Urban trunk road green wave vehicle speed guide control method and device

Abstract

The invention provides an urban trunk road green wave vehicle speed guide control method and device, and relates to the technical field of automatic driving, and the method comprises the steps: obtaining the signal lamp information of an intersection in front of a vehicle, and dividing a current driving road section into a plurality of road points based on the current position of the vehicle and the stop line position of the intersection in front; the current driving road section is a driving track between the current position of the vehicle and the stop line; the plurality of road points comprise stop line positions; and judging whether the road point to which the vehicle belongs is updated or not based on a road point triggering mechanism, and triggering green wave vehicle speed guide calculation to obtain the optimal green wave vehicle speed of the current road point under the condition of determining that the road point to which the vehicle belongs is updated. According to the urban trunk road green wave vehicle speed guide control method and device provided by the invention, the longitudinal speed trajectory is efficiently solved by using the Bayesian dynamic programming theory based on the vehicle state information, the signal lamp data and the traffic flow information, the global optimal vehicle speed is issued to the chassis actuator, and a safer, more efficient and more reliable intelligent network connection vehicle driving strategy is realized.

Description

Methods and devices for green wave vehicle speed guidance and control on urban arterial roads Technical Field

[0001] This application relates to the field of autonomous driving technology, and in particular to a method and device for green wave vehicle speed guidance control on urban arterial roads. Background Technology

[0002] Intelligent connected vehicles, integrating technologies such as artificial intelligence, the Internet of Things, and big data, offer advantages across multiple dimensions, including safety, efficiency, user experience, and environmental protection, and can drive profound changes in transportation systems, urban ecosystems, and even industrial structures. Especially in urban multi-signal intersection scenarios with complex information and fluctuating operating conditions, intelligent connected vehicles can receive real-time traffic light SPAT data (signal light phase, light color status, remaining time of the current light, etc.), map information (stop line latitude and longitude, road segment length), and traffic flow information of the current road segment from commercial map providers or roadside traffic control units via the cockpit domain or onboard unit (OBU), combined with the vehicle's location. Autonomous vehicles, building upon existing adaptive cruise control (ACC), have added a green wave predictive cruise control mode, which can further improve traffic efficiency and reduce energy consumption while ensuring vehicle safety.

[0003] However, autonomous vehicles in related technologies rely on perception and communication technologies, which place high demands on the vehicle's computing power and lack a global level of intelligence that examines driving safety from a system perspective. Summary of the Invention

[0004] The purpose of this application is to provide a green wave speed guidance control method and device for urban arterial roads. The method takes vehicle status information, traffic light data and traffic flow information as inputs, and uses Bayesian dynamic programming theory to efficiently solve the longitudinal speed trajectory, so as to issue the global optimal vehicle speed to the chassis actuator, thereby realizing a safer, more efficient and reliable intelligent connected vehicle driving strategy.

[0005] This application provides a method for green wave speed guidance control on urban arterial roads, comprising: acquiring traffic light information of an intersection ahead of the vehicle, and dividing the current driving segment into multiple road points based on the vehicle's current position and the stop line position of the intersection ahead; the current driving segment is the driving trajectory between the vehicle's current position and the stop line; the multiple road points include the stop line position; determining whether the road point to which the vehicle belongs has been updated based on a road point triggering mechanism, and triggering green wave speed guidance calculation when it is determined that the road point to which the vehicle belongs has been updated, to obtain the optimal green wave speed for the current road point; wherein, the road point triggering mechanism is: determining whether the road point to which the vehicle belongs has been updated based on the angle between the vehicle's current position and two adjacent road points among the multiple road points; the green wave speed guidance calculation includes: the travel time interval for the vehicle to reach the intersection ahead, and calculating the passable travel time interval for the current traffic light intersection based on the traffic light information and the travel time interval.

[0006] Optionally, the method for determining whether a vehicle's waypoint has been updated based on the waypoint triggering mechanism includes: determining two waypoints adjacent to the vehicle based on the vehicle's current position, and calculating the angular relationship between the vehicle and the two waypoints; if the angular relationship indicates that the angle between the vehicle and the two waypoints exceeds a preset angle threshold, then the waypoint to which the vehicle belongs has been updated; otherwise, the waypoint to which the vehicle belongs has not been updated.

[0007] Optionally, the step of triggering green wave speed guidance calculation to obtain the optimal green wave speed for the current road point includes: calculating the passage time interval for the vehicle to reach the next intersection based on the first time it takes for the vehicle to accelerate from its initial speed to its maximum speed on the current road segment, and the second time it takes for the vehicle to decelerate from its initial speed to its minimum speed on the current road segment; after determining the green light time window based on the traffic light information, intersecting the passage time interval with the green light time window to obtain multiple passable time intervals, and determining the first time interval among the multiple passable time intervals as the passable time interval; based on the passable time interval and the correlation between time and speed, calculating the upper limit and lower limit of the target speed for the current road segment, and calculating the optimal green wave speed for the current road point based on the upper limit and lower limit of the target speed.

[0008] Optionally, calculating the upper limit and lower limit of the target speed for the current travel segment based on the travel time interval and the correlation between time and speed includes: calculating the ratio of the current vehicle's distance from the stop line to the minimum travel time to obtain a first ratio, and calculating the ratio of the current vehicle's distance from the stop line to the maximum travel time to obtain a second ratio; determining the smaller value between the first ratio and the upper limit of the vehicle's travel speed as the upper limit of the target speed, and determining the smaller value between the second ratio and the lower limit of the vehicle's travel speed as the lower limit of the target speed.

[0009] Optionally, after determining whether the waypoint to which the vehicle belongs has been updated based on the waypoint triggering mechanism, the method further includes: triggering a deceleration planning mechanism to control the vehicle to stop at the stop line of the road ahead when the distance between the current position of the vehicle and the stop line position is greater than a preset distance threshold and the vehicle cannot pass through the intersection on a green light while traveling at the minimum speed limit.

[0010] Optionally, the trigger deceleration planning mechanism controls the vehicle to stop at the stop line of the road ahead, including: based on the vehicle's current position and the number of remaining road points in the current travel segment, controlling the vehicle to decelerate uniformly until it decelerates to zero at the stop line position.

[0011] This application also provides a green wave speed guidance control device for urban arterial roads, comprising: an information acquisition module for acquiring traffic light information of an intersection ahead of the vehicle; a waypoint division module for dividing the current driving segment into multiple waypoints based on the vehicle's current position and the stop line position of the intersection ahead; the current driving segment is the driving trajectory between the vehicle's current position and the stop line; the multiple waypoints include the stop line position; and a calculation module for determining whether the waypoint to which the vehicle belongs has been updated based on a waypoint triggering mechanism, and triggering green wave speed guidance calculation to obtain the optimal green wave speed for the current waypoint if it is determined that the vehicle's waypoint has been updated; wherein, the waypoint triggering mechanism is: determining whether the waypoint to which the vehicle belongs has been updated based on the angle between the vehicle's current position and two adjacent waypoints among the multiple waypoints; the green wave speed guidance calculation includes: the travel time interval for the vehicle to reach the intersection ahead, and calculating the passable travel time interval for the current traffic light intersection based on the traffic light information and the travel time interval.

[0012] Optionally, the waypoint division module is specifically used to determine two waypoints adjacent to the vehicle based on the vehicle's current position, and to calculate the angular relationship between the vehicle and the two waypoints; the waypoint division module is further used to determine that the waypoint to which the vehicle belongs has been updated if the angular relationship indicates that the angle between the vehicle and the two waypoints exceeds a preset angle threshold, otherwise, determine that the waypoint to which the vehicle belongs has not been updated.

[0013] Optionally, the calculation module is specifically used to calculate the passage time interval for the vehicle to reach the intersection ahead based on the first time when the vehicle accelerates from its initial speed to its maximum speed on the current road segment, and the second time when the vehicle decelerates from its initial speed to its minimum speed on the current road segment. The calculation module is also specifically used to, after determining the green light time window based on the traffic light information, intersect the passage time interval with the green light time window to obtain multiple passable time intervals, and determine the first of the multiple passable time intervals as the passable time interval. The calculation module is also specifically used to calculate the upper limit and lower limit of the target speed for the current road segment based on the passage time interval and the relationship between time and speed, and to calculate the optimal green wave speed for the current road point based on the upper limit and lower limit of the target speed.

[0014] Optionally, the calculation module is specifically used to calculate the ratio of the current vehicle's distance from the stop line to the minimum travel time to obtain a first ratio, and to calculate the ratio of the current vehicle's distance from the stop line to the maximum travel time to obtain a second ratio; the calculation module is further used to determine the smaller of the first ratio and the upper limit of the vehicle's travel speed as the upper limit of the target speed, and to determine the smaller of the second ratio and the lower limit of the vehicle's travel speed as the lower limit of the target speed.

[0015] Optionally, the device further includes a control module; the control module is used to trigger a deceleration planning mechanism to control the vehicle to stop at the stop line ahead when the distance between the current position of the vehicle and the stop line position is greater than a preset distance threshold and the vehicle cannot pass through the intersection on a green light while traveling at the minimum speed limit.

[0016] Optionally, the control module is specifically used to control the vehicle to decelerate uniformly based on the vehicle's current position and the number of remaining waypoints on the current travel segment, until the vehicle decelerates to zero at the stop line position.

[0017] This application also provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the urban arterial road green wave speed guidance control method described above.

[0018] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of any of the above-described urban arterial road green wave vehicle speed guidance control methods.

[0019] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the urban arterial road green wave speed guidance control method described above.

[0020] The urban arterial road green wave speed guidance control method and device provided in this application first acquires the traffic light information of the intersection ahead of the vehicle, and divides the current driving segment into multiple road points based on the vehicle's current position and the stop line position of the intersection ahead; the current driving segment is the driving trajectory between the vehicle's current position and the stop line; the multiple road points include the stop line position; then, based on the road point triggering mechanism, it is determined whether the road point to which the vehicle belongs has been updated, and if it is determined that the road point to which the vehicle belongs has been updated, green wave speed guidance calculation is triggered to obtain the optimal green wave speed of the current road point; wherein, the road point triggering mechanism is: determining whether the road point to which the vehicle belongs has been updated based on the angle between the vehicle's current position and two adjacent road points among the multiple road points; the green wave speed guidance calculation includes: the travel time interval of the vehicle to the intersection ahead, and calculating the passable travel time interval of the current traffic light intersection based on the traffic light information and the travel time interval. In this way, by taking the vehicle status information, traffic light data, and traffic flow information as inputs, Bayesian dynamic programming theory is used to efficiently solve the longitudinal velocity trajectory, and the global optimal vehicle speed is issued to the chassis actuator, so as to realize a safer, more efficient and reliable intelligent connected vehicle driving strategy. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 is a schematic diagram of the architecture of the urban arterial road green wave speed guidance control system provided in this application; Figure 2 is one of the flowcharts of the urban arterial road green wave speed guidance control method provided in this application; Figure 3 is a schematic diagram of the waypoint segmentation principle provided in this application; Figure 4 is another flowchart of the urban arterial road green wave speed guidance control method provided in this application; Figure 5 is a structural schematic diagram of the urban arterial road green wave speed guidance control device provided in this application; Figure 6 is a structural schematic diagram of the electronic device provided in this application. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0025] The related technologies, such as urban arterial road green wave speed guidance and vehicle-road cooperative technology, as well as vehicle-road-cloud cooperative technology, all rely on traffic light information, traffic flow information, and vehicle status during route planning to predictively plan the green wave speed for vehicles to the next intersection, enabling vehicles to pass through signal-controlled intersections without stopping. Current mainstream green wave speed guidance systems have significant limitations: Solutions based on vehicle-road cooperative (V2X-PC5) technology, while enabling road-vehicle information exchange to generate guidance strategies through PC5 direct communication, are limited by the PC5 communication coverage, only planning vehicle speeds within 200-300 meters of the intersection ahead. This results in unreasonable speed planning outside the communication range, restricting the system's practical value. Solutions based on vehicle-road-cloud cooperative technology, while able to expand the planning range by leveraging cloud computing power, suffer from high construction costs due to the need to deploy multiple hardware and software components. Furthermore, the lack of a unified information exchange protocol between the vehicle-cloud and onboard terminals necessitates customized development, making large-scale application difficult in the short term.

[0026] To address the aforementioned technical problems in related technologies, this application provides a method for green wave speed guidance control on urban arterial roads. This method acquires the traffic light color status, remaining time, stop line position, and traffic light timing information at the intersection ahead of the vehicle; acquires the vehicle's current speed and latitude / longitude information; plans a green wave guidance speed for the vehicle based on the acquired information; and calculates the optimal guidance speed in real time for the vehicle, taking into account the vehicle's energy consumption characteristics, comfort, and traffic efficiency requirements. It also designs a mode switching mechanism between the green wave guidance strategy mode and the adaptive cruise control mode, based on current road information and network conditions, and sends the final vehicle guidance speed to the vehicle's chassis domain for speed tracking. The designed method can reduce stops and achieve efficient and comfortable passage through each traffic light intersection, improving traffic efficiency, comfort, and reducing energy consumption while ensuring safety.

[0027] Figure 1 shows a schematic diagram of the urban arterial road green wave speed guidance control system architecture provided in this application embodiment. The user inputs the start and end points of the desired navigation route in the Human Machine Interface (HMI) and applies to enable the green wave passage function in the HMI interface. The system obtains the traffic light timing (GLi, RLi, and YLi), light color status (CurrentLighti), latitude and longitude information of the stop line position, and the start / end timestamp of the current light status (StartTimei / EndTimei) of the intersection ahead of the vehicle, and sends this information to the vehicle domain controller. At the same time, the system can also obtain the vehicle speed and latitude and longitude information, GNSS speed, current timestamp information, etc. from the CANdriver, and send this information to the vehicle domain controller for calculation via the CAN line.

[0028] The following description, in conjunction with the accompanying drawings, details the urban arterial road green wave speed guidance control method provided in this application through specific embodiments and application scenarios.

[0029] As shown in Figure 2, this application provides a method for green wave vehicle speed guidance control on urban arterial roads. The method may include the following steps 201 and 202: Step 201: Obtain the traffic light information of the intersection ahead of the vehicle, and divide the current driving segment into multiple waypoints based on the current position of the vehicle and the stop line position of the intersection ahead.

[0030] Wherein, the current driving segment is the driving trajectory between the vehicle's current position and the stop line; the plurality of waypoints includes: the stop line position.

[0031] For example, the traffic light information mentioned above includes: traffic light timing (GLi, RLi and YLi), light color status (CurrentLighti), that is, the timing of green light, red light and yellow light, as well as information such as current color and remaining time.

[0032] Specifically, in step 201 above, the step of dividing the current driving segment into multiple waypoints based on the current position of the vehicle and the stop line position of the intersection ahead may also include the following steps 201a1 and 201a2: Step 201a1: Based on the current position of the vehicle, determine two waypoints adjacent to the vehicle and calculate the angular relationship between the vehicle and the two waypoints.

[0033] Step 201a2: If the angle relationship indicates that the angle between the vehicle and the two waypoints exceeds a preset angle threshold, then the waypoint to which the vehicle belongs is determined to be updated; otherwise, the waypoint to which the vehicle belongs is determined to be not updated.

[0034] For example, this application provides a method for triggering vehicle speed planning based on waypoints, as shown in Figure 3. A trajectory point is determined based on the vehicle's position and the stop line position. The preset distance interval between trajectory points can be 25 meters, or it can be set according to actual needs. The stop line position is treated as a separate waypoint. When determining the waypoint triggering mechanism, the vehicle's position is used for judgment: A is the current vehicle position, and B1, B2, and B3 are adjacent partial waypoints. The main implementation method is to sequentially traverse adjacent waypoints, calculate the angular relationship between the vehicle and the next waypoint and the previous waypoint, until the included angle first becomes an obtuse angle (i.e., the aforementioned preset angle threshold). This method can determine the vehicle's position and determine whether the vehicle has reached a new waypoint through periodic detection. If the vehicle has reached a new waypoint, the algorithm is triggered to calculate; otherwise, the result of the previous calculation is returned. If the vehicle has passed the last waypoint of the current driving segment, the map service cloud server will send the traffic light information of the next intersection to the vehicle.

[0035] Step 202: Determine whether the road point to which the vehicle belongs has been updated based on the waypoint triggering mechanism, and if it is determined that the road point to which the vehicle belongs has been updated, trigger the green wave speed guidance calculation to obtain the optimal green wave speed for the current road point.

[0036] The waypoint triggering mechanism is as follows: it determines whether the waypoint to which the vehicle belongs is updated based on the angle between the vehicle's current position and two adjacent waypoints among the plurality of waypoints; the green wave speed guidance calculation includes: the passage time interval of the vehicle to the intersection ahead, and calculating the passage time interval of the current traffic light intersection based on the traffic light information and the passage time interval.

[0037] For example, the green wave speed guidance calculation in this embodiment may include three parts: time pre-analysis, speed pre-analysis, and optimal green wave speed calculation.

[0038] Specifically, for the time pre-analysis part, the above step 202 may also include the following steps 202a1 to 202a3: Step 202a1, based on the first time when the vehicle accelerates from the initial speed of the current road segment to the maximum driving speed, and the second time when the vehicle decelerates from the initial speed of the current road segment to the minimum driving speed, calculate the passage time interval of the vehicle to the intersection ahead.

[0039] Step 202a2: After determining the green light time window based on the traffic light information, the intersection of the passage time interval and the green light time window is obtained to obtain multiple passage time intervals, and the first time interval among the multiple passage time intervals is determined as the passage time interval.

[0040] Step 202a3: Based on the travel time interval and the relationship between time and speed, calculate the upper limit and lower limit of the target speed for the current travel segment, and calculate the optimal green wave speed for the current road point based on the upper limit and lower limit of the target speed.

[0041] For example, for a vehicle passing through the first signalized intersection with a green light, since the vehicle's initial speed is known, it is only necessary to calculate the time required to accelerate from the initial speed to the maximum speed and the time required to decelerate to the minimum speed to obtain the time interval required for the vehicle to reach the intersection. This allows for the determination of the green wave window for vehicle passage, maximizing traffic efficiency. First, based on the time required to accelerate from the initial speed to the maximum speed and the time required to decelerate to the minimum speed, the time interval required for the vehicle to reach the intersection is obtained.

[0042] For example, the aforementioned travel time interval includes: the minimum travel time and the maximum travel time of the current travel segment; the minimum travel time and the maximum travel time are calculated based on the following formula: (Formula 1) In the formula, The minimum passage time is... The maximum passage time; The distance between the current vehicle and the stop line; The initial speed of the vehicle when entering the current road segment; This represents the upper limit of vehicle speed. This represents the upper limit of the vehicle's speed. This represents the lower limit of vehicle acceleration. This represents the upper limit of vehicle acceleration.

[0043] For example, the subsequent consecutive signalized intersection passage time needs to be calculated based on the initial speed of each road segment. However, the initial speed is unknown before the vehicle speed curve is determined, so an estimation formula is used. The coarse estimation formula ignores the speed change process, resulting in an overestimation and potential interference from queuing; the conservative estimation formula considers the speed change process, assuming that the vehicle experiences deceleration and then acceleration, resulting in an underestimation. Given the need to consider queuing vehicles, the conservative estimation formula is chosen as the formula for calculating the passage time interval. Based on the traffic light phase and timing information of the intersection ahead issued by the map provider, the intersection of the estimated road segment passage time interval and the green light time window is used to obtain the passable time interval, which can be expressed by the following formula two: (Formula 2) For example, the passable interval of the current traffic light intersection is formed by combining the upper and lower speed limits and traffic light information. In order to ensure the punctuality of vehicles, the algorithm prioritizes the faster passage time, so the first passable time interval is taken as the target window to reduce invalid optimization.

[0044] Specifically, regarding the aforementioned speed pre-analysis, step 202 may further include the following steps 202b1 and 202b2: Step 202b1: Calculate the ratio of the current vehicle's distance from the stop line position to the minimum travel time to obtain a first ratio; and calculate the ratio of the current vehicle's distance from the stop line position to the maximum travel time to obtain a second ratio.

[0045] Step 202b2: Determine the smaller value between the first ratio and the upper limit of the vehicle speed as the upper limit of the target speed, and determine the smaller value between the second ratio and the lower limit of the vehicle speed as the lower limit of the target speed.

[0046] For example, the optimization process requires searching and optimizing within the vehicle speed state space. Therefore, the travel time interval obtained through time pre-analysis in the previous section needs to be transformed into a speed expression that better suits the speed control requirements. The core purpose of this transformation is to provide accurate speed references for subsequent implementation of green wave speed guidance. Specifically, assuming the longest time for a vehicle to reach the i-th intersection is... The shortest time is (All data are derived from the time pre-analysis in the previous section). By combining the length of the road segment between this intersection and the previous intersection (which implies the spatial attributes of the road segment), the upper and lower limits of the target speed of the vehicle when traveling on this road segment can be determined through the relationship between time and speed.

[0047] Specifically, the aforementioned target speed limit and target speed lower limit It can be calculated using the following formula three: (Formula 3) Wherein, and These represent the maximum and minimum speed limits for that road segment, ensuring that vehicle speeds do not exceed or fall within these thresholds. Clearly defined speed ranges provide a quantitative basis for green wave speed guidance, making it easier for vehicles traveling within this range to match the green light phases at intersections, ultimately achieving green wave traffic flow.

[0048] Specifically, regarding the optimal green wave speed calculation mentioned above, considering the green wave time window, the current vehicle state, and physical constraints, the green wave speed calculation formula can be expressed as follows: Formula 4: (Formula 4) In the formula, This represents the optimal green wave speed, meaning that vehicles traveling at this guided speed at each road point can pass through each intersection on a green light, and this speed allows vehicles to pass through during the first half of each green light time window. The coefficients 0.7 and 0.3 mentioned above can be adjusted according to actual conditions.

[0049] Optionally, in this embodiment of the application, a vehicle comfort and energy-saving deceleration plan that takes into account braking to a stop is also implemented to solve the problems of poor comfort, high energy consumption, and insufficient parking position accuracy caused by short-distance emergency braking when the vehicle encounters a red light.

[0050] For example, in step 202 above, after determining whether the waypoint to which the vehicle belongs has been updated based on the waypoint triggering mechanism, the urban arterial road green wave speed guidance control method provided in this application embodiment may also include the following step 203: Step 203, when the distance between the current position of the vehicle and the position of the stop line is greater than a preset distance threshold, and the vehicle cannot pass through the intersection on a green light at the minimum speed limit, a deceleration planning mechanism is triggered to control the vehicle to stop at the stop line of the road ahead.

[0051] For example, firstly, by combining onboard equipment such as a vehicle-to-everything (V2X) communication unit, a high-precision navigation map (positioning accuracy ≤ 0.1 meters), a vehicle speed sensor, and millimeter-wave radar (detection range ≥ 200 meters), the distance between the vehicle and the stop line ahead is determined. If the domain controller calculates that the vehicle is still at risk of running a red light even when traveling at the minimum speed limit, a vehicle comfort and energy-saving deceleration planning method based on red light duration is proposed. Deceleration planning is triggered when the following conditions are met, which can be expressed by the following formula: and (Formula 5) Wherein, The remaining time of the current light color and .

[0052] Specifically, step 203 above may also include the following step 203a: Step 203a: Based on the current position of the vehicle and the number of remaining road points on the current travel segment, control the vehicle to decelerate uniformly until it decelerates to zero at the stop line position.

[0053] For example, as shown in Figure 4, when a vehicle is within 150 meters of the stop line, if the light is red, the system calculates whether it will run the red light based on the vehicle's position and current speed. This means that if the vehicle is traveling at its current speed, it can proceed through the green light; if If the speed is set to 0, it means that the vehicle will run a red light if it travels at this speed. In this case, it needs to enter the uniform deceleration logic, which means that the vehicle will stop at a constant speed in front of the stop line. This provides a guide speed for L2 level vehicles to stop at the stop line without affecting the driving experience of surrounding vehicles.

[0054] The urban arterial road green wave speed guidance control method provided in this application takes vehicle status information, traffic light data and traffic flow information as inputs, and uses Bayesian dynamic programming theory to efficiently solve the longitudinal speed trajectory, so as to issue the global optimal vehicle speed to the chassis actuator, thereby realizing a safer, more efficient and reliable intelligent connected vehicle driving strategy.

[0055] In one possible implementation, the green wave speed guidance system can be integrated with the ACC or NOA system to obtain a fusion solution. This fusion solution includes a data interaction module, a decision control module, and an execution feedback module. These modules work together to achieve deep integration of green wave guidance and autonomous driving. First, the data interaction module establishes a communication link between the green wave system and the vehicle's ECU (Electronic Control Unit): the green wave system transmits the green wave interval speed (e.g., recommended driving speed of 50-60 km / h for the current driving segment, remaining green light duration of 8 seconds at the intersection ahead) and route planning information (e.g., maintaining the current lane 300 meters ahead) generated after processing the map provider's network information to the ECU in real time. At the same time, the ECU feeds back the operating status data of the vehicle's built-in ACC / NOA system (e.g., current cruising speed, following distance setting, lane position information) and real-time road condition data collected by onboard sensors (radar, camera) (e.g., slower vehicle 50 meters ahead, no obstacles in adjacent lanes) to the green wave system. Secondly, the decision control module makes dynamic decisions based on data from both sides: when the vehicle is in a green wave coverage area and there are no sudden disturbances, the target speed of ACC is adjusted based on the green wave interval speed to ensure the vehicle travels according to the green wave sequence, while the NOA system maintains lane stability in conjunction with green wave path planning; when the onboard sensors detect sudden situations such as vehicles cutting in or pedestrians crossing ahead, the decision module prioritizes triggering the following deceleration function of ACC, while the green wave system updates the green wave speed planning based on the vehicle's real-time position and speed (e.g., adjusting the original recommended speed of 60km / h to 40km / h), and synchronously feeds it back to the NOA system to assist it in determining whether a lane change is necessary; when the vehicle approaches an intersection and the green light is about to end, if the green wave system predicts that it cannot pass through during the green light period, the decision module triggers ACC to decelerate to a stop, and the NOA system controls the vehicle to smoothly enter the stop line, avoiding the risk of running a red light. Finally, the execution feedback module monitors the vehicle's execution results in real time (such as the deviation between the actual vehicle speed and the green wave target speed, and lane keeping accuracy), and sends the data back to the decision control module to dynamically optimize the control strategy.

[0056] The urban arterial road green wave speed guidance control method provided in this application embodiment can be fully combined with ACC cruise mode or NOA city navigation mode. By complementing each other's advantages, it solves the scenario limitations of a single system and achieves the coordinated optimization of vehicle speed "global optimal planning - local safety assurance", further comprehensively improving vehicle traffic efficiency and safety.

[0057] The urban arterial road green wave speed guidance control method provided in this application embodiment first obtains the traffic light information of the intersection ahead of the vehicle, and divides the current driving segment into multiple road points based on the vehicle's current position and the stop line position of the intersection ahead; the current driving segment is the driving trajectory between the vehicle's current position and the stop line; the multiple road points include the stop line position; then, based on the road point triggering mechanism, it is determined whether the road point to which the vehicle belongs has been updated, and if it is determined that the road point to which the vehicle belongs has been updated, green wave speed guidance calculation is triggered to obtain the optimal green wave speed of the current road point; wherein, the road point triggering mechanism is: determining whether the road point to which the vehicle belongs has been updated based on the angle between the vehicle's current position and two adjacent road points among the multiple road points; the green wave speed guidance calculation includes: the travel time interval of the vehicle to the intersection ahead, and calculating the passable travel time interval of the current traffic light intersection based on the traffic light information and the travel time interval. In this way, by taking the vehicle status information, traffic light data, and traffic flow information as inputs, Bayesian dynamic programming theory is used to efficiently solve the longitudinal velocity trajectory, and the global optimal vehicle speed is issued to the chassis actuator, so as to realize a safer, more efficient and reliable intelligent connected vehicle driving strategy.

[0058] It should be noted that the urban arterial road green wave speed guidance control method provided in this application embodiment can be executed by an urban arterial road green wave speed guidance control device, or a control module in the urban arterial road green wave speed guidance control device for executing the urban arterial road green wave speed guidance control method. This application embodiment uses the execution of the urban arterial road green wave speed guidance control method by the urban arterial road green wave speed guidance control device as an example to illustrate the urban arterial road green wave speed guidance control device provided in this application embodiment.

[0059] It should be noted that, in the embodiments of this application, the green wave vehicle speed guidance and control methods for urban arterial roads shown in the accompanying drawings are all illustrated by way of example with reference to one of the accompanying drawings in the embodiments of this application. In specific implementation, the green wave vehicle speed guidance and control methods for urban arterial roads shown in the accompanying drawings of the above methods can also be implemented in conjunction with any other accompanying drawings shown in the above embodiments, which will not be elaborated here.

[0060] The following describes the urban arterial road green wave speed guidance control device provided in this application. The description below can be referred to in correspondence with the urban arterial road green wave speed guidance control method described above.

[0061] Figure 5 is a schematic diagram of the urban arterial road green wave speed guidance control device provided in this application embodiment. As shown in Figure 5, it specifically includes: an information acquisition module 501, used to acquire traffic light information of the intersection ahead of the vehicle; a road point division module 502, used to divide the current driving segment into multiple road points based on the current position of the vehicle and the stop line position of the intersection ahead; the current driving segment is the driving trajectory between the current position of the vehicle and the stop line; the multiple road points include: the stop line position; a calculation module 503, used to determine whether the road point to which the vehicle belongs has been updated based on the road point triggering mechanism, and if it is determined that the road point to which the vehicle belongs has been updated, trigger green wave speed guidance calculation to obtain the optimal green wave speed of the current road point; wherein, the road point triggering mechanism is: determining whether the road point to which the vehicle belongs has been updated based on the angle between the current position of the vehicle and two adjacent road points among the multiple road points; the green wave speed guidance calculation includes: the travel time interval of the vehicle to the intersection ahead, and calculating the passable time interval of the current traffic light intersection based on the traffic light information and the travel time interval.

[0062] Optionally, the waypoint division module 502 is specifically used to determine two waypoints adjacent to the vehicle based on the vehicle's current position, and calculate the angular relationship between the vehicle and the two waypoints; the waypoint division module 502 is also specifically used to determine that the waypoint to which the vehicle belongs is updated if the angular relationship indicates that the angle between the vehicle and the two waypoints exceeds a preset angle threshold, otherwise, determine that the waypoint to which the vehicle belongs has not been updated.

[0063] Optionally, the calculation module 503 is specifically used to calculate the passage time interval for the vehicle to reach the intersection ahead based on the first time when the vehicle accelerates from its initial speed to its maximum speed on the current road segment and the second time when the vehicle decelerates from its initial speed to its minimum speed on the current road segment; the calculation module 503 is also specifically used to, after determining the green light time window based on the traffic light information, intersect the passage time interval with the green light time window to obtain multiple passable time intervals, and determine the first time interval among the multiple passable time intervals as the passable time interval; the calculation module 503 is also specifically used to calculate the upper limit and lower limit of the target speed of the current road segment based on the passage time interval and the correlation between time and speed, and calculate the optimal green wave speed of the current road point based on the upper limit and lower limit of the target speed.

[0064] Optionally, the calculation module 503 is specifically used to calculate the ratio of the current vehicle's distance from the stop line to the minimum travel time to obtain a first ratio, and to calculate the ratio of the current vehicle's distance from the stop line to the maximum travel time to obtain a second ratio; the calculation module 503 is further used to determine the smaller of the first ratio and the upper limit of the vehicle's travel speed as the upper limit of the target speed, and to determine the smaller of the second ratio and the lower limit of the vehicle's travel speed as the lower limit of the target speed.

[0065] Optionally, the device further includes a control module; the control module is used to trigger a deceleration planning mechanism to control the vehicle to stop at the stop line ahead when the distance between the current position of the vehicle and the stop line position is greater than a preset distance threshold and the vehicle cannot pass through the intersection on a green light while traveling at the minimum speed limit.

[0066] Optionally, the control module is specifically used to control the vehicle to decelerate uniformly based on the vehicle's current position and the number of remaining waypoints on the current travel segment, until the vehicle decelerates to zero at the stop line position.

[0067] The urban arterial road green wave speed guidance control device provided in this application first acquires the traffic light information of the intersection ahead of the vehicle, and divides the current driving segment into multiple road points based on the vehicle's current position and the stop line position of the intersection ahead; the current driving segment is the driving trajectory between the vehicle's current position and the stop line; the multiple road points include the stop line position; then, based on the road point triggering mechanism, it is determined whether the road point to which the vehicle belongs has been updated, and if it is determined that the road point to which the vehicle belongs has been updated, green wave speed guidance calculation is triggered to obtain the optimal green wave speed of the current road point; wherein, the road point triggering mechanism is: determining whether the road point to which the vehicle belongs has been updated based on the angle between the vehicle's current position and two adjacent road points among the multiple road points; the green wave speed guidance calculation includes: the travel time interval of the vehicle to the intersection ahead, and calculating the passable travel time interval of the current traffic light intersection based on the traffic light information and the travel time interval. In this way, by taking the vehicle status information, traffic light data, and traffic flow information as inputs, Bayesian dynamic programming theory is used to efficiently solve the longitudinal velocity trajectory, and the global optimal vehicle speed is issued to the chassis actuator, so as to realize a safer, more efficient and reliable intelligent connected vehicle driving strategy.

[0068] Figure 6 illustrates a schematic diagram of the physical structure of an electronic device. As shown in Figure 6, the electronic device may include: a processor 610, a communication interface 620, a memory 630, and a communication bus 640. The processor 610, the communication interface 620, and the memory 630 communicate with each other through the communication bus 640. The processor 610 can call logic instructions in the memory 630 to execute a green wave speed guidance control method for urban arterial roads. This method includes: first, acquiring traffic light information at the intersection ahead of the vehicle, and dividing the current driving segment into multiple road points based on the vehicle's current position and the stop line position at the intersection ahead; the current driving segment is the driving trajectory between the vehicle's current position and the stop line; the multiple road points include the stop line position; then, determining whether the road point to which the vehicle belongs has been updated based on a road point triggering mechanism, and if it is determined that the road point to which the vehicle belongs has been updated, triggering green wave speed guidance calculation to obtain the optimal green wave speed for the current road point; wherein, the road point triggering mechanism is: determining whether the road point to which the vehicle belongs has been updated based on the angle between the vehicle's current position and two adjacent road points among the multiple road points; the green wave speed guidance calculation includes: the travel time interval for the vehicle to reach the intersection ahead, and calculating the passable travel time interval for the current traffic light intersection based on the traffic light information and the travel time interval. In this way, by taking the vehicle status information, traffic light data, and traffic flow information as inputs, Bayesian dynamic programming theory is used to efficiently solve the longitudinal velocity trajectory, and the global optimal vehicle speed is issued to the chassis actuator, so as to realize a safer, more efficient and reliable intelligent connected vehicle driving strategy.

[0069] Furthermore, the logical instructions in the aforementioned memory 630 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0070] On the other hand, this application also provides a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by the computer, the computer can execute the urban arterial road green wave speed guidance control method provided by the above methods. The method includes: first, acquiring traffic light information of the intersection ahead of the vehicle, and dividing the current driving segment into multiple road points based on the vehicle's current position and the stop line position of the intersection ahead; the current driving segment is the driving trajectory between the vehicle's current position and the stop line; the multiple road points include the stop line position; then, determining whether the road point to which the vehicle belongs has been updated based on a road point triggering mechanism, and if it is determined that the road point to which the vehicle belongs has been updated, triggering green wave speed guidance calculation to obtain the optimal green wave speed of the current road point; wherein, the road point triggering mechanism is: determining whether the road point to which the vehicle belongs has been updated based on the angle between the vehicle's current position and two adjacent road points among the multiple road points; the green wave speed guidance calculation includes: the travel time interval of the vehicle arriving at the intersection ahead, and calculating the passable travel time interval of the current traffic light intersection based on the traffic light information and the travel time interval. In this way, by taking the vehicle status information, traffic light data, and traffic flow information as inputs, Bayesian dynamic programming theory is used to efficiently solve the longitudinal velocity trajectory, and the global optimal vehicle speed is issued to the chassis actuator, so as to realize a safer, more efficient and reliable intelligent connected vehicle driving strategy.

[0071] In another aspect, this application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the aforementioned urban arterial road green wave speed guidance control methods. The method includes: first, acquiring traffic light information of the intersection ahead of the vehicle, and dividing the current driving segment into multiple road points based on the vehicle's current position and the stop line position of the intersection ahead; the current driving segment is the driving trajectory between the vehicle's current position and the stop line; the multiple road points include the stop line position; then, determining whether the road point to which the vehicle belongs has been updated based on a road point triggering mechanism, and if it is determined that the road point to which the vehicle belongs has been updated, triggering green wave speed guidance calculation to obtain the optimal green wave speed for the current road point; wherein, the road point triggering mechanism is: determining whether the road point to which the vehicle belongs has been updated based on the angle between the vehicle's current position and two adjacent road points among the multiple road points; the green wave speed guidance calculation includes: the travel time interval for the vehicle to reach the intersection ahead, and calculating the passable travel time interval for the current traffic light intersection based on the traffic light information and the travel time interval. In this way, by taking the vehicle status information, traffic light data, and traffic flow information as inputs, Bayesian dynamic programming theory is used to efficiently solve the longitudinal velocity trajectory, and the global optimal vehicle speed is issued to the chassis actuator, so as to realize a safer, more efficient and reliable intelligent connected vehicle driving strategy.

[0072] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0073] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for guiding and controlling green wave vehicle speed on urban arterial roads, characterized in that, This includes: obtaining traffic light information at the intersection ahead of the vehicle, and dividing the current driving segment into multiple waypoints based on the vehicle's current position and the stop line position at the intersection ahead; The current driving segment is the driving trajectory between the vehicle's current position and the stop line; The multiple waypoints include: the stop line position; determining whether the waypoint to which the vehicle belongs has been updated based on the waypoint triggering mechanism, and triggering green wave speed guidance calculation when it is determined that the waypoint to which the vehicle belongs has been updated, to obtain the optimal green wave speed for the current waypoint; wherein, the waypoint triggering mechanism is: determining whether the waypoint to which the vehicle belongs has been updated based on the angle between the current position of the vehicle and two adjacent waypoints among the multiple waypoints; the green wave speed guidance calculation includes: the travel time interval for the vehicle to reach the intersection ahead, and calculating the passable travel time interval for the current traffic light intersection based on the traffic light information and the travel time interval.

2. The method according to claim 1, characterized in that, The method for determining whether a vehicle's waypoint has been updated based on the waypoint triggering mechanism includes: determining two waypoints adjacent to the vehicle based on the vehicle's current position, and calculating the angular relationship between the vehicle and the two waypoints; if the angular relationship indicates that the angle between the vehicle and the two waypoints exceeds a preset angle threshold, then the waypoint to which the vehicle belongs has been updated; otherwise, the waypoint to which the vehicle belongs has not been updated.

3. The method according to claim 1 or 2, characterized in that, The calculation of the triggered green wave speed guidance to obtain the optimal green wave speed for the current road point includes: calculating the passage time interval for the vehicle to reach the next intersection based on the first time it takes for the vehicle to accelerate from its initial speed to its maximum speed on the current road segment, and the second time it takes for the vehicle to decelerate from its initial speed to its minimum speed on the current road segment; after determining the green light time window based on the traffic light information, intersecting the passage time interval with the green light time window to obtain multiple passable time intervals, and determining the first of the multiple passable time intervals as the passable time interval; based on the passable time interval and the correlation between time and speed, calculating the upper limit and lower limit of the target speed for the current road segment, and calculating the optimal green wave speed for the current road point based on the upper limit and lower limit of the target speed.

4. The method according to claim 3, characterized in that, The passage time interval includes: the minimum passage time and the maximum passage time of the current travel segment; the minimum passage time and the maximum passage time are calculated based on the following formula: In the formula, The minimum passage time is... The maximum passage time; The distance between the current vehicle and the stop line; The initial speed of the vehicle when entering the current road segment; This represents the upper limit of vehicle speed. This represents the upper limit of the vehicle's speed. This represents the lower limit of vehicle acceleration. This represents the upper limit of vehicle acceleration.

5. The method according to claim 3, characterized in that, The step of calculating the upper limit and lower limit of the target speed for the current travel segment based on the travel time interval and the correlation between time and speed includes: calculating the ratio of the current vehicle's distance from the stop line to the minimum travel time to obtain a first ratio, and calculating the ratio of the current vehicle's distance from the stop line to the maximum travel time to obtain a second ratio; determining the smaller value between the first ratio and the upper limit of the vehicle's travel speed as the upper limit of the target speed, and determining the smaller value between the second ratio and the lower limit of the vehicle's travel speed as the lower limit of the target speed.

6. The method according to claim 1, characterized in that, After determining whether the waypoint to which the vehicle belongs has been updated based on the waypoint triggering mechanism, the method further includes: if the distance between the current position of the vehicle and the position of the stop line is greater than a preset distance threshold, and the vehicle cannot pass through the intersection on a green light while traveling at the minimum speed limit, triggering a deceleration planning mechanism to control the vehicle to stop at the stop line of the road ahead.

7. The method according to claim 6, characterized in that, The trigger deceleration planning mechanism controls the vehicle to stop at the stop line of the road ahead, including: based on the vehicle's current position and the number of remaining road points in the current travel segment, controlling the vehicle to decelerate evenly until it decelerates to zero at the stop line position.

8. A green wave vehicle speed guidance and control device for urban arterial roads, characterized in that, The device includes: an information acquisition module for acquiring traffic light information at the intersection ahead of the vehicle; a waypoint division module for dividing the current driving segment into multiple waypoints based on the vehicle's current position and the stop line position at the intersection ahead; the current driving segment is the driving trajectory between the vehicle's current position and the stop line; the multiple waypoints include the stop line position; and a calculation module for determining whether the waypoint to which the vehicle belongs has been updated based on a waypoint triggering mechanism, and triggering green wave speed guidance calculation to obtain the optimal green wave speed for the current waypoint if it is determined that the vehicle's waypoint has been updated; wherein, the waypoint triggering mechanism is: determining whether the waypoint to which the vehicle belongs has been updated based on the angle between the vehicle's current position and two adjacent waypoints among the multiple waypoints; the green wave speed guidance calculation includes: the travel time interval for the vehicle to reach the intersection ahead, and calculating the passable travel time interval for the current traffic light intersection based on the traffic light information and the travel time interval.

9. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the urban arterial road green wave speed guidance control method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the steps of the urban arterial road green wave speed guidance control method as described in any one of claims 1 to 7.

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