the green wave guided speed.

[0174] When there is an obstacle vehicle ahead, and the speed of the obstacle vehicle is less than the current guiding speed of the controlled vehicle, the vehicle ahead will follow. If the relative distance is less than the AEB threshold, emergency braking will be performed.

[0175] ①The logic for calculating the guided vehicle speed is as follows:

[0176] like Figure 8 The diagram shows a green wave traffic scenario. Assume that the distance between the controlled vehicle SV and the stop line of its lane before the traffic light is S.

[0177] like Figure 9 Assume the traffic light phases for the current lane are two-phase, meaning that when the green light is on, you can go straight, turn left, or turn right. The phase sequence of the traffic lights is 30 seconds for green, 5 seconds for yellow, and 20 seconds for red, and this sequence is executed cyclically.

[0178] When the controlled vehicle SV travels to a distance of 300 meters from the intersection, it can receive traffic light status information sent by the roadside unit (RSU).

[0179] If the current light is green, then the green wave guidance speed at that moment is:

[0180]

[0181] Where T represents the remaining time of the green light.

[0182] When the driver presses the ACC setting switch, the controlled vehicle SV will cruise through the intersection at the guided speed.

[0183] If the current light is yellow, then the green wave guide speed at that moment is:

[0184]

[0185] Where T represents the remaining time of the yellow light.

[0186] Similarly, if the current light is red, the green wave guides the vehicle speed as follows:

[0187]

[0188] Where T represents the remaining time of the red light.

[0189] If the calculated guide speed is too high, the road speed limit and the vehicle's maximum speed must be taken into account. In this case, the vehicle cannot proceed through the intersection and must still brake and stop. If the maximum guide speed is too low, traffic efficiency must be considered. In this case, the vehicle cannot proceed at the guide speed and must still proceed at the original speed and then brake and stop.

[0190] To address the aforementioned speed limits, a speed limit range is initially set, tentatively 30-50 km / h. If the algorithm calculates a speed limit within this range, the vehicle will travel at that speed; otherwise, the vehicle will continue at its current speed and slow down to a stop at a red light.

[0191] ②The logic behind the gentle braking decision is as follows:

[0192] Based on the current position of the vehicle and the position of the stop line, when the traffic light is red, decelerate at a small rate and stop before the stop line.

[0193] Distance formula:

[0194] Among them, s br (br, braking) The distance traveled to the stop line by gently braking and decelerating, a = 0.2g (the specific deceleration value needs to be calculated and optimized in real time based on the current relative speed and relative distance).

[0195] Distance s relative to the stop line during start-up and gentle deceleration mbr (mildbraking) Braking response time t RBR (responsebraking, braking response time)

[0196] s mbr =s br +v rel *t RBR

[0197] Warning

[0198] Method for setting the warning safety distance: The test vehicle can decelerate gently (e.g., |a SV Safe parking can be achieved under the condition of |<0.2g (this value can be adjusted and optimized as needed).

[0199] Estimation of SV motion of the test vehicle (control target under mild braking conditions), deceleration time t to stop. stop :

[0200]

[0201] (where, for example, a) SV =0.2g or other appropriate value)

[0202] The distance d that SV travels to a complete stop. stop :

[0203]

[0204] Gently stop at a red light

[0205] The system's general coverage distance should meet the following requirements: When a vehicle is traveling at 50 km / h, upon receiving a red traffic light ahead, the system should automatically control the vehicle to gently decelerate and stop before the stop line. The distance the vehicle travels during this process is:

[0206]

[0207] Where v0 is the initial vehicle speed, t SVD , t RBR These represent the driver's reaction time and the braking system's response time, respectively, with a1 being the deceleration rate during gentle braking.

[0208] Take v0 = 50 km / h, t SVD =0.5s,t RBR =0.6s, a1=0.2g, so S0=63.4m at this time.

[0209] When there are no obstructing vehicles ahead, at this distance, the system can bring the vehicle to a stop before the stop line by gently braking.

[0210] Obstacle vehicle cuts in, causing emergency braking

[0211] The system's maximum coverage distance should meet the following requirements: When the vehicle is traveling at its maximum speed, after the system detects an obstacle vehicle ahead, it automatically controls the vehicle to decelerate to a stop at its maximum speed. The distance the vehicle travels during this process is:

[0212]

[0213] Where v0 is the initial vehicle speed, t RBR a1 represents the braking system response time, and a2 represents the emergency braking deceleration.

[0214] Take v0 = 50 km / h, t RBR =0.6s, a2=0.5g, so S2=34.4m at this time.

[0215] Note: When the vehicle braking system has a pre-fill function, that is, when the braking pressure is established in advance, the effect of t2 can be eliminated, and the braking distance can be further shortened.

[0216] 3. Driver takeover and exit mechanism

[0217] To enter the "ACC Green Wave Speed ​​Guidance" mode, the driver needs to press the "Longitudinal Control" button. The controlled vehicle calculates the guidance speed based on the current speed and traffic light status, and then adaptively cruises at that guidance speed or the speed of the vehicle in front. When the driver presses the brake pedal and the pedal travel exceeds the set threshold (which needs to be calibrated), the "ACC Green Wave Speed ​​Guidance" mode is completely exited. To re-enter it, the "Longitudinal Control" button must be pressed again.

[0218] Control Module Analysis and Design

[0219] Green wave speed control principle diagram as follows Figure 10 As shown. When there are no obstructing vehicles, cruise at the calculated guide speed; when there are obstructing vehicles, adaptive cruise control follows the vehicle in front.

[0220] Adaptive cruise control

[0221] The ACC adaptive cruise control algorithm uses the LQR model algorithm. An ACC following diagram is shown below. Figure 11 As shown.

[0222] Formula for calculating expected distance:

[0223] d des =v e t h +d0

[0224] d des t represents the ideal following distance; h This indicates the headway; d0 represents the safety distance threshold, typically one vehicle length, which can be adjusted based on the actual vehicle length; v e Indicates the current vehicle speed.

[0225] Distance error calculation formula:

[0226] Δd=dd des (1)

[0227] d represents the actual distance between the two vehicles as measured by the sensor.

[0228] Formula for calculating relative velocity:

[0229] Δv=v p -v e (2)

[0230] v p This indicates the speed of the vehicle following ahead.

[0231] The relationship between the actual acceleration and the expected acceleration of the vehicle can be represented as a first-order inertial element:

[0232]

[0233] Let the desired acceleration a des As control variables, distance error Δd, relative velocity Δv, and vehicle acceleration a are state variables. Equations (1), (2), and (3) are combined:

[0234]

[0235] Therefore, the state equations can be obtained as follows: (The state equations are a system of first-order differential equations consisting of the system's state variables)

[0236]

[0237] make

[0238]

[0239] The state equation can then be rewritten as:

[0240]

[0241] Based on the principle of state feedback, a suitable state feedback control law is designed to enable the vehicle to track the target vehicle with minimal speed error, distance error, and acceleration, while also minimizing control input to reduce fuel consumption and unnecessary braking. Therefore, the system cost function is defined as follows:

[0242]

[0243] Where x represents the state variable, u represents the control variable, and T represents the transpose of the matrix.

[0244] Let the Q matrix be:

[0245]

[0246] Let the R matrix be:

[0247] R = 1

[0248] Q is the weight matrix of the performance index function for the state variables. It is a diagonal matrix; the larger the element, the more important the variable is in the performance function. Minimizing the performance function means that the constraint requirements for this state are high. R is the weight matrix of the control variables. It is also a diagonal matrix; similarly, the larger the corresponding element, the stronger the control constraint.

[0249] Then, use MATLAB's built-in lqr library to calculate K. The command is:

[0250]

[0251] The initial test values ​​for Q and R are as described above, and will be adjusted based on actual testing. If the discrete LQR algorithm is used, the continuous-time system state equations need to be discretized.

[0252] Discretization method for continuous-time system state equations using approximate discretization:

[0253] x(k+1)=Gx(k)+Hu(k)

[0254] Where G = I + AT s H = BT s I is the identity matrix, T s This is the sampling time, taken as 0.01s;

[0255] Then, use the dlqr library that comes with MATLAB to calculate K. The command is:

[0256]

[0257] Guided speed cruise

[0258] A PID control algorithm is employed to calculate the vehicle's desired acceleration based on the deviation between the driver's set speed and the actual vehicle speed. The lower-level controller then adjusts the braking pressure and drive torque to achieve the desired acceleration set by the upper-level controller. The control flow is as follows: Figure 12 As shown, a guide speed is set for the controlled vehicle, and the vehicle enters the guide speed cruise mode.

[0259] The mathematical model of a PID controller can be expressed by the following formula:

[0260]

[0261] u(t) represents the output of the controller, which is the desired acceleration.

[0262] e(t) represents the input to the controller, which is the vehicle speed in this case;

[0263] K P The proportional gain representing the controller;

[0264] T i Represents the integral time of the controller;

[0265] T d This represents the derivative time of the controller.

[0266] The tuning of its PID parameters needs to be obtained through simulation testing.

[0267] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A vehicle guidance method based on ACC and green wave traffic, characterized by, The scene recognition judges whether the vehicle enters the green wave traffic scene, if yes, it continues, otherwise, it ends; The decision makes the vehicle cruise according to the guide speed, judges whether there is an obstacle vehicle in front, if no, it cruises according to the guide speed, if yes, it judges whether the guide speed is greater than the speed of the front vehicle, if yes, it follows, and judges whether the relative distance is less than the AEB threshold, if yes, it brakes urgently, if no, it continues to follow; If the guide speed is less than or equal to the speed of the front vehicle, the vehicle cruises according to the guide speed; After cruising according to the guide speed, it judges whether the distance to the stop line is less than the gentle braking threshold, if no, it drives normally, if yes, it judges whether it is a red light, if no, it drives normally, if yes, it issues a red light warning and makes gentle braking; The vehicle makes corresponding actions according to the decisions; The cruise includes ACC adaptive cruise control; The ACC adaptive cruise control adopts an LQR model algorithm, and the expected distance d des The calculation formula is: d des = v e t h + d0; wherein the v e vehicle current speed, t h is the headway, d0 is the safety distance threshold; The calculation formula of the distance error Δd is: Ad = d - d des ; Wherein, the d is the actual distance to the front vehicle; The calculation formula of the relative speed Δv is: Δv = v p -v e ; Wherein, the vp is the driving speed of the front vehicle; The first-order inertia link between the actual acceleration ae of the vehicle and the expected acceleration is expressed as: where Ts is a constant in the first order inertia link, said a des is the desired acceleration; Let the desired acceleration a des is a control variable, distance error Δd, relative speed Δv, self-vehicle acceleration a e is a state variable, the simultaneous equations: Thus, the state equation is obtained as: Let: The state equation can be rewritten as: According to the state feedback principle, a suitable state feedback control law is designed, and the cost function of the system is defined as: Wherein, x represents the state quantity, u represents the control quantity, and T represents the transpose of the matrix; The Q matrix is set as: The R matrix is set as: R=1; Q is the weight matrix of the performance index function for the state quantity, which is a diagonal matrix; R is the weight of the control quantity, which is a diagonal matrix; Wherein, the initial test Q and R values are: K is calculated using the lqr library of matlab; If the discrete LQR algorithm is used for solving, the continuous-time system state equation needs to be discretized; The continuous-time system state equation is discretized using the approximate discretization method: x(k + 1) = Gx(k) + Hu(k) where G = I + AT s , H = BT s , I is the identity matrix, T s is the sampling time; K is calculated using the dlqr library of matlab. 2.The ACC and green wave traffic based vehicle guidance method according to claim 1, wherein, In the scene recognition, it is judged whether the vehicle enters the green wave traffic scene, if yes, it needs to be confirmed by the driver. 3.The ACC and green wave traffic based vehicle guidance method according to claim 1, wherein, The cruise includes constant speed cruise according to the guide speed; The constant speed cruise includes calculating the expected acceleration of the vehicle according to the deviation between the driver-set speed and the actual speed, and the vehicle accelerates or decelerates according to the expected acceleration. 4.The ACC and green wave traffic based vehicle guidance method according to claim 3, wherein, The constant speed cruise includes calculating the expected acceleration of the vehicle according to the deviation between the driver-set speed and the actual speed, and the vehicle accelerates or decelerates according to the expected acceleration by using the PID control algorithm; where u(t) is the desired acceleration; e(t) is the vehicle speed; K P represents the proportional coefficient of the controller; T i represents the integral time of the controller; T d represents the derivative time of the controller. 5.The ACC and green wave traffic based vehicle guidance method according to claim 1, wherein, The guide speed calculation includes that when the current state is green light, the green wave guide speed at this moment is: Wherein, S is the distance of the vehicle to the stop line, and T is the remaining green light time. When the current state is green light, the green wave guide speed at this moment is: Wherein, S is the distance of the vehicle to the stop line, T is the remaining yellow light time, and X is the remaining red light time. When the current state is green light, the green wave guide speed at this moment is: Wherein, S is the distance of the vehicle to the stop line, and T is the remaining red light time. If the calculated guide speed is greater than the road speed limit and the maximum driving speed of the vehicle, the vehicle needs to brake to stop at the stop line. If the maximum guide speed is very small, the vehicle drives at the original speed and then brakes to stop. 6.The ACC and green wave traffic based vehicle guidance method according to claim 1, wherein, The calculation formula of the distance sbr of the gentle braking deceleration to the stop line is: Wherein, v is the vehicle speed, and a is the deceleration; The distance s from the stop line at the start of the braking deceleration mbr The formula for calculating s is: s mbr = s br + v rel * t RBR ; wherein v rel is the relative driving speed of the vehicle to the target vehicle or target stop line, t RBR is the brake response time; The pre-warning is that the vehicle can achieve safe parking under the condition of gentle braking, and the time of the vehicle deceleration to stop under the pre-warning condition is: where v sv is the vehicle speed, a sv is the deceleration; The driving distance d to the vehicle deceleration to the standstill stop The calculation formula is: where t SVD is the driver reaction time; Under the condition of gentle braking at the red light, the vehicle is stopped before the stop line, and the calculation formula of the driving distance S0 of the vehicle in the process is: Wherein, v0 is the initial vehicle speed, and a1 is the gentle braking deceleration. 7.The ACC and green wave traffic based vehicle guidance method according to claim 1, wherein, If other vehicles cut in during the decision process and are determined to be emergency braking, the calculation formula of the driving distance S2 of the vehicle in the process is: Wherein, a2 is the emergency braking deceleration. 8.The ACC and green wave traffic based vehicle guidance method of claim 7, wherein, The determination process of the other vehicles cutting in is: After the GNN multi-target matching tracking, the front CIPV needs to be screened, and the screening strategy is the nearest dangerous vehicle to identify the front inserted vehicle.

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