Overtaking prompting method, device, equipment, medium and product

Abstract

The application discloses an overtaking prompting method and device, equipment, medium and product, and relates to the technical field of intelligent driving. The method comprises the following steps: obtaining target vehicle driving information and driving environment information of a target vehicle; in the case that the target vehicle has a following vehicle, determining an overtaking risk level of the following vehicle according to the target vehicle driving information and the driving environment information, the following vehicle being a vehicle located in the same lane as the target vehicle and behind the target vehicle; and outputting overtaking prompting information according to the overtaking risk level of the following vehicle. According to the application, the danger of overtaking can be reduced.

Description

Technical Field

[0001] This application belongs to the field of intelligent driving technology, and in particular relates to an overtaking warning method, device, equipment, medium and product. Background Technology

[0002] With traffic safety issues becoming increasingly serious, vehicle driver assistance systems, as an important component of modern automotive technology, have become a crucial means to improve road safety and reduce traffic accidents.

[0003] There are many types of vehicle driver assistance systems with different functions. For example, adaptive cruise control systems can automatically adjust the vehicle's speed based on the speed and distance of the vehicle in front to maintain a safe distance; overtaking assist systems can monitor the surrounding environment in real time through sensors when the driver intends to overtake, providing necessary assistance and feedback to ensure the smooth overtaking process, thereby improving the safety of the vehicle.

[0004] However, while these driver assistance systems play a significant role in enhancing safety, most focus on improving the vehicle's perception and reaction to its surroundings, neglecting intelligent monitoring and warnings of overtaking maneuvers. In actual driving, if the vehicle in front is a large truck, the following vehicle may be unable to accurately assess the current road conditions due to the obstruction, leading to blind overtaking maneuvers that pose numerous safety hazards and are therefore quite dangerous. Summary of the Invention

[0005] This application provides an overtaking warning method, device, equipment, medium, and product that can reduce the danger of overtaking.

[0006] A first aspect of this application provides an overtaking warning method, the method comprising:

[0007] Obtain the target vehicle's driving information and driving environment information;

[0008] When there are following vehicles, the overtaking risk level of the following vehicles is determined based on the target vehicle's driving information and driving environment information. The following vehicles are those that are in the same lane as the target vehicle and are behind the target vehicle.

[0009] Based on the overtaking risk level of the following vehicle, an overtaking prompt message is output.

[0010] A second aspect of this application provides an overtaking warning device, the device comprising:

[0011] The environment acquisition module is used to acquire the target vehicle's driving information and driving environment information;

[0012] The risk assessment module is used to determine the overtaking risk level of the following vehicles when there are following vehicles, based on the target vehicle's driving information and driving environment information. The following vehicles are those that are in the same lane as the target vehicle and are behind the target vehicle.

[0013] The overtaking warning module is used to output overtaking warning information based on the overtaking risk level of the following vehicle.

[0014] A third aspect of the embodiments of this application provides an electronic device, the device comprising: a memory and a program or instructions stored in the memory and executable on a processor, wherein when the program or instructions are executed by the processor, they implement the overtaking prompting method provided in any of the embodiments of this application described above.

[0015] A fourth aspect of the embodiments of this application provides a readable storage medium on which a program or instructions are stored, and when the program or instructions are executed by a processor, they implement the overtaking prompting method provided by any aspect of the embodiments of this application described above.

[0016] A fifth aspect of the embodiments of this application provides a computer program product in which instructions, when executed by a processor of an electronic device, cause the electronic device to perform the overtaking prompting method provided in any aspect of the embodiments of this application described above.

[0017] The overtaking warning method provided in this application obtains the target vehicle's driving information and driving environment information. When a following vehicle is present, the overtaking risk level of the following vehicle is determined based on the target vehicle's driving information and driving environment information. Thus, by accurately determining whether a safe overtaking environment exists for the following vehicle using the target vehicle's driving information and driving environment information, an overtaking warning is issued to the following vehicle. Therefore, the following vehicle can decide whether to perform an overtaking maneuver based on the target vehicle's overtaking warning, avoiding the safety hazards caused by blind overtaking and reducing the danger of overtaking. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating an overtaking warning method provided in one embodiment of this application;

[0020] Figure 2 This is a flowchart illustrating S102 provided in one embodiment of this application;

[0021] Figure 3 This is a schematic diagram of the overtaking warning device provided in one embodiment of this application;

[0022] Figure 4 This is a schematic diagram of the overtaking warning device provided in one embodiment of this application. Detailed Implementation

[0023] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0024] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0025] It should be noted that the acquisition, storage, use, and processing of data in the technical solution of this application all comply with the relevant provisions of national laws and regulations.

[0026] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0027] With the continuous development of vehicle driver assistance systems, technologies such as adaptive cruise control and overtaking assist systems have played a significant role in improving road safety. However, these systems primarily focus on the vehicle's perception and reaction to its surroundings, neglecting the intelligent monitoring and warning of overtaking behavior from vehicles behind. In actual driving, especially when the vehicle in front is a large truck, the following vehicle may be unable to accurately judge the current road conditions due to obstructed visibility, potentially leading to blind overtaking and increasing the risk of traffic accidents. Therefore, addressing the risk of overtaking from behind has become a pressing technical problem.

[0028] Specifically, on highways or multi-lane roads, it's common to see large vehicles like trucks or buses traveling ahead. Due to their size and height, drivers of smaller vehicles behind often cannot see the road conditions clearly. In such situations, if a smaller vehicle driver decides to overtake, but there is roadwork ahead or a vehicle is stopped in the lane due to a breakdown, the overtaking maneuver poses a significant safety hazard.

[0029] The purpose of this application is to provide a method, device, equipment, medium, and product for overtaking warning. The overtaking warning method provided in this application acquires the target vehicle's driving information and driving environment information. When a following vehicle is present, the overtaking risk level of the following vehicle is determined based on the target vehicle's driving information and driving environment information. Thus, by accurately determining whether a safe overtaking environment exists for the following vehicle using the target vehicle's driving information and driving environment information, an overtaking warning is issued to the following vehicle. Therefore, the following vehicle can decide whether to perform an overtaking action based on the target vehicle's overtaking warning information, avoiding the safety hazards caused by blind overtaking and reducing the danger of overtaking.

[0030] The following describes specific embodiments of the overtaking warning method, apparatus, equipment, medium, and product provided in this application. The overtaking warning method will be described first.

[0031] Figure 1 A flowchart of an overtaking prompt method is provided. This overtaking prompt method can be applied to the server and may include the following steps S101 to S103.

[0032] S101, Obtain the target vehicle's driving information and driving environment information.

[0033] In this embodiment, the target vehicle driving information is used to characterize relevant information about the target vehicle during its driving process. For example, the target vehicle driving information may include dynamic parameters such as the target vehicle's speed, acceleration, and direction, which can be acquired using onboard sensors.

[0034] Driving environment information is used to characterize the environmental information of a target vehicle during its driving process. For example, driving environment information may include information such as road conditions, weather conditions, and the positions of other vehicles around the target vehicle, which can be collected using devices such as cameras and radar.

[0035] As an example, the server acquires the target vehicle's driving information, including dynamic parameters such as speed, acceleration, and direction, through onboard sensors. Simultaneously, it uses cameras, radar, and other devices to collect information about the target vehicle's driving environment, such as road conditions, weather conditions, and the positions of other vehicles. This information provides comprehensive data support for subsequent risk assessment.

[0036] S102, when there are following vehicles, determine the overtaking risk level of the following vehicles based on the target vehicle's driving information and driving environment information. The following vehicles are those that are in the same lane as the target vehicle and are behind the target vehicle.

[0037] In this embodiment, a following vehicle refers to a vehicle that is in the same lane as the target vehicle and is behind the target vehicle. Specifically, it can be identified by rear radar or a camera.

[0038] Overtaking risk level refers to the degree of danger of following a vehicle when overtaking. It can be determined by multi-dimensional information analysis and risk assessment models.

[0039] As an example, the server continuously monitors traffic conditions behind the target vehicle using rear radar or cameras to identify whether there are any tailgating vehicles. Once a tailgating vehicle is detected, an overtaking risk assessment process is initiated.

[0040] Specifically, the server comprehensively analyzes the target vehicle's driving information and environmental information, combined with the behavioral characteristics of the following vehicle, such as changes in distance to the vehicle in front, changes in lateral position, and changes in speed, to calculate the overtaking risk level of the following vehicle through a pre-set risk assessment model. This risk assessment model needs to consider multiple factors, such as road type, number of lanes, weather conditions, the condition of the vehicle in front, the behavior of the following vehicle, and the condition of other surrounding vehicles.

[0041] S103 outputs overtaking prompts based on the overtaking risk level of the following vehicle.

[0042] In this embodiment, the overtaking warning information refers to a warning message issued by the driver or vehicle system regarding the risk of overtaking, which can be presented through vision, sound, or an in-vehicle communication system.

[0043] As an example, the server outputs corresponding overtaking warning information based on the assessed overtaking risk level. This warning information can be presented in various ways, such as displaying different colors or patterns on the LED display screen behind the target vehicle, or triggering the target vehicle's horn or warning lights when a high-risk overtaking intention is detected. If vehicle-to-everything (V2X) connectivity is available, risk warning information can also be sent directly to following vehicles.

[0044] The overtaking warning method provided in this embodiment obtains the target vehicle's driving information and driving environment information. When there are following vehicles, the overtaking risk level of the following vehicles is determined based on the target vehicle's driving information and driving environment information. Thus, by accurately determining whether a safe overtaking environment exists for the following vehicles using the target vehicle's driving information and driving environment information, an overtaking warning message is issued to the following vehicles. Therefore, the following vehicles can decide whether to perform an overtaking maneuver based on the target vehicle's overtaking warning message, avoiding the safety hazards caused by blind overtaking behavior and reducing the danger of overtaking.

[0045] As an optional embodiment, the driving environment information includes traffic condition information, obstacle information, and surrounding vehicle driving information;

[0046] like Figure 2 As shown, S102 may specifically include the following S201 to S203.

[0047] S201, based on traffic information, determines whether overtaking is permitted under current conditions, thus obtaining the first risk result;

[0048] S202, if the first risk result indicates that the current road conditions allow overtaking, then based on the obstacle information, determine whether the obstacle will interfere with overtaking, and obtain the second risk result;

[0049] S203, if the second risk result indicates that the obstacle will not interfere with overtaking, determine the overtaking risk level of the following vehicle based on the driving information of surrounding vehicles and the driving information of the target vehicle.

[0050] In this embodiment, traffic information is used to characterize the basic conditions of the road on which the target vehicle is currently traveling. For example, it may include road signs, lane line types, etc.

[0051] Obstacle information is used to characterize objects that may affect the overtaking behavior of following vehicles. For example, obstacle information may include information about surrounding pedestrians and non-motorized vehicles.

[0052] As an example, the server first obtains traffic information, including road signs and lane markings. Then, it determines whether there is a "No Overtaking" sign or whether the left lane marking is solid. If there is a "No Overtaking" sign or the left lane marking is solid, then overtaking is not permitted; otherwise, overtaking is permitted.

[0053] If current road conditions do not permit overtaking, the overtaking risk level of the following vehicle is directly classified as high-risk. If current road conditions permit overtaking, further obstacle information is obtained, including the obstacle's location, speed, and type. For dynamic obstacles, their trajectory is generated, and it is predicted whether they will enter the lane of the following vehicle or the overtaking lane, and the timing of their entry. If the obstacle will enter the lane of the following vehicle or the overtaking lane, and the entry time is earlier than a preset time threshold (e.g., 5 seconds), it is determined that the obstacle will interfere with overtaking; otherwise, it is determined that the obstacle will not interfere with overtaking.

[0054] Finally, if the obstacle would interfere with overtaking, the overtaking risk level of the following vehicle is directly determined to be high-risk. If the obstacle would not interfere with overtaking, the driving information of surrounding vehicles and the target vehicle is obtained to generate a predicted overtaking trajectory for the following vehicle, as well as the predicted driving trajectories of surrounding vehicles that might interfere with the overtaking behavior of the following vehicle. Based on the predicted overtaking trajectory and the predicted driving trajectories, it is assessed whether a collision will occur during the overtaking process. If it is assessed that a collision will occur during the overtaking process, the overtaking risk level of the following vehicle is determined to be high-risk; if it is assessed that a collision will not occur during the overtaking process, the overtaking risk level of the following vehicle is determined to be low-risk.

[0055] This embodiment first determines whether traffic conditions permit overtaking, ensuring that the overtaking behavior complies with traffic rules. Second, it considers obstacle factors, assessing the presence of obstacles that might interfere with overtaking. Finally, it comprehensively analyzes the driving information of surrounding and target vehicles to derive the final overtaking risk level. Thus, this multi-dimensional risk assessment method not only improves the accuracy of overtaking risk judgment but also provides a more reliable decision-making basis for drivers or autonomous driving systems.

[0056] As an optional embodiment, S201 may specifically include:

[0057] Determine the current road conditions based on traffic information;

[0058] If there is a no-overtaking sign or the left lane line of the first lane is a solid line, it is determined that overtaking is not allowed under the current road conditions, and the first lane is the lane where the following vehicle is located.

[0059] If there is no no-overtaking sign in the current road conditions and the left lane line of the first lane is not a solid line, then overtaking is permitted under the current road conditions.

[0060] In this embodiment, traffic information can be acquired through methods such as vehicle-mounted cameras. For example, vehicle-mounted cameras can capture real-time traffic information such as road signs and lane markings.

[0061] No-overtaking signs can be identified using image recognition technology, employing deep learning algorithms to train models that can recognize various forms of no-overtaking signs. Lane markings can be determined using lane marking detection algorithms, which not only identify the position of lane markings but also further distinguish between solid and dashed lines.

[0062] In practical applications, specific parameter ranges can be set. For example, for lane line recognition, a confidence threshold can be set; the recognition is considered valid only when the confidence level of the recognition result exceeds 95%. The effective distance for no-overtaking signs can be set to within 100 meters ahead. These specific parameters can be adjusted according to actual road conditions and vehicle performance.

[0063] As an example, the server first obtains traffic information to establish a comprehensive understanding of the current road environment.

[0064] Next, check if there is a clear prohibition on overtaking (either there is a "No Overtaking" sign or the left lane line of the following vehicle is a solid line). If so, overtaking is directly prohibited, and the overtaking risk level of the following vehicle is immediately set to high risk. Overtaking is only permitted if neither of these two conditions is met.

[0065] In practice, the system can make a judgment at preset intervals to ensure timely updates to decisions when road conditions change. For example, when a vehicle enters a new road section and the camera detects a no-overtaking sign 100 meters ahead, the system will promptly update its judgment to indicate that overtaking is not permitted under current road conditions.

[0066] This embodiment determines the current road conditions based on traffic information, and then uses two conditions to decide whether overtaking is permitted: first, whether there is a "no overtaking" sign; and second, whether the left lane line of the following vehicle's lane is a solid line. If a "no overtaking" sign is present or the left lane line is a solid line, overtaking is not permitted; conversely, if there is no "no overtaking" sign and the left lane line is not a solid line, overtaking is permitted. This method effectively improves overtaking safety.

[0067] As an optional embodiment, S202 may specifically include:

[0068] When the obstacle is a dynamic obstacle, the obstacle's trajectory is generated based on the obstacle information;

[0069] Based on the movement trajectory, predict whether the obstacle will enter the target lane and when it will enter the target lane. The target lane includes the first lane and the second lane. The first lane is the lane where the following vehicle is located, and the second lane is the overtaking lane corresponding to the first lane.

[0070] If an obstacle is about to enter the target lane and its entry into the target lane occurs before a preset time threshold, it is determined that the obstacle will interfere with overtaking.

[0071] If the obstacle will not enter the target lane or enters the target lane later than a preset time threshold, it is determined that the obstacle will not interfere with overtaking.

[0072] In this embodiment, obstacle information is used to identify and analyze dynamic obstacles that may affect overtaking behavior. For example, dynamic obstacles may be pedestrians and non-motorized vehicles on the roadside. For example, obstacle information can be acquired through onboard sensors, such as cameras, radar, or lidar. These onboard sensors can capture dynamic obstacle information in the surrounding environment in real time, including data such as the obstacle's position, speed, and direction.

[0073] A preset time threshold is used to compare with the predicted time of a dynamic obstacle entering the target lane to determine whether the obstacle will interfere with overtaking. The preset time threshold can be dynamically adjusted based on factors such as vehicle speed and road conditions. For example, on highways, where vehicle speeds are higher, the preset time threshold can be set shorter; on urban roads, where vehicle speeds are slower, the preset time threshold can be set longer.

[0074] Various algorithms can be used to generate motion trajectories, such as Kalman filters or particle filters. These algorithms can predict the future trajectory of an obstacle based on its historical position and current state. For example, linear or nonlinear motion models can be used, combined with the obstacle's velocity and acceleration information, to generate a predicted trajectory over a period of time.

[0075] Predicting the target lane involves analysis in both spatial and temporal dimensions. Spatially, it's necessary to determine whether the obstacle's trajectory intersects with the target lane (lane one or lane two). Temporally, it's necessary to calculate the estimated time for the obstacle to reach the intersection point. This can be achieved through geometric calculations comparing the obstacle's trajectory with the lane boundary lines.

[0076] As an example, this application can be implemented in an autonomous driving system. Specifically, the vehicle can be equipped with a sensor system that includes cameras, millimeter-wave radar, and lidar. These sensors can capture information about obstacles in a 360-degree radius around the vehicle in real time, including data such as their position, speed, and acceleration.

[0077] When the obstacle is dynamic, the server runs a deep learning-based obstacle tracking algorithm to generate its trajectory. Simultaneously, the in-vehicle navigation system provides precise lane-level map information, including the exact location and width of the vehicle's current first lane and the adjacent second lane (overtaking lane). The server compares the predicted obstacle trajectory with this lane information to determine whether the obstacle will enter the target lane.

[0078] In addition, the preset time threshold can be dynamically adjusted according to the current vehicle speed. For example, in a highway scenario, when the vehicle speed is 100km / h, the time threshold can be set to 2 seconds; while in an urban road scenario, when the vehicle speed is 50km / h, the time threshold can be set to 3 seconds.

[0079] If the server predicts that an obstacle will enter the overtaking lane in 2.5 seconds, and the current time threshold is 2 seconds, the server will determine that the obstacle will not interfere with overtaking. Conversely, if the predicted time is 1.8 seconds, which is less than the current time threshold, the server will determine that the obstacle will interfere with overtaking and will issue a warning in a timely manner.

[0080] This embodiment establishes a more intelligent and reliable overtaking risk assessment method by comprehensively considering the motion characteristics of dynamic obstacles, lane information, and time factors. This effectively identifies and assesses the potential interference of dynamic obstacles on overtaking behavior, thereby improving overtaking safety.

[0081] As an optional embodiment, the surrounding vehicle driving information includes first driving information of the following vehicle and second driving information of the interfering vehicle, wherein the interfering vehicle is a vehicle located in the overtaking lane of the following vehicle.

[0082] S203 may specifically include:

[0083] Based on the initial driving information of the following vehicle, a predicted overtaking trajectory for the following vehicle is generated.

[0084] Based on the second driving information of the interfering vehicle, a predicted driving trajectory of the interfering vehicle is generated;

[0085] Based on the predicted overtaking trajectory and the predicted driving trajectory, the overtaking risk level of the following vehicle is determined.

[0086] In this embodiment, the surrounding vehicle driving information includes first driving information of following vehicles and second driving information of interfering vehicles. A following vehicle is a vehicle located in the same lane as the target vehicle and behind it, while an interfering vehicle is a vehicle located in the overtaking lane of the following vehicle.

[0087] When generating a predicted overtaking trajectory based on the initial driving information of the following vehicle, various factors can be considered, such as vehicle speed, acceleration, and steering wheel angle. The predicted overtaking trajectory can be generated using algorithms such as polynomial fitting or Kalman filtering. Similarly, the predicted driving trajectory of interfering vehicles can be generated using a similar method.

[0088] When determining the overtaking risk level based on the predicted overtaking trajectory and the predicted driving trajectory, various assessment methods can be used. For example, the minimum distance between the two trajectories within the prediction time window can be calculated. If the minimum distance is less than a safety threshold (e.g., 2 meters), it may indicate a high risk. Alternatively, the time difference at the trajectory intersection point can be calculated. If the time difference is less than a safety threshold (e.g., 1.5 seconds), it may also indicate a high risk.

[0089] As an example, suppose on a two-way four-lane highway, target vehicle A is traveling at 100 km / h in the rightmost lane. Following vehicle B is 50 meters behind vehicle A at 110 km / h, while in the adjacent left lane, interfering vehicle C is traveling at 95 km / h 100 meters in front of vehicle B.

[0090] The server first obtains real-time driving data for the three vehicles, including their location, speed, and acceleration. Then, based on vehicle B's current state and historical data, the server predicts that if vehicle B were to change lanes to the left to overtake, the overtaking process would take approximately 8 seconds. Simultaneously, the server also predicts vehicle C's trajectory over the next 10 seconds.

[0091] By comparing the two predicted trajectories, the server found that approximately 5 seconds after car B began changing lanes, the distance between car B and car C would shrink to less than 5 meters, which is less than the preset safety threshold of 10 meters. Therefore, the server classified this overtaking maneuver as "high risk".

[0092] Based on this assessment, the server can immediately issue warning messages to both vehicle A and vehicle B, advising vehicle B not to attempt to overtake, or reminding vehicle A to observe the movements of vehicles behind it. If necessary, the server can take relevant measures to create a safer overtaking environment for vehicle B.

[0093] This embodiment enables precise prediction and analysis of the behavior of surrounding vehicles, achieving accurate assessment of the overtaking risk level of following vehicles. This method not only considers the driving status of the following vehicle itself but also takes into account interfering vehicles that may affect overtaking safety, thus providing a more comprehensive and reliable risk assessment result. This helps to promptly identify potential overtaking risks, providing important evidence for subsequent early warning and auxiliary decision-making, thereby improving road driving safety.

[0094] As an optional embodiment, before S102, the overtaking warning method may further include:

[0095] Determine the intensity of the overtaking intention of the following vehicle based on driving environment information;

[0096] S102 may specifically include:

[0097] If there are following vehicles on the target vehicle, and the intensity of the following vehicles' overtaking intention meets the preset overtaking conditions, the overtaking risk level of the following vehicles is determined based on the target vehicle's driving information and driving environment information.

[0098] In this embodiment, the overtaking intention intensity is used to characterize the likelihood of a following vehicle attempting to overtake.

[0099] Determining the strength of overtaking intent can be achieved in several ways. For example, machine learning models can be used to analyze driving environment information and predict the strength of the overtaking intent of following vehicles. Specifically, the driving information of following vehicles (such as speed, acceleration, lane position, etc.) can be used as input features to construct an overtaking intent prediction model. This model can be trained on a large amount of historical data and can output a numerical value representing the strength of the overtaking intent. Another approach is rule-based judgment, where a series of judgment conditions can be set to determine the strength of the overtaking intent of following vehicles.

[0100] When preset overtaking conditions are used to characterize the overtaking risk level of following vehicles, the conditions that the intensity of the following vehicle's overtaking intention must meet can be flexibly set according to the actual situation. For example, the intensity of overtaking intention can be divided into three levels: low, medium, and high. Only when the intensity of overtaking intention reaches the "high" level will the preset overtaking conditions be met, thereby triggering the assessment of the overtaking risk level.

[0101] As an example, when the server detects a following vehicle, it first analyzes the driving environment information to determine the strength of the following vehicle's overtaking intention. Assuming a scoring system from 0 to 100 is used, where 0 represents no overtaking intention and 100 represents an extremely strong overtaking intention, the server may consider the following factors: the relative speed between the following and target vehicles (weight: 30%), the rate of change of distance between the two vehicles (weight: 25%), the following vehicle's lane departure (weight: 20%), whether the following vehicle has its turn signal on (weight: 15%), and the road type and traffic conditions (weight: 10%).

[0102] By weighting these factors, the server calculates an overtaking intent strength score. Assuming the preset overtaking condition is a score greater than 70, the server will only proceed to the next step of overtaking risk level assessment when the overtaking intent strength exceeds this threshold.

[0103] This embodiment utilizes analysis of driving environment information to determine the intensity of a following vehicle's overtaking intention. This step helps to more accurately determine whether a following vehicle intends to overtake, avoiding unnecessary risk assessments. An overtaking risk level assessment is only performed when the intensity of the following vehicle's overtaking intention meets preset overtaking conditions. This method effectively filters out cases without a clear overtaking intention, improving the system's efficiency and accuracy.

[0104] As an optional embodiment, the driving environment information includes at least traffic condition information and surrounding vehicle driving information, and the surrounding vehicle driving information includes at least the first driving information of the following vehicles;

[0105] Based on driving environment information, determine the intensity of the overtaking intention of the following vehicle, which may specifically include:

[0106] The following vehicle's first driving information, the target vehicle's driving information, and traffic conditions are combined to form the overtaking intention vector of the following vehicle.

[0107] The overtaking intention vector is input into the overtaking intention model to obtain the overtaking intention intensity of the following vehicle. The overtaking intention model is a model trained based on the historical overtaking intention vector and the corresponding historical overtaking intention intensity.

[0108] In this embodiment, the overtaking intent vector is a vector comprehensively reflecting the overtaking scenario, composed of the following vehicle's initial driving information, the target vehicle's driving information, and traffic condition information. This overtaking intent vector can be a multi-dimensional array, with each dimension representing a specific parameter. For example, the overtaking intent vector may include parameters such as the following vehicle's speed, acceleration, lateral position, distance to the vehicle in front, the target vehicle's speed, acceleration, road type, and speed limit.

[0109] The overtaking intent model is trained on historical data and is used to process overtaking intent vectors and output the intensity of overtaking intent. The overtaking intent model can be a deep neural network, such as a Long Short-Term Memory network. The input to the model is the overtaking intent vector, and the output is a value between 0 and 1, representing the intensity of the overtaking intent.

[0110] As an example, the server first collects the following vehicle's initial driving information, the target vehicle's driving information, and traffic condition information to construct an overtaking intent vector containing multi-dimensional data. This overtaking intent vector comprehensively reflects the current overtaking scenario, including the states of the following and target vehicles, as well as the surrounding environment.

[0111] The server then inputs this vector into the overtaking intent model, which has been trained using historical data. The overtaking intent model processes this information, considers various factors, and outputs a numerical value representing the strength of the overtaking intent.

[0112] This embodiment not only considers the behavior of the following vehicle, but also takes into account the surrounding environment and the state of the target vehicle. By constructing a multi-dimensional overtaking intention vector, more subtle behavioral features and environmental factors can be captured, thereby improving the accuracy and robustness of overtaking intention judgment.

[0113] As an optional embodiment, the driving environment information includes at least the driving information of surrounding vehicles, and the driving information of surrounding vehicles includes at least the first driving information of the following vehicle;

[0114] Based on driving environment information, determine the intensity of the overtaking intention of the following vehicle, which may specifically include:

[0115] If the first driving information of the following vehicle meets the first intent condition, it is determined that the following vehicle belongs to the first overtaking intent intensity. The first intent condition is that the speed of the following vehicle continues to increase and the relative distance between the following vehicle and the vehicle in front of the following vehicle is less than the preset safe distance.

[0116] If the first driving information of the following vehicle meets the second intent condition, it is determined that the following vehicle belongs to the second overtaking intent intensity. The second intent condition is that the lane deviation rate of the following vehicle is greater than the preset deviation rate threshold and the following vehicle turns on its turn signal.

[0117] If the first driving information of the following vehicle meets the third intent condition, the following vehicle is determined to be of third overtaking intent intensity. The third intent condition is that the acceleration of the following vehicle is greater than the preset acceleration threshold and lasts for at least the target duration.

[0118] If the first driving information of the following vehicle satisfies at least two of the first intent condition, the second intent condition, and the third intent condition, the following vehicle is determined to have the fourth overtaking intent intensity.

[0119] In this embodiment, the first intent condition focuses on the speed changes of the following vehicle and its distance from the vehicle in front. When the following vehicle continues to accelerate and gets too close to the vehicle in front, it may indicate that it intends to overtake.

[0120] The second intention condition takes into account lane departure and the use of turn signals, which are typical precursors to overtaking behavior.

[0121] The third intention condition focuses on the acceleration and duration of the following vehicle; sustained high acceleration may indicate preparation to overtake.

[0122] The fourth overtaking intent strength provides a higher level of overtaking intent judgment by comprehensively considering multiple conditions.

[0123] As an example, the server first obtains the initial driving information of the following vehicle, including its speed, acceleration, distance to the vehicle in front, lane departure rate, and turn signal status. Simultaneously, it sets relevant thresholds, such as a preset safe distance of 30 meters, a preset lane departure rate threshold of 0.5 meters per second, a preset acceleration threshold of 2 meters per second, and a target duration of 3 seconds.

[0124] Then, the driving status of the following vehicle is continuously monitored and judged: if the vehicle speed increases continuously for 10 seconds and the distance to the vehicle in front is less than 30 meters, it is judged as the first overtaking intention intensity; if the lane deviation rate is greater than 0.5 m / s and the turn signal is turned on, it is judged as the second overtaking intention intensity; if the acceleration is greater than 2 m / s² and lasts for more than 3 seconds, it is judged as the third overtaking intention intensity; if any two of the above conditions are met at the same time, it is judged as the fourth overtaking intention intensity.

[0125] This embodiment establishes multiple judgment conditions to determine the overtaking intent intensity of a following vehicle. This application considers multiple factors such as vehicle speed changes, distance, lane departure, turn signal usage, and acceleration, and combines these factors to form four different levels of overtaking intent intensity. This multi-dimensional, multi-level judgment method can more comprehensively and accurately assess the overtaking intent of following vehicles, thus providing a more reliable basis for subsequent overtaking risk assessment and alerts.

[0126] As an optional embodiment, S103 may specifically include:

[0127] If the overtaking risk level of the following vehicle meets the overtaking risk conditions, an overtaking prompt message is output in a target manner;

[0128] The target method includes at least one of visual prompting, audio-visual prompting, and inter-vehicle communication prompting, wherein the audio-visual prompting includes illuminating the left turn signal of the target vehicle.

[0129] In this embodiment, the overtaking risk condition is used to characterize the conditions that following vehicles must meet when an overtaking risk warning is required.

[0130] Visual prompts can include displaying warning icons or text on the vehicle's head-up display.

[0131] Audible and visual warnings can be issued through in-vehicle speakers or by flashing exterior warning lights to alert following vehicles. For example, the target vehicle's left turn signal can be illuminated to warn following vehicles that they cannot attempt to overtake.

[0132] Vehicle-to-vehicle communication alerts can utilize vehicle-to-vehicle communication technology to directly send overtaking risk warnings to following vehicles.

[0133] As an example, this application can select different prompting methods or combinations based on the different levels of overtaking risk. For instance, when the overtaking risk level is low, only visual prompts may be used; when the risk level increases, both visual and audio-visual prompts may be enabled simultaneously; and in high-risk situations, all three prompting methods may be used simultaneously to ensure effective communication of information.

[0134] This embodiment effectively solves the problem of how to output overtaking warning information based on the overtaking risk level when the overtaking risk level of the following vehicle meets preset conditions. It provides timely and accurate communication of overtaking risk information to the following vehicle, helping to prevent potential overtaking hazards and improve road safety. Furthermore, the combination of multiple warning methods enhances the reliability and coverage of information transmission, adapting to different driving environments and driver needs.

[0135] Based on the overtaking warning method provided in this application, correspondingly, this application also provides specific embodiments of the overtaking warning device.

[0136] like Figure 3 As shown, the overtaking warning device 300 provided in this application embodiment includes an environment acquisition module 310, a risk judgment module 320, and an overtaking warning module 330.

[0137] The environment acquisition module 310 is used to acquire the target vehicle's driving information and driving environment information;

[0138] The risk assessment module 320 is used to determine the overtaking risk level of the following vehicles based on the target vehicle's driving information and driving environment information when there are following vehicles. The following vehicles are those that are in the same lane as the target vehicle and are behind the target vehicle.

[0139] The overtaking warning module 330 is used to output overtaking warning information based on the overtaking risk level of the following vehicle.

[0140] As an optional embodiment, the driving environment information includes traffic condition information, obstacle information, and surrounding vehicle driving information;

[0141] Risk assessment module 320 includes the following units:

[0142] The first judgment unit is used to determine whether overtaking is permitted under current road conditions based on traffic information, and to obtain the first risk result;

[0143] The second judgment unit is used to determine whether an obstacle will interfere with overtaking, based on obstacle information, when the first risk result indicates that the current road conditions allow overtaking, and to obtain a second risk result.

[0144] The third judgment unit is used to determine the overtaking risk level of the following vehicle based on the driving information of surrounding vehicles and the target vehicle, when the second risk result indicates that the obstacle will not interfere with overtaking.

[0145] As an optional embodiment, the first determination unit is specifically used for:

[0146] Determine the current road conditions based on traffic information;

[0147] If there is a no-overtaking sign or the left lane line of the first lane is a solid line, it is determined that overtaking is not allowed under the current road conditions, and the first lane is the lane where the following vehicle is located.

[0148] If there is no no-overtaking sign in the current road conditions and the left lane line of the first lane is not a solid line, then overtaking is permitted under the current road conditions.

[0149] As an optional embodiment, the second determination unit is specifically used for:

[0150] When the obstacle is a dynamic obstacle, the obstacle's trajectory is generated based on the obstacle information;

[0151] Based on the movement trajectory, predict whether the obstacle will enter the target lane and when it will enter the target lane. The target lane includes the first lane and the second lane. The first lane is the lane where the following vehicle is located, and the second lane is the overtaking lane corresponding to the first lane.

[0152] If an obstacle is about to enter the target lane and its entry into the target lane occurs before a preset time threshold, it is determined that the obstacle will interfere with overtaking.

[0153] If the obstacle will not enter the target lane or enters the target lane later than a preset time threshold, it is determined that the obstacle will not interfere with overtaking.

[0154] As an optional embodiment, the surrounding vehicle driving information includes first driving information of the following vehicle and second driving information of the interfering vehicle, wherein the interfering vehicle is a vehicle located in the overtaking lane of the following vehicle.

[0155] The third judgment unit is specifically used for:

[0156] Based on the initial driving information of the following vehicle, a predicted overtaking trajectory for the following vehicle is generated.

[0157] Based on the second driving information of the interfering vehicle, a predicted driving trajectory of the interfering vehicle is generated;

[0158] Based on the predicted overtaking trajectory and the predicted driving trajectory, the overtaking risk level of the following vehicle is determined.

[0159] As an optional embodiment, when there are following vehicles, before determining the overtaking risk level of the following vehicles based on the target vehicle's driving information and driving environment information, the overtaking warning device 300 further includes the following modules:

[0160] The intent recognition module is used to determine the intensity of the overtaking intent of the following vehicle based on driving environment information;

[0161] Risk assessment module 320 is specifically used for:

[0162] If there are following vehicles on the target vehicle, and the intensity of the following vehicles' overtaking intention meets the preset overtaking conditions, the overtaking risk level of the following vehicles is determined based on the target vehicle's driving information and driving environment information.

[0163] As an optional embodiment, the driving environment information includes at least traffic condition information and surrounding vehicle driving information, and the surrounding vehicle driving information includes at least the first driving information of the following vehicles;

[0164] The intent recognition module is specifically used for:

[0165] The following vehicle's first driving information, the target vehicle's driving information, and traffic conditions are combined to form the overtaking intention vector of the following vehicle.

[0166] The overtaking intention vector is input into the overtaking intention model to obtain the overtaking intention intensity of the following vehicle. The overtaking intention model is a model trained based on the historical overtaking intention vector and the corresponding historical overtaking intention intensity.

[0167] As an optional embodiment, the driving environment information includes at least the driving information of surrounding vehicles, and the driving information of surrounding vehicles includes at least the first driving information of the following vehicles;

[0168] The intent recognition module is specifically used for:

[0169] If the first driving information of the following vehicle meets the first intent condition, it is determined that the following vehicle belongs to the first overtaking intent intensity. The first intent condition is that the speed of the following vehicle continues to increase and the relative distance between the following vehicle and the vehicle in front of the following vehicle is less than the preset safe distance.

[0170] If the first driving information of the following vehicle meets the second intent condition, it is determined that the following vehicle belongs to the second overtaking intent intensity. The second intent condition is that the lane deviation rate of the following vehicle is greater than the preset deviation rate threshold and the following vehicle turns on its turn signal.

[0171] If the first driving information of the following vehicle meets the third intent condition, the following vehicle is determined to be of third overtaking intent intensity. The third intent condition is that the acceleration of the following vehicle is greater than the preset acceleration threshold and lasts for at least the target duration.

[0172] If the first driving information of the following vehicle satisfies at least two of the first intent condition, the second intent condition, and the third intent condition, the following vehicle is determined to have the fourth overtaking intent intensity.

[0173] As an optional embodiment, the overtaking warning module 330 is specifically used for:

[0174] If the overtaking risk level of the following vehicle meets the overtaking risk conditions, an overtaking prompt message is output in a target manner;

[0175] The target method includes at least one of visual prompting, audio-visual prompting, and inter-vehicle communication prompting, wherein the audio-visual prompting includes illuminating the left turn signal of the target vehicle.

[0176] Based on the overtaking warning method provided in this application, correspondingly, this application also provides specific embodiments of the overtaking warning device.

[0177] Figure 4 A schematic diagram of the hardware structure of the overtaking warning device provided in an embodiment of this application is shown.

[0178] The overtaking warning device may include a processor 401 and a memory 402 storing computer program instructions.

[0179] Specifically, the processor 401 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0180] Memory 402 may include mass storage for data or instructions. For example, and not limitingly, memory 402 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 402 may include removable or non-removable (or fixed) media. Where appropriate, memory 402 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 402 is non-volatile solid-state memory.

[0181] The processor 401 reads and executes computer program instructions stored in the memory 402 to implement any of the overtaking prompting methods in the above embodiments.

[0182] In one example, the overtaking warning device may also include a communication interface 403 and a bus 410. For example, Figure 4 As shown, the processor 401, memory 402, and communication interface 403 are connected through bus 410 and complete communication with each other.

[0183] The communication interface 403 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0184] Bus 410 includes hardware, software, or both, that couples components of the overtaking warning device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 410 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.

[0185] Furthermore, in conjunction with the overtaking warning method in the above embodiments, this application embodiment can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the overtaking warning methods in the above embodiments.

[0186] In addition, in conjunction with the overtaking prompting method in the above embodiments, this application embodiment can provide a computer program product to implement it. When the instructions in the computer program product are executed by the processor of the electronic device, the electronic device executes the overtaking prompting method provided by any aspect of the above embodiments of this application.

[0187] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0188] The functional blocks shown in the above-described structural diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0189] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0190] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0191] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A method for providing overtaking guidance, characterized in that, The method includes: Obtain the target vehicle's driving information and driving environment information; If there are following vehicles, the overtaking risk level of the following vehicles is determined based on the driving information of the target vehicle and the driving environment information. The following vehicles are those located in the same lane as the target vehicle and behind the target vehicle. Based on the overtaking risk level of the following vehicle, an overtaking prompt message is output.

2. The method according to claim 1, characterized in that, The driving environment information includes traffic conditions, obstacles, and surrounding vehicle driving information; The step of determining the overtaking risk level of the following vehicle based on the target vehicle's driving information and the driving environment information includes: Based on the traffic information, determine whether overtaking is permitted under current conditions to obtain the first risk result; If the first risk result indicates that the current road conditions allow overtaking, the obstacle information is used to determine whether the obstacle will interfere with overtaking, and a second risk result is obtained. If the second risk result indicates that the obstacle will not interfere with overtaking, the overtaking risk level of the following vehicle is determined based on the driving information of the surrounding vehicles and the driving information of the target vehicle.

3. The method according to claim 2, characterized in that, The step of determining whether overtaking is permitted under the current traffic conditions based on the traffic information, and obtaining a first risk result, includes: Based on the traffic information, determine the current road conditions; If there is a no-overtaking sign or the left lane line of the first lane is a solid line in the current road conditions, it is determined that overtaking is not allowed in the current road conditions, and the first lane is the lane where the following vehicle is located. If there is no no-overtaking sign in the current road conditions and the left lane line of the first lane is not a solid line, then overtaking is permitted under the current road conditions.

4. The method according to claim 2, characterized in that, The step of determining whether an obstacle will interfere with overtaking based on the obstacle information, and obtaining a second risk result, includes: If the obstacle is a dynamic obstacle, the motion trajectory of the obstacle is generated based on the obstacle information; Based on the movement trajectory, it is predicted whether the obstacle will enter the target lane and the time it will enter the target lane. The target lane includes a first lane and a second lane. The first lane is the lane where the following vehicle is located, and the second lane is the overtaking lane corresponding to the first lane. If the obstacle is about to enter the target lane and the time of entry into the target lane is earlier than a preset time threshold, it is determined that the obstacle will interfere with overtaking. If the obstacle does not enter the target lane or enters the target lane later than a preset time threshold, it is determined that the obstacle will not interfere with overtaking.

5. The method according to claim 2, characterized in that, The surrounding vehicle driving information includes first driving information of the following vehicle and second driving information of the interfering vehicle, wherein the interfering vehicle is a vehicle located in the overtaking lane of the following vehicle. The step of determining the overtaking risk level of the following vehicle based on the driving information of surrounding vehicles and the driving information of the target vehicle includes: Based on the first driving information of the following vehicle, a predicted overtaking trajectory of the following vehicle is generated. Based on the second driving information of the interfering vehicle, a predicted driving trajectory of the interfering vehicle is generated; Based on the predicted overtaking trajectory and the predicted driving trajectory, the overtaking risk level of the following vehicle is determined.

6. The method according to claim 1, characterized in that, Before determining the overtaking risk level of the following vehicles based on the target vehicle's driving information and the driving environment information when the target vehicle is followed by other vehicles, the method further includes: Based on the driving environment information, determine the intensity of the overtaking intention of the following vehicle; When the target vehicle is followed by other vehicles, the overtaking risk level of the following vehicles is determined based on the target vehicle's driving information and the driving environment information, including: If there are following vehicles around the target vehicle, and the intensity of the following vehicles' overtaking intention meets the preset overtaking conditions, the overtaking risk level of the following vehicles is determined based on the target vehicle's driving information and the driving environment information.

7. The method according to claim 6, characterized in that, The driving environment information includes at least traffic condition information and surrounding vehicle driving information, and the surrounding vehicle driving information includes at least the first driving information of the following vehicle. Determining the overtaking intent intensity of the following vehicle based on the driving environment information includes: The following vehicle's first driving information, the target vehicle's driving information, and the traffic condition information are used to construct the following vehicle's overtaking intention vector. The overtaking intention vector is input into the overtaking intention model to obtain the overtaking intention intensity of the following vehicle. The overtaking intention model is a model trained based on historical overtaking intention vectors and corresponding historical overtaking intention intensities.

8. The method according to claim 6, characterized in that, The driving environment information includes at least the driving information of surrounding vehicles, and the driving information of surrounding vehicles includes at least the first driving information of the following vehicle. Determining the overtaking intent intensity of the following vehicle based on the driving environment information includes: If the first driving information of the following vehicle meets the first intention condition, it is determined that the following vehicle belongs to the first overtaking intention intensity. The first intention condition is that the speed of the following vehicle continues to increase and the relative distance between the following vehicle and the vehicle in front of the following vehicle is less than a preset safe distance. If the first driving information of the following vehicle meets the second intention condition, it is determined that the following vehicle belongs to the second overtaking intention intensity. The second intention condition is that the lane deviation rate of the following vehicle is greater than a preset deviation rate threshold, and the following vehicle turns on its turn signal. If the first driving information of the following vehicle satisfies the third intent condition, it is determined that the following vehicle belongs to the third overtaking intent intensity. The third intent condition is that the acceleration of the following vehicle is greater than a preset acceleration threshold and lasts for at least the target duration. If the first driving information of the following vehicle satisfies at least two of the first intention condition, the second intention condition, and the third intention condition, the following vehicle is determined to have a fourth overtaking intention intensity.

9. The method according to claim 1, characterized in that, The step of outputting overtaking prompt information based on the overtaking risk level of the following vehicle includes: If the overtaking risk level of the following vehicle meets the overtaking risk conditions, an overtaking prompt message is output in a target manner; The target method includes at least one of visual prompting, audio-visual prompting, and inter-vehicle communication prompting, wherein the audio-visual prompting includes illuminating the left turn signal of the target vehicle.

10. An electronic device, characterized in that, The device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the overtaking prompting method as described in any one of claims 1-9.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions, which, when executed by a processor, implement the overtaking prompting method as described in any one of claims 1-9.

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