Navigation prompting method in traffic light scenario and corresponding apparatus

Abstract

A navigation prompting method in a traffic light scenario and a corresponding apparatus, relating to the technical field of navigation. The method comprises: on the basis of road network data, determining a queuing position of a target vehicle at a target traffic light at a current intersection (201); on the basis of the queuing position and traffic light timing data, determining an estimated motion initiation time of the target vehicle (203); and, on the basis of the estimated motion initiation time, generating motion initiation reminder information (205). Accurate navigation prompts can be provided to a driving user, facilitating prompt motion initiation by the driving user, and improving driving safety and traffic efficiency. In addition, such a mode does not require high dependence on perception capabilities, and can be implemented without a sensing device and a sensing system, resulting in low implementation costs.

Description

Navigation prompts and corresponding devices in traffic light scenarios

[0001] This disclosure claims priority to Chinese Patent Application No. 202411986435.8, filed with the Chinese Patent Office on December 31, 2024, entitled "Navigation prompting method and corresponding device in traffic light scenario", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This disclosure relates to the field of navigation technology, and in particular to a navigation prompting method and corresponding device in a traffic light scenario. Background Technology

[0003] With the acceleration of urbanization, traffic congestion and other traffic problems are becoming increasingly prominent, especially on urban surface roads. Navigation prompts, as an indispensable part of modern navigation systems, provide drivers with real-time guidance on routes, directions, traffic conditions, and other important information through sound, vision, or touch. Traffic light scenarios are among the most frequently encountered by drivers, and how to provide accurate navigation prompts during these scenarios has a significant impact on driving safety and traffic efficiency, making it a pressing issue that needs to be addressed. Summary of the Invention

[0004] In view of this, this disclosure provides a navigation prompting method and corresponding device in traffic light scenarios, which can provide accurate navigation prompts to drivers and improve driving safety and traffic efficiency.

[0005] This disclosure provides the following solutions:

[0006] Firstly, a navigation prompt method for traffic light scenarios is provided, the method comprising:

[0007] Based on road network data, determine the queuing position of the target vehicle in front of the target traffic light at the current intersection;

[0008] Based on the queue position and traffic light timing data, the estimated start time of the target vehicle is determined;

[0009] Based on the estimated start time, a start reminder message is generated.

[0010] Secondly, a navigation prompting device for traffic light scenarios is provided, the device comprising:

[0011] The location determination unit is configured to determine the queuing position of the target vehicle in front of the target traffic light at the current intersection based on road network data.

[0012] The first estimation unit is configured to determine the estimated start time of the target vehicle based on the queue position and traffic light timing data.

[0013] The reminder generation unit is configured to generate start reminder information based on the estimated start time.

[0014] Thirdly, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps of the method described in the first aspect above.

[0015] Fourthly, an electronic device is provided, comprising:

[0016] One or more processors; and

[0017] A memory associated with the one or more processors, the memory being used to store program instructions that, when read and executed by the one or more processors, perform the steps of the method described in the first aspect above.

[0018] Fifthly, a computer program product is provided, comprising a computer program that, when executed by a processor, performs the steps of the method described in the first aspect.

[0019] Based on the specific embodiments provided in this disclosure, the following technical effects are disclosed:

[0020] This disclosure utilizes road network data and traffic light timing data to determine the estimated start time of target vehicles queuing at a target traffic light (red light). Based on this estimated start time, start reminder information can be generated, providing accurate navigation prompts to drivers, facilitating timely start-up, and improving driving safety and traffic efficiency. Furthermore, this method does not heavily rely on sensing capabilities and can be implemented without sensors or perception systems, resulting in low implementation costs. Attached Figure Description

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

[0022] Figure 1 is a system architecture diagram applicable to the embodiments of this disclosure;

[0023] Figure 2 is a flowchart of the navigation prompt method in a traffic light scenario provided in an embodiment of this disclosure;

[0024] Figure 3 is a schematic diagram of an intersection provided in an embodiment of this disclosure;

[0025] Figure 4 is a schematic diagram of the target vehicle's status at various times provided in the embodiments of this disclosure;

[0026] Figure 5 is a schematic block diagram of the navigation prompting device provided in an embodiment of this disclosure;

[0027] Figure 6 is a schematic block diagram of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0028] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure are within the scope of protection of this disclosure.

[0029] The terminology used in the embodiments of this disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of this disclosure. The singular forms “a,” “the,” and “the” as used in the embodiments of this disclosure and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0030] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0031] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0032] Currently, some navigation prompts exist for traffic light scenarios, but most are for traffic light changes (i.e., light change reminders). For example, 3-5 seconds before the traffic light changes from red to green, a navigation prompt such as "The red light is about to turn green, prepare to start" is given to the driver. However, this type of navigation prompt is only useful for vehicles waiting to start, while drivers further back in the queue find it difficult to determine when to start. To solve the problem of start-up prompts, some technologies equip vehicles with sensors such as cameras and radar to detect the start of vehicles ahead and generate start-up guidance information. However, this method is highly dependent on perception capabilities, requiring sensing devices and perception systems to implement, which is costly.

[0033] In view of this, this disclosure provides a new approach. To facilitate understanding of this disclosure, the system architecture on which this disclosure is based is first described. Figure 1 illustrates an exemplary system architecture to which embodiments of this disclosure can be applied. As shown in Figure 1, this system architecture may include: a client running on a user terminal and a server.

[0034] The server provides map-related services to users, such as providing map data, navigation services, and traffic information (including road conditions). This disclosure focuses on navigation services, where the server provides navigation services to users. Drivers request navigation services through a client while driving and receive navigation prompts through their client. These prompts can be displayed or broadcast to drivers in various ways, such as text, voice, or icons. For ease of description, subsequent embodiments will use voice navigation as an example.

[0035] The user terminal can include, but is not limited to, smart mobile terminals and wearable devices. Smart mobile devices can include, for example, mobile phones, tablets, PDAs (Personal Digital Assistants), and connected car terminals. Wearable devices can include, for example, smartwatches, smart glasses, smart bracelets, VR (Virtual Reality) devices, AR (Augmented Reality) devices, and mixed reality devices (i.e., devices that support both virtual and augmented reality).

[0036] The aforementioned user terminal can be a client running on a user terminal, a mini-program, or a web application running through a browser.

[0037] As one possible approach, the server can generate navigation prompts using the methods provided in this embodiment and send the generated navigation prompts to the user terminal. The user terminal then broadcasts the prompts to the driver in a manner such as voice.

[0038] As another possible approach, the user terminal can use the method provided in the embodiments of this disclosure to generate navigation prompts and display or play the navigation prompts to the user. Specific methods will be detailed in subsequent embodiments.

[0039] The servers mentioned above can be standalone servers, server clusters, or cloud servers. Cloud servers, also known as cloud computing servers or cloud hosts, are a type of hosting product within the cloud computing service system, designed to address the shortcomings of traditional physical hosts and Virtual Private Servers (VPS) services, such as high management difficulty and weak service scalability.

[0040] It should be understood that the number of servers, client terminals, and user terminals shown in Figure 1 is merely illustrative. Depending on implementation needs, any number of servers, client terminals, and user terminals can be used.

[0041] Figure 2 is a flowchart of a navigation prompt method in a traffic light scenario provided by an embodiment of this disclosure. This method can be executed by the server in the system shown in Figure 1 or by the user terminal. As shown in Figure 2, the method may include the following steps:

[0042] Step 201: Based on the road network data, determine the queuing position of the target vehicle in front of the target traffic light at the current intersection.

[0043] Step 203: Based on the queue position and traffic light timing data mentioned above, determine the estimated start time of the target vehicle.

[0044] Step 205: Generate start-up reminder information based on the estimated start-up time.

[0045] As can be seen from the above process, this disclosure utilizes road network data and traffic light timing data to determine the estimated start time of target vehicles queuing at a target traffic light. Based on this estimated start time, start reminder information can be generated, thereby providing accurate navigation prompts to drivers, facilitating timely start-up, and improving driving safety and traffic efficiency. Furthermore, this method does not heavily rely on sensing capabilities and can be implemented without sensing devices and perception systems, resulting in low implementation costs.

[0046] The following describes in detail each step of the above process and the effects that can be further produced, with reference to the embodiments. It should be noted that the terms "first" and "second" used in this disclosure do not have limitations in terms of size, order, or quantity, but are only used to distinguish them in name. For example, "first time" and "second time" are only used to distinguish the passage time of the two intersections in name.

[0047] First, the above step 201, namely "determining the queuing position of the target vehicle in front of the target traffic light at the current intersection based on road network data", will be described in detail with reference to the embodiments.

[0048] Traffic lights, also known as red and green 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.

[0049] Road network data refers to road network data, including the geometric features, attribute information, and connectivity of each road (typically a road contains multiple lanes). The connectivity of roads can include intersection information, such as the intersections between roads forming a specific junction. Road attribute information can include traffic light information at the corresponding intersection, road type, road grade, etc. With the rapid development of data mining technology, lane-level road network data has emerged, enabling the extraction of lane information for each road from lane-level road network data.

[0050] Traffic light timing data refers to a series of time parameters used to control and optimize traffic light operation. These parameters typically determine when the traffic lights illuminate red, green, and yellow, as well as the duration of each color. For any traffic light symbol, its corresponding timing data can be found in the traffic light timing data.

[0051] Road network data and traffic light timing data can be pre-mined using specific algorithms, obtained through interfaces provided by traffic management departments, smart city platforms, etc., or obtained through other means. This disclosure does not impose any special restrictions on these.

[0052] The obtained road network data and traffic light timing data can be stored on a server. If the generation of navigation prompts provided in this embodiment is performed by the server, the server can obtain the already stored road network data and traffic light timing data.

[0053] If the navigation prompts provided in this embodiment are generated by the user terminal, the user terminal can obtain road network data and traffic light timing data within the nearby location range from the server, or it can obtain road network data and traffic light timing data within the range corresponding to the navigation route from the server. The nearby location range can be a preset distance range from the user terminal's current location. Besides this method, other methods can also be used, such as the user terminal pre-obtaining road network data and traffic light timing data within its city, district, or other administrative division.

[0054] In this step, the status of the target traffic light can be determined based on the traffic light timing data. The target traffic light can be any traffic light at any intersection.

[0055] In this embodiment of the disclosure, "in front of the target traffic light" refers to a location where queuing may occur due to the influence of the target traffic light, which may specifically include the lane associated with the target traffic light.

[0056] In this step, the lanes associated with the target traffic light at the intersection can be determined based on 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 both left-turn and straight-ahead signals, then the lanes affected by the target traffic light are the left-turn lane and the straight-ahead lane, as shown in Figure 3. Figure 3 is a schematic diagram of an intersection provided by an embodiment of this disclosure. In some cases, the lanes affected by the target traffic light may also include a left-turn / straight-ahead merging lane. After a vehicle travels to the lane affected by the target traffic light, continuous monitoring of the vehicle begins. If queuing occurs, the vehicle's queuing position at the target traffic light at the intersection is determined. It should be noted that the aforementioned left-turn lanes, straight-ahead lanes, and left-turn / straight-ahead merging lanes are merely examples and do not represent all possible situations.

[0057] One possible approach is to identify a vehicle as a stationary vehicle located in the lane associated with a target traffic light, and then determine its queuing position at the target traffic light at the current intersection. Typically, a stationary vehicle in the lane associated with the target traffic light is the vehicle waiting for the red light, referred to as the target vehicle in this embodiment. This group of target vehicles includes vehicles already queuing when the target traffic light is red, as well as vehicles queuing after the target traffic light changes from red to green because the queue has not yet dispersed. In other words, vehicles in a queuing state require a start-up reminder. This method can accurately identify target vehicles requiring start-up reminders using road network data, predicting and reminding only the target vehicles of their estimated start-up time, thus avoiding disturbance to other vehicles.

[0058] When determining whether a vehicle is located in the lane associated with a target traffic light, vehicle trajectory data can be used. Vehicle trajectory data reflects the vehicle's position at consecutive points in time, as well as its speed, acceleration, and other state information. Therefore, determining whether a target vehicle is located in the lane associated with a target traffic light using vehicle trajectory data is generally more accurate than simply using the target vehicle's location data.

[0059] If lane-level trajectory data cannot be obtained, the vehicle's location information and whether it is located in the lane associated with the target traffic light can be determined based on the vehicle's navigation data.

[0060] If a vehicle is determined to be stationary in the lane associated with the target traffic light, navigation data can be further used to differentiate vehicles in certain scenarios. Specifically, based on the vehicle's navigation data, if the vehicle's driving intention matches a preset intention, that vehicle can be designated as the target vehicle. Preset intentions can include vehicles turning left or going straight through the current intersection. For the target vehicle, the navigation data reflects not only its location but also its driving intention. In intersection scenarios, the driving intention primarily refers to the method the vehicle intends to use to pass through the intersection, including going straight, turning left, turning right, or making a U-turn. In some scenarios, turning right and making a U-turn are less affected by traffic lights. However, going straight and turning left are often affected by traffic lights, causing queues. Therefore, this method can more accurately identify vehicles requiring start-up reminders as target vehicles, improving user experience and avoiding disturbance to other vehicles that do not need reminders.

[0061] The following describes step 203, namely "determining the estimated start time of the target vehicle based on the queue position and traffic light timing data," in detail with reference to the embodiments.

[0062] In typical traffic light scenarios, when the traffic light changes from red to green, vehicles in a queue start moving sequentially and gradually accelerate through the intersection. The starting time of a vehicle is related to its position and the time of the light change (i.e., the time it takes for the light to turn green). In this disclosure embodiment, the following two methods may be used, but are not limited to:

[0063] The first method: based on conduction speed

[0064] In this method, the queue distance corresponding to the target vehicle is first determined based on the queue position of the target vehicle in front of the target traffic light at the current intersection. The queue distance is the distance between the target vehicle and the stop line of the current intersection. Then, the estimated start time of the target vehicle is determined based on the time it takes for the target traffic light to turn green, the queue distance, and the preset transmission speed.

[0065] Figure 4 is a schematic diagram of the target vehicle's state at various times according to an embodiment of this disclosure. As shown in Figure 4, at time T1, the target traffic light is red, and the gray vehicle is assumed to be the target vehicle, with a corresponding queue distance of L. At time T2, the target traffic light turns green, and the vehicle at the head of the queue begins to move. According to the queue dissipation law, each vehicle in the queue starts moving sequentially with a starting acceleration. Assuming the transmission speed of the vehicle's start is V, the queue dissipation time of the target vehicle can be determined based on L and V. Based on T2 and the queue dissipation time, the starting time T3 of the target vehicle (referred to as the estimated starting time in this embodiment of the disclosure) can be determined.

[0066] The transmission speed V can be taken as an empirical or experimental value. For example, the historical trajectory of a vehicle at an intersection can be collected, and the transmission speed at the start can be determined based on the historical trajectory.

[0067] This implementation method is computationally simple and efficient. When it is necessary to predict the start-up time of a large number of target vehicles, it can effectively reduce the performance pressure on computing resources and achieve higher prediction efficiency.

[0068] Furthermore, since the actual starting time of each vehicle in front of the target vehicle (i.e., other vehicles in the queue before the target vehicle) may deviate due to factors such as the driver's reaction time and the surrounding environment, the estimated starting time of the target vehicle can be corrected by using the actual starting time of the vehicles in front before the estimated starting time is displayed or broadcast on the user's end, thereby further improving the accuracy of the estimated starting time.

[0069] For example, assuming the difference between the queue distance of a preceding vehicle and the queue distance of the target vehicle is ΔL, and the actual start time of the preceding vehicle is t1, then the corrected estimated start time t' of the target vehicle can be obtained using ΔL, V, and t1. If the actual start time of the preceding vehicle is earlier than expected, then the estimated start time of the target vehicle is adjusted to be earlier; otherwise, it is adjusted to be later.

[0070] In this embodiment, the actual start time of each preceding vehicle can be used for correction, or the actual start time of a subset of preceding vehicles can be selected for correction, such as making a correction every two preceding vehicles, and so on. In addition to using preceding vehicles for correction, the estimated start time of the target vehicle can also be corrected by combining the actual start times of vehicles in other lanes, or the estimated start time of the target vehicle can be left uncorrected.

[0071] The second approach: model-based approach

[0072] In this method, the target vehicle's queuing position at the target traffic light at the current intersection, the timing data of the target traffic light, and environmental information can be input into the start-time prediction model to obtain the target vehicle's start-time prediction output by the start-time prediction model. The time prediction model can employ linear regression, deep learning, or other similar models.

[0073] As one possible approach, the aforementioned environmental information may include the characteristics of the current intersection.

[0074] The characteristics of the current intersection can include at least one of the following: intersection structure characteristics, intersection size characteristics, intersection control characteristics, and the presence of special geographical features. Intersection structure characteristics can include intersection shape (e.g., crossroads, T-junctions, Y-junctions, etc.) and road type (expressway, arterial road, secondary arterial road, etc.). Intersection size characteristics can include the number of lanes and intersection width. Intersection control characteristics can include right-turn signal control and ramp control. The presence of special geographical features can be reflected in whether there are bus stops, taxi stands, schools, gas stations, etc., within a preset range of the intersection.

[0075] As another possible approach, the aforementioned environmental information may include the surrounding vehicle features corresponding to the target vehicle.

[0076] By collecting and analyzing historical data, including traffic light changes at intersections, vehicle queue distances, and start times, the following pattern was observed: with shorter queue distances, queue distance is the dominant factor influencing vehicle start time. However, with longer queue distances, the relationship between start time and queue distance becomes inconsistent. Analysis revealed that the bias is primarily caused by the influence of surrounding vehicle characteristics; therefore, surrounding vehicle characteristics need to be incorporated into the model prediction to ensure accuracy.

[0077] The characteristics of surrounding vehicles mainly include their trajectory data. Since vehicles in front and in adjacent lanes may affect the starting of the target vehicle, surrounding vehicles can include those in front or adjacent lanes. The trajectory data of surrounding vehicles can reflect their starting and stopping characteristics, position characteristics, speed characteristics, acceleration characteristics, etc.

[0078] In this implementation, a start-time prediction model is used to predict the estimated start time of the target vehicle. Incorporating intersection features into the model effectively addresses prediction biases caused by specific intersections, improving prediction accuracy. Incorporating surrounding vehicle features into the model effectively addresses prediction biases caused by long queues or abnormal conditions of other vehicles, further improving prediction accuracy.

[0079] In addition to the two methods mentioned above, other methods can be used to predict the start-up time.

[0080] The two methods provided in this disclosure can be implemented on the user end or on the server. For example, for the first method, the transmission speed-based method has lower computational performance requirements and can be implemented on the user end. This allows the user end to obtain more real-time location information, and the user end can further combine it with multimodal information such as images and videos of the surrounding environment to achieve accurate prediction. For the second method, the model-based method has higher computational performance requirements and can be implemented on the server. This method can utilize more feature information to achieve accurate prediction. If the model size is small or the user terminal's computational performance is strong enough, it can also be implemented on the user end.

[0081] Alternatively, the embodiments of this disclosure can employ either of the above two methods for different scenarios. For example, for short-distance scenarios, the first method is used, i.e., if the queuing distance of the target vehicle is less than or equal to a preset distance threshold, the first method is used to predict the estimated start time. As another example, for long-distance scenarios, the second method is used, i.e., if the queuing distance of the target vehicle is greater than a preset distance threshold, the second method is used to predict the estimated start time.

[0082] In addition to choosing one of the two methods mentioned above, a combination of the two methods can also be used. For example, the estimated start time obtained from the two methods can be processed by averaging or taking the median value, and the resulting value can be used as the estimated start time.

[0083] The following describes step 205, namely "generating start reminder information based on start-up estimated time," in detail with reference to the embodiments.

[0084] If this process is implemented by the server, the server can send the generated start-up reminder information to the user's client for display and playback. If this process is implemented by the user's client, the user can display or play the generated start-up reminder information.

[0085] In this embodiment, the user terminal can display or broadcast a start-up reminder message when the estimated start-up time is reached. Alternatively, the start-up reminder message can be displayed or broadcast at a time (referred to as the prompt time in this embodiment) before the estimated start-up time is reached. The difference between the prompt time and the estimated start-up time is less than or equal to a preset duration, for example, displaying or broadcasting the start-up reminder message 1 second before the estimated start-up time t. This allows the driver to receive the start-up reminder message and start the vehicle in a timely manner.

[0086] The purpose of the start-up reminder message is to remind the driver to start the vehicle. The wording used can be such as "The vehicle can start", "Caution: The vehicle is starting", or "It is safe to start".

[0087] Furthermore, in this embodiment of the disclosure, in addition to providing vehicle start-up reminders, navigation prompts with other driving suggestions can also be provided to the target vehicle. For example, by using the estimated start-up time, the time it takes for the target vehicle to pass through at least one intersection can be predicted; driving suggestion information is generated based on the time to pass through at least one intersection and the timing data of the traffic lights at at least one intersection.

[0088] The driving advice information mentioned above can be used to guide traffic through intersections, such as suggesting whether it is safe to proceed through the current intersection.

[0089] One feasible approach is to use start-up prediction time and start-up acceleration to predict the time it will take for the target vehicle to pass through the current intersection; then, based on the time it takes for the target vehicle to pass through the current intersection and the timing data of the target traffic light, it can be determined whether the target vehicle can pass through the current intersection after starting; based on the determination result, driving suggestion information can be generated, which may include information on whether the vehicle can pass through the current intersection on a green light.

[0090] Typically, after a vehicle starts moving, it uses a relatively uniform acceleration to initiate the initial movement and maintains that acceleration. Therefore, by using the estimated start time and start acceleration, the time it takes for a target vehicle to pass through the current intersection can be predicted. The start acceleration can be taken as an empirical or experimental value, for example, by collecting historical vehicle trajectories at intersections and determining the start acceleration based on those historical trajectories.

[0091] If the predicted time to pass through the current intersection is before the target traffic light turns from green to red, it can be determined that the target vehicle can pass through the current intersection after starting. Driving suggestions such as "Follow closely the car in front, you are likely to pass through the green light" and "Keep your attention, you can pass through the intersection smoothly" can be generated. This can remind users to keep their attention and thus improve traffic efficiency.

[0092] If the predicted time to pass through the current intersection is after the target traffic light turns from green to red, then it is determined that the target vehicle cannot pass through the current intersection after starting. In this case, no additional driving advice information may be generated, or driving advice information such as "Keep a safe distance, the intersection cannot be passed smoothly" or "Drive slowly, the intersection cannot be passed smoothly" may be generated.

[0093] Furthermore, in some scenarios, the distance between the current intersection and the next intersection may be very short (usually a few hundred meters). In such cases, the traffic conditions at the next intersection will typically influence the behavior of drivers. Analysis revealed that regardless of whether the traffic light at the next intersection is red or green, drivers generally accelerate first and then decelerate between the current and next intersection. Therefore, by collecting historical vehicle trajectories between the current and next intersections, an acceleration change curve can be fitted.

[0094] Based on this, the estimated start time of the target vehicle, the preset acceleration change curve, and the location of the next intersection can be used to predict the time it will take for the target vehicle to pass through the next intersection. Based on the target vehicle's estimated pass time and the traffic light timing data for the next intersection, it can be determined whether the target vehicle can pass through the next intersection after starting. Based on the determination result, driving suggestion information is generated. This driving suggestion information may include information on whether the vehicle can pass through the next intersection on a green light.

[0095] If the predicted time to cross the next intersection is before the target traffic light at the next intersection turns from green to red, then it is determined that the target vehicle can pass through the next intersection on a green light. Driving suggestions such as "Maintain speed, the next intersection will have a green light" or "Accelerate appropriately, the next intersection is likely to have a green light" can be generated. This allows drivers to maintain their current driving status and quickly pass through the current road segment and the next intersection, thereby improving driving efficiency and experience.

[0096] If the predicted time to cross the next intersection is after the target traffic light at that intersection has turned from green to red, then it is determined that the target vehicle cannot cross the next intersection on a green light. Driving advice such as "Control your speed; you will still have to wait at the next intersection" can be generated. This way, when drivers know they cannot cross the next intersection on a green light, they will appropriately reduce their speed and avoid blindly accelerating, thereby improving driving safety and the driving experience.

[0097] In this embodiment, the aforementioned start-up reminder information and driving suggestion information (e.g., whether the light is green for passing through the current intersection and / or whether the light is green for passing through the next intersection) can be sent together or sequentially. For example, the start-up reminder and driving suggestion information can be sent to the user terminal together when the estimated start-up time is reached. The user terminal continuously plays the start-up reminder and driving suggestion information during the estimated start-up time, such as "The vehicle can start, follow the car in front, the intersection has a green light, accelerate appropriately, the next intersection is expected to have a green light." In this way, even if the driver is in the middle or later part of the queue, they can receive the start-up reminder in a timely manner and obtain the light-passing suggestions for the current and next intersections, thereby adopting an appropriate driving strategy.

[0098] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0099] Figure 5 is a schematic block diagram of a navigation prompt device provided in an embodiment of this disclosure. This device can be installed on the server in the architecture shown in Figure 1, or it can be installed on the user terminal. As shown in Figure 5, the device 500 includes: a location determination unit 502, a first estimation unit 503, and a prompt generation unit 504. It 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 are as follows:

[0100] The location determination unit 502 is configured to determine the queuing position of the target vehicle in front of the target traffic light at the current intersection based on road network data.

[0101] The first estimation unit 503 is configured to determine the estimated start time of the target vehicle based on the queue position and traffic light timing data.

[0102] The reminder generation unit 504 is configured to generate a start reminder message based on the estimated start time.

[0103] Furthermore, the data acquisition unit 501 can be configured to acquire road network data and traffic light timing data.

[0104] If this device is installed on a server, the data acquisition unit 501 can acquire 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 obtained from other devices or databases.

[0105] If this device is installed on the client side, the data acquisition unit 501 can obtain road network data and traffic light timing data within the nearby location range from the server, or it can obtain road network data and traffic light timing data within the range corresponding to the navigation route from the server. The nearby location range can be a preset distance range from the user's current location. Besides this method, other methods can also be used, such as the user pre-obtaining road network data and traffic light timing data within the city, district, or county where they are located.

[0106] As one possible implementation, the location determination unit 502 can be specifically configured to: if it is determined that a vehicle is located in the lane associated with the target traffic light and is stationary, take the vehicle as the target vehicle and determine the vehicle's queuing position in front of the target traffic light at the current intersection, wherein the lane associated with the target traffic light is determined based on road network data.

[0107] As one possible implementation, the location determination unit 502 can determine that the vehicle is located in the lane associated with the target traffic light based on the vehicle's trajectory data and / or navigation data; if the vehicle is stationary, it is determined that the vehicle is located in the lane associated with the target traffic light and is stationary.

[0108] The location determination unit 502 can be specifically configured to: if it is determined that the vehicle is located in the lane associated with the target traffic light and is stationary, and the vehicle's driving intention is determined to be consistent with a preset intention based on the vehicle's navigation data, then the vehicle is designated as the target vehicle, wherein the preset intention includes turning left or going straight through the current intersection.

[0109] As one possible implementation method, the first estimation unit 503 can be specifically configured to: determine the queuing distance corresponding to the target vehicle based on the queuing position, wherein the queuing distance is the distance between the target vehicle and the stop line of the current intersection; and determine the estimated start time of the target vehicle based on the time when the target traffic light changes to green, the queuing distance, and the preset transmission speed, wherein the time when the target traffic light changes to green is determined based on traffic light timing data.

[0110] Furthermore, before generating start reminder information based on start prediction time, the first prediction unit 503 can also be configured to: identify other vehicles preceding the target vehicle; and correct the start prediction time of the target vehicle using the actual start times of the other vehicles.

[0111] As another possible approach, the first prediction unit 503 can be specifically configured to: input the queuing position of the target vehicle in front of the target traffic light at the current intersection, the timing data of the target traffic light, and environmental information into the start time prediction model, and obtain the start time prediction of the target vehicle output by the start time prediction model; wherein, the environmental information includes the characteristics of the current intersection and / or the surrounding vehicle characteristics corresponding to the target vehicle.

[0112] As one possible implementation method, the reminder generation unit 504 can be specifically configured to: in response to the arrival of the estimated start time, or in response to the reminder time before the arrival of the estimated start time, the difference between the reminder time and the estimated start time is less than or equal to a preset duration, and the start reminder information is displayed or broadcast by the user terminal.

[0113] Furthermore, the second prediction unit 505 can be configured to: predict the time it takes for the target vehicle to pass through at least one intersection using the start-up prediction time; and generate driving suggestion information based on the time to pass through at least one intersection and the timing data of the traffic lights at the at least one intersection.

[0114] As one possible implementation, the at least one intersection includes the current intersection, and the second prediction unit 505 can be specifically configured to: predict the time it takes for the target vehicle to pass through the current intersection using the start-up prediction time and start-up acceleration; or...

[0115] The at least one intersection includes the next intersection of the current intersection. The second prediction unit 505 can be specifically configured to predict the time it takes for the target vehicle to pass through the next intersection by using the start-up prediction time, the preset acceleration change curve and the position of the next intersection.

[0116] As another possible implementation, the second prediction unit 505 can be specifically configured to: determine whether the target vehicle can pass through the at least one intersection on a green light after starting, based on the time taken to pass through at least one intersection and the timing data corresponding to the traffic lights at the at least one intersection; and generate driving suggestion information based on the determination result.

[0117] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for system or device embodiments, since they are basically similar to method embodiments, the description is relatively simple, and relevant parts can be referred to the description of the method embodiments. The system and 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 creative effort.

[0118] 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, data stored, data displayed, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation portals are provided for users to choose to authorize or refuse.

[0119] In addition, this disclosure also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any of the foregoing method embodiments.

[0120] This disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method described in any of the foregoing method embodiments.

[0121] This disclosure also provides an electronic device, including:

[0122] One or more processors; and a memory associated with the one or more processors, the memory being used to store program instructions that, when read and executed by the one or more processors, perform the steps of the method described in any of the foregoing method embodiments.

[0123] Figure 6 is a schematic block diagram of an electronic device provided in an embodiment of this disclosure. The electronic device 600 may specifically 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, video display adapter 611, disk drive 612, input / output interface 613, network interface 614, and memory 620 can communicate with each other via a communication bus 630.

[0124] The processor 610 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in this disclosure.

[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 the operating system 621 for controlling the operation of the electronic device 600, and the Basic Input / Output System (BIOS) 622 for controlling the low-level operations of the electronic device 600. Additionally, it can store a web browser 623, a data storage management system 624, and a navigation prompting device 500, etc. The aforementioned navigation prompting device 500 can be the application program that specifically implements the aforementioned steps in this embodiment. In summary, when the technical solution provided by this disclosure is implemented through software or firmware, the relevant program code is stored in the memory 620 and is called and executed by the processor 610.

[0126] Input / output interface 613 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touch screens, microphones, various sensors, etc., and output devices may include displays, speakers, vibrators, indicator lights, etc.

[0127] Network interface 614 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0128] Bus 630 includes a pathway for transmitting information between various components of the device, such as processor 610, video display adapter 611, disk drive 612, input / output interface 613, network interface 614, and memory 620.

[0129] It should be noted that although the above-described device only shows the processor 610, video display adapter 611, disk drive 612, input / output interface 613, network interface 614, memory 620, bus 630, etc., in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the present disclosure, and need not include all the components shown in the figures.

[0130] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this disclosure can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer program product. This computer program product can be stored in a 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 various embodiments or some parts of the embodiments of this disclosure.

[0131] The technical solutions provided in this disclosure have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this disclosure. The descriptions of the embodiments above are only for the purpose of helping to understand the methods and core ideas of this disclosure. Furthermore, those skilled in the art will recognize that, based on the ideas of this disclosure, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this disclosure.

Claims

1. A navigation prompt method for traffic light scenarios, wherein, The method includes: Based on road network data, determine the queuing position of the target vehicle in front of the target traffic light at the current intersection; Based on the queue position and traffic light timing data, the estimated start time of the target vehicle is determined; Based on the estimated start time, a start reminder message is generated.

2. The method according to claim 1, wherein, Determining the queuing position of the target vehicle in front of the target traffic light at the current intersection includes: If a vehicle is determined to be in the lane associated with the target traffic light and is stationary, the vehicle is designated as the target vehicle, and the vehicle's queuing position 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, wherein, The determination that the vehicle is located in the lane associated with the target traffic light and is stationary includes: Based on the vehicle's trajectory data and / or navigation data, determine that the vehicle is located in the lane associated with the target traffic light; If the vehicle is stationary, then it is determined that the vehicle is located in the lane associated with the target traffic light and is stationary.

4. The method according to claim 2 or 3, wherein, If it is determined that a vehicle is located in the lane associated with the target traffic light and is stationary, the vehicle is designated as the target vehicle, including: If it is determined that a vehicle is located in the lane associated with the target traffic light and is stationary, and the vehicle's driving intention is determined to be consistent with a preset intention based on the vehicle's navigation data, then the vehicle is designated as the target vehicle, wherein the preset intention includes turning left or going straight through the current intersection.

5. The method according to any one of claims 1 to 4, wherein, Based on the queue position and traffic light timing data, the estimated start time of the target vehicle is determined, including: Based on the queue position, the queue distance corresponding to the target vehicle is determined, where the queue distance is the distance between the target vehicle and the stop line of the current intersection. Based on the time it takes for the target traffic light to turn green, the queue distance, and the preset transmission speed, the estimated start time of the target vehicle is determined, wherein the time it takes for the target traffic light to turn green is determined based on the traffic light timing data.

6. The method according to claim 5, wherein, Before generating the start-up reminder information based on the estimated start-up time, the process also includes: Identify other vehicles preceding the target vehicle; The estimated start time of the target vehicle is corrected by using the actual start times of the other vehicles.

7. The method according to any one of claims 1 to 4, wherein, Based on the queue position and traffic light timing data, the estimated start time of the target vehicle is determined, including: Input the queue position, the timing data corresponding to the target traffic light, and environmental information into the start time prediction model to obtain the start time prediction of the target vehicle output by the start time prediction model. The environmental information includes the features of the current intersection and / or the surrounding vehicle features corresponding to the target vehicle.

8. The method according to any one of claims 1 to 7, wherein, The method further includes: In response to the arrival of the estimated start time, or in response to a notification time prior to the arrival of the estimated start time, where the difference between the notification time and the estimated start time is less than or equal to a preset duration, the start reminder information is displayed or broadcast by the user terminal.

9. The method according to any one of claims 1 to 9, wherein, The method further includes: Using the estimated start time, predict the time it will take for the target vehicle to pass through at least one intersection; Based on the time taken to pass through at least one intersection and the timing data of the traffic lights at the at least one intersection, driving suggestion information is generated.

10. The method according to claim 9, wherein, The at least one intersection includes the current intersection. Predicting the time for the target vehicle to pass through the at least one intersection using the estimated start time includes: predicting the time for the target vehicle to pass through the current intersection using the estimated start time and start acceleration; or... The at least one intersection includes the next intersection of the current intersection. Predicting the time for the target vehicle to pass through at least one intersection using the estimated start time includes: predicting the time for the target vehicle to pass through the next intersection using the estimated start time, a preset acceleration change curve, and the position of the next intersection.

11. The method according to claim 9 or 10, wherein, Based on the time taken to pass through at least one intersection and the traffic light timing data corresponding to the at least one intersection, driving suggestion information is generated, including: Based on the time taken to pass through at least one intersection and the timing data of the traffic lights at at least one intersection, determine whether the target vehicle can pass through at least one intersection on a green light after starting; Based on the judgment results, driving suggestion information is generated.

12. The method according to any one of claims 1 to 11, wherein, If the method is executed by the user terminal, then the method further includes: The system retrieves road network data and traffic light timing data within a preset distance range from the user terminal from the server in real time; or... Obtain road network data and traffic light timing data within the range corresponding to the navigation route of the user terminal from the server; or, Obtain road network data and traffic light timing data within the area where the user terminal is located from the server.

13. A navigation prompt device for traffic light scenarios, wherein, The device includes: The location determination unit is configured to determine the queuing position of the target vehicle in front of the target traffic light at the current intersection based on road network data. The first estimation unit is configured to determine the estimated start time of the target vehicle based on the queue position and traffic light timing data. The reminder generation unit is configured to generate start reminder information based on the estimated start time.

14. An electronic device, comprising: One or more processors, and memory associated with said one or more processors; The memory is used to store program instructions, which, when read and executed by the one or more processors, implement the steps of the method according to any one of claims 1 to 12.

15. A computer-readable storage medium having a computer program stored thereon, wherein, When the program is executed by the processor, it implements the steps of the method according to any one of claims 1 to 12.

16. A computer program product comprising a computer program, wherein, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 12.

Images