Navigation prompting method in traffic light scene and corresponding device

Abstract

The embodiment of the invention discloses a navigation prompting method in a traffic light scene and a corresponding device, and relates to the technical field of navigation. The method comprises the following steps: determining a queuing position of a target vehicle in front of a target traffic light of a current intersection according to road network data; according to the queuing position and the traffic light timing data, determining the starting estimated time of the target vehicle; and starting reminding information is generated based on the starting estimated time. According to the invention, accurate navigation prompt can be provided for a driving user, the driving user can start in time conveniently, and the driving safety and the passing efficiency are improved. Besides, the method can be realized without highly depending on the sensing capability and without sensing equipment and a sensing system, so that the realization cost is low.

Description

Technical Field

[0001] The present application relates to the field of navigation technology, and in particular to a navigation prompt method and corresponding device in a traffic light scenario. Background Art

[0002] With the acceleration of urbanization, traffic congestion and traffic problems are becoming increasingly prominent, especially on urban ground roads. As an indispensable part of modern navigation systems, navigation prompts can provide real-time guidance to drivers on routes, directions, traffic conditions and other important information through sound, vision or touch. Among them, traffic light scenes are frequently experienced by drivers. How to provide accurate navigation prompts to drivers in traffic light scenes will have a significant impact on driving safety and traffic efficiency, and has become an urgent problem to be solved. Summary of the invention

[0003] In view of this, the present application provides a navigation prompt method and corresponding scenario in a traffic light scenario, so as to provide accurate navigation prompts to driving users and improve driving safety and traffic efficiency.

[0004] This application provides the following solutions:

[0005] In a first aspect, a navigation prompt method in a traffic light scenario is provided, the method comprising:

[0006] According to the road network data, determine the queue position of the target vehicle in front of the target traffic light at the current intersection;

[0007] Determining the estimated starting time of the target vehicle based on the queue position and traffic light timing data;

[0008] Based on the estimated start time, start reminder information is generated.

[0009] In a second aspect, a navigation prompt device in a traffic light scenario is provided, the device comprising:

[0010] A position determination unit is configured to determine a queue position of the target vehicle in front of a target traffic light at a current intersection based on the road network data;

[0011] A first estimation unit is configured to determine an estimated start time of the target vehicle according to the queue position and traffic light timing data;

[0012] The reminder generating unit is configured to generate start reminder information based on the estimated start time.

[0013] In a third aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the steps of any one of the methods in the first aspect are implemented.

[0014] In a fourth aspect, an electronic device is provided, including:

[0015] one or more processors; and

[0016] A memory associated with the one or more processors, the memory being used to store program instructions, wherein the program instructions, when read and executed by the one or more processors, execute the steps of the method described in any one of the first aspects above.

[0017] In a fifth aspect, a computer program product is provided, comprising a computer program, which, when executed by a processor, performs the steps of any one of the methods described in the first aspect.

[0018] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0019] This application uses road network data and traffic light timing data to determine the estimated start time of the target vehicle waiting in line at the target traffic light. Based on the estimated start time, start reminder information can be generated, thereby providing accurate navigation prompts to the driving user, facilitating the driving user to start in time, and improving driving safety and traffic efficiency. In addition, this method does not need to be highly dependent on perception capabilities, and can be implemented without sensor equipment and perception systems, and has low implementation costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 is a system architecture diagram applicable to the embodiments of the present application;

[0022] Figure 2 A flow chart of a navigation prompt method in a traffic light scenario provided by an embodiment of the present application;

[0023] Figure 3 A schematic diagram of an intersection provided for an embodiment of the present application;

[0024] Figure 4 A schematic diagram of the state of the target vehicle at each moment provided in an embodiment of the present application;

[0025] Figure 5 A schematic block diagram of a navigation prompt device provided in an embodiment of the present application;

[0026] Figure 6 A schematic block diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the present application belong to the scope of protection of this application.

[0028] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The singular forms "a", "said" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0029] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0030] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0031] At present, there are some navigation prompt methods in traffic light scenarios, but most of them are navigation prompts for traffic light changes (i.e., light change reminders). For example, 3 to 5 seconds before the traffic light changes from red to green, a navigation prompt such as "the red light will turn green soon, get ready to start" is issued to the driver. However, this navigation prompt method is only a reference for vehicles waiting to start in the front position, while it is difficult for drivers who are farther away to judge when it is appropriate to start. In order to solve the problem of starting prompts, there are currently some technologies that equip the vehicle with sensor equipment such as cameras and radars to sense the start of the vehicle in front and then generate start guidance information, but this method is highly dependent on perception capabilities and requires sensor equipment and perception systems to be implemented, which is costly.

[0032] In view of this, the present application provides a new idea. In order to facilitate the understanding of the present application, the system architecture on which the present application is based is first described. Figure 1 An exemplary system architecture to which the embodiments of the present application can be applied is shown. Figure 1 As shown in , the system architecture may include: a user terminal and a server running on a user terminal.

[0033] The server provides map services to users, such as providing map data to the user end, providing navigation services to users, traffic information (including traffic conditions of road sections), etc. The embodiments of this application focus on navigation services, that is, the server end provides navigation services to users. Driving users request navigation services through the client during driving, and can obtain navigation prompts through the user end during the use of navigation. Among them, the navigation prompts can be displayed or broadcasted to driving users in the form of text, voice, icons, etc. For the convenience of description, the navigation broadcast in voice form is used as an example in the subsequent embodiments.

[0034] The user terminal may include, but is not limited to, smart mobile terminals, wearable devices, etc. Smart mobile devices may include, for example, mobile phones, tablet computers, PDAs (Personal Digital Assistants), Internet car terminals, etc. Wearable devices may include, for example, smart watches, smart glasses, smart bracelets, VR (Virtual Reality) devices, AR (Augmented Reality), mixed reality devices (i.e., devices that can support both virtual reality and augmented reality), etc.

[0035] The above-mentioned user end can be a client running on a user terminal, a small program, or a Web application running through a browser, etc.

[0036] As one possible implementation, the server may generate navigation prompts using the method provided in the embodiment of the present application, and send the generated navigation prompts to the user, who then broadcasts the navigation prompts to the driving user in a form such as voice.

[0037] As another achievable method, the user terminal may use the method provided in the embodiment of the present application to generate a navigation prompt, and display or play the navigation prompt to the user. The specific method will be described in detail in the subsequent embodiments.

[0038] The above-mentioned server can be an independent server, a server group, or a cloud server. A cloud server, also known as a cloud computing server or cloud host, is a host product in the cloud computing service system to solve the defects of difficult management and weak service scalability in traditional physical hosts and virtual private servers (VPS) services.

[0039] It should be understood that Figure 1 The number of servers, clients and user terminals in the embodiment is only for illustration. Any number of servers, clients and user terminals may be provided according to implementation requirements.

[0040] Figure 2 A flowchart of a navigation prompt method in a traffic light scenario provided in an embodiment of the present application. The method can be performed by Figure 1 The server side in the system shown can also be executed by the user side. Figure 2 As shown in , the method may include the following steps:

[0041] Step 201: Determine the queue position of the target vehicle in front of the target traffic light at the current intersection based on the road network data.

[0042] Step 203: Determine the estimated starting time of the target vehicle based on the queue position and traffic light timing data.

[0043] Step 205: Generate start reminder information based on the estimated start time.

[0044] As can be seen from the above process, this application uses road network data and traffic light timing data to determine the estimated start time of the target vehicle waiting in line at the target traffic light. Based on the estimated start time, start reminder information can be generated, thereby providing accurate navigation prompts to driving users, facilitating driving users to start in time, and improving driving safety and traffic efficiency. In addition, this method does not need to be highly dependent on perception capabilities, and can be implemented without sensor equipment and perception systems, and has low implementation costs.

[0045] The following is a detailed description of each step in the above process and the effects that can be further produced in conjunction with the embodiments. It should be noted that the "first" and "second" limitations involved in the present disclosure do not have limitations in terms of size, order, and quantity, and are only used to distinguish them in name. For example, "first time" and "second time" are only used to distinguish the time of passing through two intersections in name.

[0046] First, the above step 201, namely "determining the queue position of the target vehicle in front of the target traffic light at the current intersection based on the road network data" is described in detail in conjunction with the embodiment.

[0047] The so-called traffic lights, also known as traffic lights, are usually set up at intersections or other places where traffic control is required. They include at least red and green lights, and may also include yellow lights.

[0048] Road network data refers to road network data, including geometric features, attribute information, and connection relationships of each road (usually a road contains multiple lanes). Among them, the connection relationship of each road may include intersection information, for example, the intersection between roads forms a specific intersection. The attribute information of the road may include traffic light information, road type, road grade, etc. at the corresponding intersection. With the rapid development of data mining technology, lane-level road network data has emerged, and lane information of each road can be obtained from lane-level road network data.

[0049] Traffic light timing data refers to a series of time parameters used to control and optimize traffic lights. These parameters usually determine when the traffic light turns on red, green and yellow, and how long each color of light lasts. Through any traffic light sign, the timing data corresponding to the sign can be found from the traffic light timing data.

[0050] Road network data and traffic light timing data can be pre-mined through specific algorithms, or obtained through interfaces provided by traffic management departments, smart city platforms, etc., or obtained through other methods. This application does not impose any special restrictions on this.

[0051] The obtained road network data and traffic light timing data can be stored on the server side. If the generation of the navigation prompt provided in the embodiment of the present application is performed by the server side, the server side can obtain the stored road network data and traffic light timing data.

[0052] If the generation of the navigation prompt provided in the embodiment of the present application is performed by the user end, the user end can obtain the corresponding road network data and traffic light timing data within the nearby location range from the server end, and can also obtain the road network data and traffic light timing data within the corresponding range of the navigation route from the server end. The nearby location range can be within a preset distance range from the current location of the user end. In addition to this method, other methods can also be used, such as the user end pre-acquiring the road network data and traffic light timing data within the city, district, county, etc.

[0053] In this step, the state of the target traffic light can be determined based on the traffic light timing data. The so-called target traffic light can be any traffic light at any intersection.

[0054] The “in front of the target traffic light” involved in the embodiments of the present application refers to a position where a queue may occur due to the influence of the target traffic light, and may specifically include a lane associated with the target traffic light.

[0055] In this step, the lanes associated with the target traffic light corresponding to the intersection can be determined based on the road network data. The lanes associated with the target traffic light can be understood as the lanes affected by the target traffic light. For example, if a target traffic light includes left turn and through signals, the lanes affected by the target traffic light are the left turn lane and the through lane. Figure 3 In some cases, the lanes affected by the target traffic light may also include left-turn and straight-ahead merging lanes. After the vehicle drives to the lanes affected by the target traffic light, continuous monitoring of the vehicle begins. If a queue occurs, the queue position of the vehicle in front of the target traffic light at the intersection is determined. It should be noted that the above-mentioned left-turn lanes, straight-ahead lanes, left-turn and straight-ahead merging lanes, etc. are only examples and do not represent all possible situations.

[0056] As one of the feasible ways, if it is determined that a vehicle is located in a lane associated with a target traffic light and is stationary, the vehicle is taken as a target vehicle, and the queue position of the vehicle in front of the target traffic light at the current intersection is determined. Usually, a vehicle that is located in a lane associated with the target traffic light and is stationary is a vehicle waiting for a red light, and is referred to as a target vehicle in the embodiment of the present application. This part of the target vehicles includes vehicles that are waiting in line when the target traffic light is red, and also includes vehicles that are waiting in line after the target traffic light changes from red to green because the queued vehicles have not yet dissipated. In other words, vehicles in a queue waiting state need a start reminder. This method can accurately determine the target vehicles that need a start reminder through road network data, and only predict the estimated start time and start reminder for the target vehicle to avoid disturbing other vehicles.

[0057] When determining whether the vehicle is located in the lane associated with the target traffic light, it can be determined based on the vehicle's trajectory data. The vehicle's trajectory data reflects the vehicle's position information at each continuous time point, and also reflects the vehicle's speed, acceleration and other state information. Therefore, determining whether the target vehicle is located in the lane associated with the target traffic light based on the vehicle's trajectory data is usually more accurate than simply using the target vehicle's positioning data.

[0058] If lane-level trajectory data cannot be obtained, the vehicle's location information and whether it is located in a lane associated with a target traffic light may also be determined based on the vehicle's navigation data.

[0059] If it is determined that the vehicle is located in the lane associated with the target traffic light and is stationary, the navigation data can be further combined to distinguish vehicles in some scenarios. That is, based on the navigation data of the vehicle, the vehicle whose driving intention meets the preset intention can be determined and the vehicle can be used as the target vehicle. The preset intention can include vehicles turning left or going straight through the current intersection. For the target vehicle, its navigation data can reflect the driving intention of the target vehicle in addition to reflecting the location information of the target vehicle. The driving intention in the intersection scenario is mainly how to pass the intersection, including going straight through the intersection, turning left through the intersection, turning right through the intersection, turning around at the intersection, etc. In some scenarios, turning right through the intersection and turning around at the intersection are less affected by traffic lights. However, going straight through the intersection and turning left through the intersection are often affected by traffic lights and cause queues. Therefore, this method can more accurately determine the vehicle that needs to be reminded to start as the target vehicle, improve the user experience, and avoid disturbing other vehicles that do not need reminders.

[0060] The above step 203, namely "determining the estimated starting time of the target vehicle based on the queue position and traffic light timing data", is described in detail below in conjunction with an embodiment.

[0061] Usually in a traffic light scenario, when the traffic light changes from red to green, the vehicles in the queue start one by one and gradually accelerate to pass through the intersection. The starting time of the vehicle is related to its location and the light change time (i.e. the time when the light turns green). In the embodiments of the present application, the following two methods can be used but are not limited to:

[0062] The first method: conduction velocity-based method

[0063] In this method, first, the queuing distance corresponding to the target vehicle is determined based on the queuing position of the target vehicle in front of the target traffic light at the current intersection. The queuing distance is the distance between the target vehicle and the stop line of the current intersection; then, the estimated starting time of the target vehicle is determined based on the time when the state of the target traffic light changes to green, the queuing distance and the preset transmission speed.

[0064] like Figure 4 As shown in , at time T1, the target traffic light is in the red light state. Assuming that the green vehicle is the target vehicle, its corresponding queue distance is L. At time T2, the state of the target traffic light changes to green, and the vehicle at the head of the queue starts to move. According to the queue dissipation law, each vehicle in the queue starts in turn and starts to move with the starting acceleration. Assuming that the conduction speed of the vehicle start is V, the queue dissipation time of the target vehicle can be determined based on L and V, and the starting time T3 of the target vehicle (referred to as the estimated starting time in the embodiment of the present application) can be determined based on T2 and the queue dissipation time.

[0065] The conduction speed V may be an empirical value or an experimental value. For example, the historical trajectory of the vehicle at the intersection is collected and the conduction speed at the start is determined based on the historical trajectory.

[0066] This implementation method is computationally simple and efficient. When it is necessary to process the prediction of the estimated starting time of a large number of target vehicles, it can effectively reduce the performance pressure on computing resources and achieve higher prediction efficiency.

[0067] Furthermore, before the target vehicle starts, the actual starting time of each preceding vehicle (i.e., other vehicles in the queue before the target vehicle) may have some deviations due to factors such as the reaction time of the driving user and the surrounding environment. Therefore, before the estimated starting time is displayed or announced on the user side, the actual starting time of the preceding vehicle can be used to correct the estimated starting time of the target vehicle, thereby further improving the accuracy of the estimated starting time.

[0068] For example, assuming that the difference between the queue distance of a preceding vehicle and the queue distance of the target vehicle is ΔL, and the actual starting time of the preceding vehicle is t1, the revised estimated starting time t' of the target vehicle can be obtained using ΔL, V and t1. If the actual starting time of the preceding vehicle is earlier than expected, the estimated starting time of the target vehicle is revised to be advanced; otherwise, it is delayed.

[0069] In the embodiment of the present application, the actual starting time of each preceding vehicle may be used for correction, or the actual starting time of some preceding vehicles may be selected for correction, for example, correction may be performed every two preceding vehicles, etc. In addition to using the preceding vehicle for correction, the estimated starting time of the target vehicle may be further corrected in combination with the actual starting time of vehicles in other lanes, or the estimated starting time of the target vehicle may not be corrected.

[0070] The second approach: model-based approach

[0071] In this method, the queue position of the target vehicle in front of the target traffic light at the current intersection, the timing data corresponding to the target traffic light, and the environmental information can be input into the start time estimation model to obtain the start estimated time of the target vehicle output by the start time estimation model. Among them, the time estimation model can adopt a linear regression model, a deep learning model, etc.

[0072] As one possible implementation method, the above-mentioned environmental information may include the characteristics of the current intersection.

[0073] Among them, the characteristics of the current intersection may include at least one of intersection structure characteristics, intersection scale characteristics, intersection control characteristics, and whether there are special geographical objects. Intersection structure characteristics may include intersection shape (such as crossroads, T-shaped intersections, Y-shaped intersections, etc.), road type (expressway, main road, secondary road, etc.), etc. Intersection scale characteristics may be the number of lanes at the intersection, intersection width, etc. Intersection control characteristics may be such as right turn signal control, ramp control, etc. Whether there are special geographical objects can be reflected in whether there are bus stops, taxi stops, schools, gas stations, etc. within a preset range from the intersection.

[0074] As another achievable manner, the above-mentioned environmental information may include surrounding vehicle characteristics corresponding to the target vehicle.

[0075] By collecting and analyzing historical data, including changes in traffic lights at intersections, queue distances of vehicles, and starting times, we found that when the queue distance is short, the queue distance is the dominant factor affecting the vehicle starting time. When the queue distance is long, the relationship between the starting time and the queue distance becomes chaotic. After analysis, it was determined that the deviation was mainly caused by the influence of the surrounding vehicle characteristics, so the surrounding vehicle characteristics need to be introduced in the model prediction to ensure the accuracy of the prediction.

[0076] The surrounding vehicle features mainly include the trajectory data of the surrounding vehicles. Since the front vehicle and the vehicles in the adjacent lanes may affect the start of the target vehicle, the surrounding vehicles can include the front vehicle or the vehicles in the adjacent lanes. The trajectory data of the surrounding vehicles can reflect the start-stop characteristics, position characteristics, speed characteristics, acceleration characteristics, etc. of the surrounding vehicles.

[0077] In this implementation, the start time estimation model is used to predict the start time of the target vehicle. Introducing the characteristics of the intersection into the model can effectively solve the prediction deviation caused by specific intersections and improve the prediction accuracy. Introducing the surrounding vehicle characteristics into the model can effectively solve the prediction deviation caused by long queue distance or abnormal conditions of other vehicles and improve the prediction accuracy.

[0078] In addition to the above two methods, other methods can also be used to predict the estimated starting time.

[0079] The above two methods provided in the embodiments of the present application can be implemented on the user side or on the server side. For example, for the first method, the method based on the conduction velocity has lower requirements on the computing performance and can be set to be implemented on the user side, so that the positioning information obtained by the user side is more real-time, and the user side can further combine the collected images, videos and other multimodal information of the surrounding environment to achieve accurate prediction. For the second method, the model-based method has higher requirements on the computing performance and can be set to be implemented on the server side. This method can use more feature information to achieve accurate prediction. If the model scale is small or the computing performance of the user terminal is strong enough, it can also be implemented on the user side.

[0080] In addition, the embodiments of the present application may also selectively adopt the above two methods for different scenarios. For example, the above first method is adopted for short-distance scenarios, that is, if the queue distance of the target vehicle is less than or equal to the preset distance threshold, the above first method is adopted to predict the estimated start time. For another example, the above second method is adopted for long-distance scenarios, that is, if the queue distance of the target vehicle is greater than the preset distance threshold, the above second method is adopted to predict the estimated start time.

[0081] In addition to adopting one of the above two methods, the above two methods can also be combined. For example, the estimated start time predicted by the above two methods is processed by averaging or taking the median value, and the obtained value is used as the estimated start time.

[0082] The above step 205, namely "generating start reminder information based on the estimated start time", is described in detail below in conjunction with an embodiment.

[0083] If this process is implemented by the server, the server can send the generated start reminder information to the user, and the user will display and broadcast it. If this process is implemented by the user, the user can display or broadcast the generated start reminder information.

[0084] In an embodiment of the present application, the user terminal can display or broadcast the start reminder information when the estimated start time is reached. The start reminder information can also be displayed or broadcast at a time before the estimated start time (referred to as the prompt time in the embodiment of the present application). The difference between the prompt time and the estimated start time is less than or equal to the preset duration, for example, the start reminder information is displayed or broadcast 1 second before the estimated start time t is reached. In this way, the driving user can start in time after receiving the start reminder information.

[0085] The purpose of the start reminder information is to remind the driving user to start, and the words used may be such as "the vehicle can start", "pay attention to the vehicle starting", "it can start safely", etc.

[0086] Furthermore, in the embodiment of the present application, in addition to realizing the vehicle start reminder, other driving suggestions can also be provided to the target vehicle. For example, the estimated start time is used to predict the time when the target vehicle passes through at least one intersection; and driving suggestion information is generated based on the time when the target vehicle passes through at least one intersection and the timing data corresponding to the traffic lights at at least one intersection.

[0087] The above driving advice information can be used for traffic light guidance, such as providing advice on whether the current intersection can be passed.

[0088] As one of the feasible methods, the estimated start time and the start acceleration can be used to predict the time when the target vehicle passes the current intersection; then, based on the time when the target vehicle passes the current intersection and the timing data corresponding to the target traffic light, it is judged whether the target vehicle can pass the current intersection after starting; based on the judgment result, driving suggestion information is generated, and the driving suggestion information may include information on whether the target vehicle can pass the current intersection with a green light.

[0089] Usually, after a vehicle starts, it will start at a relatively uniform acceleration and maintain this acceleration. Therefore, the estimated start time and the start acceleration can be used to predict the time when the target vehicle passes the current intersection. The start acceleration can be an empirical value or an experimental value, for example, the historical trajectory of the vehicle at the intersection is collected and the start acceleration is determined based on the historical trajectory.

[0090] If the predicted time for passing the current intersection is before the time when the target traffic light changes from green to red, it is judged that the target vehicle can pass the current intersection after starting, and driving suggestion information such as "Follow the car in front closely and hope to pass the green light" and "Stay focused and you can pass the intersection smoothly" can be generated. This can remind users to stay focused, thereby improving traffic efficiency.

[0091] If the predicted time for passing the current intersection is after the time when the target traffic light turns from green to red, it is determined that the target vehicle cannot pass the current intersection after starting, and no additional driving suggestion information may be generated. Alternatively, driving suggestion information such as "pay attention to the distance between vehicles, the intersection cannot be passed smoothly" or "drive slowly, the intersection cannot be passed smoothly" may be generated.

[0092] Furthermore, in some scenarios, the distance between the current intersection and the next intersection may be very short (usually a few hundred meters), so the traffic conditions at the next intersection usually affect the behavior of drivers. After analysis, it was found that regardless of whether the traffic light at the next intersection is red or green, drivers basically accelerate first and then decelerate between the current intersection and the next intersection. Therefore, by collecting the historical trajectory of the vehicle between the current intersection and the next intersection, an acceleration change curve can be fitted.

[0093] Based on this, the estimated start time of the target vehicle, the preset acceleration change curve and the position of the next intersection can be used to predict the time when the target vehicle passes the next intersection; based on the time when the target vehicle passes the next intersection and the timing data corresponding to the traffic light at the next intersection, it is judged whether the target vehicle can pass the next intersection after starting; based on the judgment result, driving advice information is generated. The driving advice information may include information on whether the next intersection can be passed with a green light.

[0094] If the predicted time to pass the next intersection is before the time when the target traffic light at the next intersection changes from green to red, it is determined that the target vehicle can pass the next intersection with a green light, and driving advice information such as "maintain the speed, the next intersection can pass with a green light" and "accelerate appropriately, the next intersection is expected to pass with a green light" can be generated. In this way, the driver can maintain the current driving status and quickly pass the current section and the next intersection, thereby improving driving efficiency and driving experience.

[0095] If the predicted time to pass the next intersection is after the time when the target traffic light at the next intersection turns from green to red, it is determined that the target vehicle cannot pass the next intersection with a green light, and driving advice information such as "control the speed, you will still have to wait at the next intersection" can be generated. In this way, when the driver learns that the next intersection cannot be passed with a green light, he or she will appropriately reduce the speed and avoid blind acceleration, thereby improving driving safety and driving experience.

[0096] In an embodiment of the present application, the above-mentioned start reminder information and driving advice information (such as information on whether the current intersection can be passed with a green light and / or information on whether the next intersection can be passed with a green light) can be sent together, or they can be sent one after another. For example, the start reminder and driving advice information can be sent together to the user terminal when the estimated start time is reached, and the user terminal continuously plays the start reminder information and driving advice information at the estimated start time, such as "the vehicle can start, follow the vehicle in front closely, the intersection can be passed with a green light, accelerate appropriately, and the next intersection is expected to be passed with a green light." In this way, even if the driving user is in the middle or rear section of the queue, he can get a start reminder in time, and get advice on passing the lights at the current intersection and the next intersection, so as to adopt an appropriate driving strategy.

[0097] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0098] Figure 5 A schematic block diagram of a navigation prompt device provided in an embodiment of the present application, which can be arranged in Figure 1 The server side in the architecture shown can also be set on the user side. Figure 5 As shown, the device 500 includes: a location determination unit 502, a first estimation unit 503 and a reminder generation unit 504, and may further include a data acquisition unit 501, a second estimation unit 505 and a third estimation unit 506. The main functions of each component unit are as follows:

[0099] The position determination unit 502 is configured to determine the queue position of the target vehicle in front of the target traffic light at the current intersection based on the road network data.

[0100] The first estimation unit 503 is configured to determine the estimated start time of the target vehicle according to the queue position and traffic light timing data.

[0101] The reminder generating unit 504 is configured to generate start reminder information based on the estimated start time.

[0102] Furthermore, the data acquisition unit 501 may be configured to acquire road network data and traffic light timing data.

[0103] If the device is set at the server side, the data acquisition unit 501 can acquire the road network data and traffic light timing data from the storage space. The road network data and traffic light timing data stored in the storage space can be pre-mined or acquired from other devices or databases.

[0104] If the device is set at the client, the data acquisition unit 501 can obtain the corresponding road network data and traffic light timing data within the nearby location range from the server, and can also obtain the road network data and traffic light timing data within the corresponding range of the navigation route from the server. The nearby location range can be within a preset distance range from the current location of the user. In addition to this method, other methods can also be used, such as the user pre-acquiring the road network data and traffic light timing data within the city, district, county, etc.

[0105] As one of the achievable methods, the position determination unit 502 can be specifically configured as follows: if it is determined that a vehicle is located in a lane associated with a target traffic light and is stationary, the vehicle is taken as a target vehicle, and the queue position of the vehicle in front of the target traffic light at the current intersection is determined, wherein the lane associated with the target traffic light is determined based on road network data.

[0106] As one of the achievable methods, the position determination unit 502 may determine that the vehicle is located in a lane associated with a target traffic light based on the vehicle's trajectory data and / or navigation data; if the vehicle is stationary, then it is determined that the vehicle is located in a lane associated with the target traffic light and is stationary.

[0107] Among them, the position determination unit 502 can be specifically configured as follows: if it is determined that the vehicle is located in the lane associated with the target traffic light and is in a stationary state, and it is determined based on the navigation data of the vehicle that the driving intention of the vehicle meets the preset intention, then the vehicle is taken as the target vehicle, where the preset intention includes turning left or going straight through the current intersection.

[0108] As one of the feasible ways, the first estimation unit 503 can be specifically configured as follows: according to the queue position, determine the queue distance corresponding to the target vehicle, the queue distance being the distance between the target vehicle and the stop line of the current intersection; according to the time when the state of the target traffic light changes to the green light, the queue distance and the preset transmission speed, determine the estimated starting time of the target vehicle, wherein the time when the state of the target traffic light changes to the green light is determined according to the traffic light timing data.

[0109] Furthermore, before generating the start reminder information based on the estimated start time, the first estimating unit 503 may also be configured to: determine other vehicles before the target vehicle; and correct the estimated start time of the target vehicle using the actual start time of the other vehicles.

[0110] As another achievable method, the first estimation unit 503 may be specifically configured as follows: inputting the queue position of the target vehicle in front of the target traffic light at the current intersection, the timing data corresponding to the target traffic light, and the environmental information into a starting time estimation model, and obtaining the starting estimated time of the target vehicle output by the starting time estimation model; wherein the environmental information includes the characteristics of the current intersection and / or the surrounding vehicle characteristics corresponding to the target vehicle.

[0111] As one of the feasible ways, the reminder generation unit 504 may be specifically configured as follows: in response to reaching the estimated starting time, or in response to the prompt time before reaching the estimated starting time, the difference between the prompt time and the estimated starting time is less than or equal to a preset duration, and the user terminal displays or broadcasts the starting reminder information.

[0112] Furthermore, the second estimation unit 505 can be configured to: use the estimated starting time to predict the time it takes for the target vehicle to pass through at least one intersection; and generate driving advice information based on the time it takes to pass through at least one intersection and the timing data corresponding to the traffic lights at the at least one intersection.

[0113] As one of the achievable ways, the at least one intersection includes the current intersection, and the second estimation unit 505 can be specifically configured to: predict the time when the target vehicle passes the current intersection by using the estimated start time and the start acceleration; or,

[0114] The at least one intersection includes a next intersection of the current intersection, and the second estimation unit 505 can be specifically configured to: use the estimated start time, the preset acceleration change curve and the position of the next intersection to predict the time when the target vehicle passes the next intersection.

[0115] As another achievable method, the second estimation unit 505 can be specifically configured to: determine whether the target vehicle can pass through the at least one intersection with a green light after starting, based on the time of passing through the at least one intersection and the timing data corresponding to the traffic lights at the at least one intersection; and generate driving advice information based on the judgment result.

[0116] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system or device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The system and device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0117] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0118] In addition, an embodiment of the present application further provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the steps of any one of the methods in the aforementioned method embodiments are implemented.

[0119] And an electronic device, comprising:

[0120] one or more processors; and

[0121] A memory associated with the one or more processors, the memory being used to store program instructions, wherein the program instructions, when read and executed by the one or more processors, execute the steps of the method described in any one of the aforementioned method embodiments.

[0122] The present application also provides a computer program product, including a computer program, which implements the steps of any one of the methods in the aforementioned method embodiments when executed by a processor.

[0123] in, Figure 6 The architecture of the electronic device is shown as an example, which may include a processor 610, a video display adapter 611, a disk drive 612, an input / output interface 613, a network interface 614, and a memory 620. The processor 610, the video display adapter 611, the disk drive 612, the input / output interface 613, the network interface 614, and the memory 620 may be communicatively connected via a communication bus 630.

[0124] Among them, the processor 610 can be implemented by a general-purpose CPU, a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., to execute relevant programs to implement the technical solutions provided in this application.

[0125] The memory 620 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 620 can store an operating system 621 for controlling the operation of the electronic device 600, and a basic input and output system (BIOS) 622 for controlling the low-level operation of the electronic device 600. In addition, a web browser 623, a data storage management system 624, and a navigation prompt device 500, etc. can also be stored. The above-mentioned navigation prompt device 500 can be an application program that specifically implements the operations of the aforementioned steps in the embodiment of the present application. In short, when the technical solution provided by the present application is implemented by software or firmware, the relevant program code is stored in the memory 620 and is called and executed by the processor 610.

[0126] The input / output interface 613 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0127] The network interface 614 is used to connect to a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (such as USB, network cable, etc.) or a wireless mode (such as mobile network, WIFI, Bluetooth, etc.).

[0128] The bus 630 comprises a pathway for transmitting information between the various components of the device (eg, the processor 610, the video display adapter 611, the disk drive 612, the input / output interface 613, the network interface 614, and the memory 620).

[0129] It should be noted that, although the above device only shows a processor 610, a video display adapter 611, a disk drive 612, an input / output interface 613, a network interface 614, a memory 620, a bus 630, etc., in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include components necessary for implementing the solution of the present application, and does not necessarily include all the components shown in the figure.

[0130] It can be known from the description of the above implementation methods that those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be essentially or partly contributed to the prior art in the form of a computer program product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present application or certain parts of the embodiments.

[0131] The technical solution provided by the present application is described in detail above. The principle and implementation method of the present application are described in detail using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for those skilled in the art, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as limiting the present application.

Claims

1. A navigation prompt method in a traffic light scenario, characterized in that: The method comprises: According to the road network data, determine the queue position of the target vehicle in front of the target traffic light at the current intersection; Determining the estimated starting time of the target vehicle based on the queue position and traffic light timing data; Based on the estimated start time, start reminder information is generated.

2. The method according to claim 1, characterized in that Determining the queue position of the target vehicle in front of the target traffic light at the current intersection includes: If it is determined that the vehicle is located in the lane associated with the target traffic light and is stationary, the vehicle is taken as the target vehicle and the queue position of the vehicle in front of the target traffic light at the current intersection is determined, wherein the lane associated with the target traffic light is determined based on the road network data.

3. The method according to claim 2, characterized in that The determining that the vehicle is located in the lane associated with the target traffic light and is stationary includes: Determining, based on the trajectory data and / or navigation data of the vehicle, that the vehicle is located in a lane associated with the target traffic light; If the vehicle is in a stationary state, it is determined that the vehicle is located in a lane associated with the target traffic light and is in a stationary state.

4. The method according to claim 2, characterized in that: If it is determined that the vehicle is located in the lane associated with the target traffic light and is stationary, taking the vehicle as the target vehicle includes: If it is determined that the vehicle is located in the lane associated with the target traffic light and is stationary, and if it is determined based on the navigation data of the vehicle that the vehicle's driving intention meets the preset intention, the vehicle is taken as the target vehicle, where the preset intention includes turning left or going straight through the current intersection.

5. The method according to claim 1, characterized in that: Determining the estimated starting time of the target vehicle based on the queue position and traffic light timing data includes: Determine the queue distance corresponding to the target vehicle according to the queue position, the queue distance being the distance between the target vehicle and the stop line of the current intersection; The estimated starting time of the target vehicle is determined based on the time when the state of the target traffic light changes to green, the queuing distance and the preset transmission speed, wherein the time when the state of the target traffic light changes to green is determined based on the traffic light timing data.

6. The method according to claim 5, characterized in that Before generating the start reminder information based on the estimated start time, the method further includes: Determining other vehicles ahead of the target vehicle; The estimated starting time of the target vehicle is corrected by using the actual starting time of the other vehicles.

7. The method according to claim 1, characterized in that Determining the estimated starting time of the target vehicle based on the queue position and traffic light timing data includes: Inputting the queue position, the timing data corresponding to the target traffic light and the environmental information into a starting time estimation model, and obtaining the starting estimated time of the target vehicle output by the starting time estimation model; The environmental information includes the characteristics of the current intersection and / or the surrounding vehicle characteristics corresponding to the target vehicle.

8. The method according to any one of claims 1 to 7, characterized in that The method further comprises: In response to reaching the estimated start time, or in response to reaching the prompt time before the estimated start time, the difference between the prompt time and the estimated start time is less than or equal to a preset duration, and the user terminal displays or broadcasts the start reminder information.

9. The method according to any one of claims 1 to 7, characterized in that The method further comprises: Using the estimated start time, predicting the time it takes for the target vehicle to pass through at least one intersection; Driving suggestion information is generated according to the time of passing through the at least one intersection and the timing data corresponding to the traffic lights of the at least one intersection.

10. The method according to claim 9, characterized in that The at least one intersection includes the current intersection, and the using the estimated start time to predict the time when the target vehicle passes through the at least one intersection includes: using the estimated start time and the start acceleration to predict the time when the target vehicle passes through the current intersection; or The at least one intersection includes a next intersection of the current intersection, and using the estimated start time to predict the time when the target vehicle passes through at least one intersection includes: using the estimated start time, a preset acceleration change curve and the position of the next intersection to predict the time when the target vehicle passes through the next intersection.

11. The method according to claim 9, characterized in that Generating driving advice information according to the time of passing through at least one intersection and the timing data corresponding to the traffic light at the at least one intersection includes: Determining whether the target vehicle can pass through the at least one intersection with a green light after starting, based on the time of passing through the at least one intersection and the timing data corresponding to the traffic lights at the at least one intersection; Based on the judgment results, driving advice information is generated.

12. The method according to any one of claims 1 to 7, characterized in that The method is executed by a user terminal, and the method further includes: Acquire from the server in real time the road network data and traffic light timing data within a preset distance range of the user terminal; or Acquire the road network data and traffic light timing data within the corresponding range of the navigation route of the user terminal from the server terminal; or, Obtain road network data and traffic light timing data within the area where the user terminal is located from the server side.

13. A navigation prompt device in a traffic light scene, characterized in that: The device comprises: A position determination unit is configured to determine a queue position of the target vehicle in front of a target traffic light at a current intersection based on the road network data; A first estimation unit is configured to determine an estimated start time of the target vehicle according to the queue position and traffic light timing data; The reminder generating unit is configured to generate start reminder information based on the estimated start time.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 12 are implemented.

15. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 12 are implemented.

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