Vehicle obstacle avoidance method, electronic device, and vehicle

Abstract

The application provides a vehicle obstacle avoidance method, an electronic device and a vehicle. The current road type can be determined according to the current position of the vehicle, different obstacle avoidance control strategies can be provided for different roads, and the safety of the vehicle driving on different roads is improved. For slow roads, since the vehicle speed is slow, there is no collision risk at this time, so it is necessary to determine the expressway with collision risk in the driving path, and whether the unmanned aerial vehicle needs to be started for auxiliary obstacle avoidance when driving to the corresponding expressway is determined according to the current position and the first predicted vehicle speed. For expressways, the vehicle has a collision risk at this time, so whether the unmanned aerial vehicle needs to be started for auxiliary obstacle avoidance is directly determined according to the current vehicle speed. When the first obstacle avoidance condition or the second obstacle avoidance condition is met, it indicates that the probability of collision risk is high, and starting the unmanned aerial vehicle for auxiliary obstacle avoidance can help the driver to predict the road conditions in front of the vehicle in advance, reduce the collision risk, and improve the driving safety.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and more particularly to a vehicle obstacle avoidance method, electronic equipment, and vehicle. Background Technology

[0002] When a vehicle is traveling at high speed, there may be obstacles on the road that are difficult to see with the naked eye or pedestrians that suddenly appear. Because the braking distance of a vehicle is large at high speeds, users may not be able to avoid obstacles or pedestrians that suddenly appear, which greatly increases the probability of traffic accidents. Summary of the Invention

[0003] In view of this, the purpose of this application is to provide a vehicle obstacle avoidance method, electronic equipment and vehicle for avoiding collisions between the vehicle and obstacles or pedestrians in the blind spot at high vehicle speeds, thereby improving driving safety.

[0004] To achieve the above objectives, this application provides a vehicle obstacle avoidance method, comprising:

[0005] Determine the current road type based on the vehicle's current location;

[0006] In response to the current road type being a slow road, the system determines whether the expressway exists in the driving path based on navigation information. If the expressway exists, the system determines a first predicted speed based on navigation information and determines whether the first obstacle avoidance condition is met based on the current position and the first predicted speed.

[0007] In response to the current road type being an expressway, determine whether the second obstacle avoidance condition is met based on the current vehicle speed;

[0008] In response to the fulfillment of the first obstacle avoidance condition or the second obstacle avoidance condition, the drone is activated, and the drone is controlled to perform road condition monitoring based on the preset detection distance and navigation information, and obstacle avoidance control is performed based on the road condition images monitored by the drone.

[0009] Based on the same inventive concept, this disclosure also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein the processor implements the method described above when executing the computer program.

[0010] Based on the same inventive concept, this disclosure also provides a vehicle including the electronic equipment described above.

[0011] As can be seen from the above, the vehicle obstacle avoidance method, electronic equipment, and vehicle provided in this application can determine the current road type based on the vehicle's current location. When the current road type is a slow road, it determines whether there is an expressway in the driving path based on navigation information. If an expressway exists, it determines a first predicted vehicle speed based on navigation information and determines whether a first obstacle avoidance condition is met based on the current location and the first predicted vehicle speed. When the current road type is an expressway, it determines whether a second obstacle avoidance condition is met based on the current vehicle speed. When either the first or second obstacle avoidance condition is met, a drone is activated, and the drone is controlled to monitor road conditions based on a preset detection distance and navigation information. Obstacle avoidance control is then performed based on the road condition images monitored by the drone. Determining the current road type based on the vehicle's current location can provide different obstacle avoidance control strategies for different roads, thereby improving the safety of the vehicle driving on different roads. For slow roads, since the vehicle speed is low, there is no collision risk at this time. Therefore, it is necessary to identify expressways with collision risks in the driving path and determine whether drone-assisted obstacle avoidance is needed when driving to the corresponding expressway based on the current location and the first predicted vehicle speed. For expressways, where a collision risk already exists, the decision to activate a drone for obstacle avoidance is made based solely on the current vehicle speed. When the first or second obstacle avoidance condition is met, it indicates the vehicle is already or about to be traveling at high speed on the expressway, with a high probability of collision. Activating a drone for obstacle avoidance helps the driver anticipate road conditions ahead, allowing them to brake earlier when obstacles or pedestrians are detected, reducing the risk of collision and improving driving safety. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in this application or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart of a vehicle obstacle avoidance method according to an embodiment of this application;

[0014] Figure 2 This is a schematic diagram illustrating the segmentation of the planned path based on the predicted speed, according to an embodiment of this application.

[0015] Figure 3 A flowchart for determining whether the first obstacle avoidance condition is met in an embodiment of this application;

[0016] Figure 4 A flowchart for determining whether the second obstacle avoidance condition is met in an embodiment of this application;

[0017] Figure 5This is a flowchart illustrating drone-assisted obstacle avoidance at intersections during high-speed driving, as described in an embodiment of this application.

[0018] Figure 6 This is a flowchart illustrating the drone-assisted obstacle avoidance method when the navigation signal strength is weak, as described in this application embodiment.

[0019] Figure 7 This is a flowchart illustrating obstacle avoidance control based on road condition images monitored by an unmanned aerial vehicle (UAV) according to an embodiment of this application.

[0020] Figure 8 This is a schematic diagram of the vehicle obstacle avoidance device according to an embodiment of this application;

[0021] Figure 9 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.

[0023] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this application should have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms "first," "second," and similar terms used in the embodiments of this application do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are only used to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0024] In this article, it is important to understand that any number of elements in the accompanying figures is for illustrative purposes and not for limitation, and any naming is for distinction only and has no limiting meaning.

[0025] Based on the above background description, the following situations also exist in the related technologies:

[0026] Braking distance is a key parameter for evaluating a vehicle's braking performance. It represents the distance a vehicle travels from the moment it begins braking until it comes to a complete stop at a given speed. It is the distance a vehicle travels from the moment the driver applies the brake pedal to the moment the vehicle comes to a complete stop. The shorter the braking distance, the better the vehicle's braking performance.

[0027] Theoretically, when a vehicle brakes, if the tires lock up (i.e., the braking force is greater than or equal to the maximum friction between the tires and the ground, which can be maintained until the vehicle comes to a stop), the braking distance is independent of the vehicle's mass. The braking distance depends primarily on the friction between the tires and the ground and the initial vehicle speed at the start of braking. The magnitude of the friction depends on the coefficient of friction. Assuming the coefficient of friction is μ, then the braking distance S = V 2 / 2gμ, thus it can be seen that the braking distance is directly proportional to the square of the speed and inversely proportional to the coefficient of friction. When the coefficient of friction is constant, the braking distance depends on the vehicle speed. If the vehicle speed increases by 100%, theoretically the braking distance will increase by 400%. If the braking force F is less than the maximum friction force between the tire and the ground, the braking distance S = (mV) / 2gμ. 2 With the vehicle mass m and braking force F remaining constant, if the vehicle speed increases by 1, the braking distance will theoretically increase by 4 times as well (the actual value may have some deviation, because the coefficient of friction between the tire and the ground will change as the braking distance increases).

[0028] Therefore, when a vehicle is traveling at high speed, there may be obstacles on the road that are difficult to see with the naked eye, or pedestrians that suddenly appear. Collisions may occur due to insufficient braking time. The main cause of collisions between vehicles and obstacles or pedestrians is high speed. When the driver notices an obstacle or pedestrian, immediate braking is necessary, but the braking distance at high speeds is also longer, preventing the vehicle from coming to a complete stop before a collision, thus creating a safety risk. However, even on roads where high speeds are permitted, driving at consistently low speeds will result in a poor driving experience. Therefore, alternative obstacle avoidance solutions are needed to prevent collisions with obstacles or pedestrians at high speeds.

[0029] The vehicle obstacle avoidance method, electronic device, and vehicle provided in this application can determine the current road type based on the vehicle's current position and provide different obstacle avoidance control strategies for different roads, thereby improving the safety of the vehicle driving on different roads. For slow roads, where there is no collision risk, it is necessary to identify expressways with collision risks along the driving path and determine whether drone-assisted obstacle avoidance is needed when reaching the corresponding expressway based on the current position and the first predicted vehicle speed. For expressways where there is already a collision risk, the decision to activate the drone for obstacle avoidance is directly based on the current vehicle speed. When the first or second obstacle avoidance condition is met, it indicates that the vehicle is already or about to be traveling at high speed on an expressway, with a high probability of collision. Activating the drone for obstacle avoidance can help the driver predict road conditions ahead, allowing the user to brake in advance when obstacles or pedestrians are detected, reducing the risk of collision and improving driving safety.

[0030] The vehicle obstacle avoidance method provided by the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0031] In some embodiments, such as Figure 1 As shown, a vehicle obstacle avoidance method includes:

[0032] Step 101: Determine the current road type based on the vehicle's current location.

[0033] In practice, the probability of collision risk varies on different roads. For example, highways are wide and do not have tall obstacles that obstruct the view. They are also usually equipped with guardrails on both sides to prevent pedestrians from suddenly running out. When driving at high speeds on highways, users have a wide field of vision with almost no blind spots. Users can subjectively judge whether there is a possibility of collision. Since there are almost no interfering factors that affect the user's subjective judgment, the user's subjective judgment has a high degree of credibility. Therefore, there is almost no risk of collision when driving at high speeds on highways, so there is no need to use drones for obstacle avoidance assistance.

[0034] For some rural roads and scenic area roads, while high speed limits allow vehicles to travel at high speeds, dense trees or other obstacles on both sides of the road may obstruct the user's view, interfering with their subjective judgment and preventing them from understanding subsequent road conditions or perceiving whether there are intersections or other obstacles behind the obstructed view. If there are obstacles or pedestrians behind the obstructed view, or if there is an intersection with vehicles or non-motorized vehicles present, the high speed and long braking distance after the user's view is restored may lead to insufficient time to avoid collisions. Furthermore, some rural roads and scenic area roads may have large potholes or other hard-to-see obstacles. If, at high speeds, the braking distance is greater than the distance between the vehicle and the obstacle when the driver notices it, a collision risk arises, necessitating the use of drones for obstacle avoidance assistance.

[0035] For some slow roads with low speed limits or suitable for driving at low speeds, the vehicle speed is slow and the braking distance is short, so users can brake before a collision to avoid it. In this case, there is no need to turn on the drone for obstacle avoidance assistance.

[0036] Therefore, the current road type determined based on the current location can be used to preliminarily determine whether there is a collision risk at the current moment. The process of determining the current road type is as follows:

[0037] In some embodiments, determining the current road type based on the vehicle's current location includes:

[0038] Step 1011: Determine the planned route and its speed information based on the navigation data.

[0039] In practice, after a user enters their destination into the navigation software, the software provides multiple routes for selection. Once the user makes a selection, the chosen route from the starting point to the destination becomes the planned route. This planned route is determined by reading navigation data. After the user selects a planned route, the navigation software obtains the corresponding route information, including speed information, congestion information, and whether there is construction. The speed information is the vehicle's speed (generally a predicted average speed) across different sections of the planned route.

[0040] For example, such as Figure 2 As shown, the handover position represents the dividing point of the vehicle speed value. That is, the vehicle speed value V1 between the starting position and the handover position is different from the vehicle speed value V2 between the handover position and the destination, and there is a large difference between the vehicle speed values ​​V1 and V2 (if the difference between the vehicle speed values ​​V1 and V2 is small, they can be divided into the same speed segment).

[0041] Step 1012: Divide the planned path into segments based on the speed information to obtain multiple road segments with different predicted speeds, and determine the target road segment that includes the current location.

[0042] In practice, the predicted vehicle speed is a statistical value collected by the navigation software based on the average speed of different vehicles traveling along the planned route. Based on the speed information, the predicted speed for the first segment between the starting position and the junction is V1, and the predicted speed for the second segment between the junction and the destination is V2. Specifically, if the current position is between the starting position and the junction, the first segment is used as the target segment; if the current position is between the junction and the destination, the second segment is used as the target segment.

[0043] Step 1013: In response to the fact that the target road segment is not a highway, determine the target predicted speed corresponding to the target road segment.

[0044] In practice, highways are wide and free of tall trees or other obstacles obstructing the view. Drivers on highways have a wide field of vision with virtually no blind spots, and their subjective judgment is highly reliable. Therefore, there is almost no risk of collision when driving at high speeds on highways, and there is no need to activate drones for obstacle avoidance assistance. Thus, if driving on a highway, the drone obstacle avoidance function should be turned off. Therefore, drones are only needed for obstacle avoidance assistance on non-highway roads. First, it is necessary to determine that the target road segment is not a highway. After determining that the target road segment is not a highway, the type of the target road segment needs to be determined based on the target predicted speed corresponding to that segment. If the target road segment is segment one, the target predicted speed is V1; if the target road segment is segment two, the target predicted speed is V2.

[0045] Step 1014: In response to the target predicted vehicle speed being less than or equal to a preset vehicle speed threshold, the slow road is identified as the current road type.

[0046] In practice, the vehicle speed threshold is the maximum speed at which the vehicle will not collide with obstacles within the user's field of vision after the user actively brakes. If the coefficient of friction between the tires and the ground is 0.8, the relationship between vehicle speed and braking distance is shown in Table 1:

[0047] Table 1. Relationship between vehicle speed and braking distance when the coefficient of friction is 0.8

[0048] Vehicle speed (km / h) Braking distance (m) Vehicle speed (km / h) Braking distance (m) 20 2.0 90 39.7 30 4.7 120 70.9 40 7.9 150 110.7 50 12.3 180 159.4 60 17.7 200 196.8 70 24.1 250 307.6 80 31.5

[0049] If 12.3 meters is taken as the safe braking distance that will not collide with obstacles in the user's field of vision after the user actively brakes, then the vehicle speed threshold corresponding to the safe braking distance is 50 km / h.

[0050] like Figure 2 As shown, if the target road segment is the first road segment between the starting position and the intersection position, the corresponding target predicted vehicle speed is V1. If the target predicted vehicle speed is less than or equal to the preset vehicle speed threshold, it means that the vehicle is traveling at a relatively low speed on the target road segment, and the corresponding braking distance is short. When the user detects an obstacle or pedestrian, the user can brake before the collision to avoid it. In this case, there is no need to activate the drone for obstacle avoidance. If there is no collision risk, the slow road with no collision risk is determined as the current road type.

[0051] Step 1015: In response to the target predicted vehicle speed being greater than the preset vehicle speed threshold, the expressway is identified as the current road type.

[0052] In practice, if the target road segment is the second segment between the intersection and the destination, the corresponding predicted vehicle speed is V2. If the predicted vehicle speed is greater than the preset speed threshold, it indicates that the vehicle is traveling at a relatively high speed on the target road segment, resulting in a longer braking distance. When the user brakes after spotting an obstacle or pedestrian, the high speed and long braking distance may lead to insufficient time for the vehicle to avoid the obstacle, potentially causing a collision. Furthermore, some rural roads and scenic roads may have large potholes or other difficult-to-detect obstacles. If the braking distance is greater than the distance between the vehicle and the obstacle when the driver spots it at high speed, a collision risk arises. In such cases, drone-assisted obstacle avoidance is required. Therefore, highways requiring drone-assisted obstacle avoidance are identified as the current road type.

[0053] Step 102: In response to the current road type being a slow road, determine whether there is an expressway in the driving path based on the navigation information. If there is an expressway, determine the first predicted speed based on the navigation information, and determine whether the first obstacle avoidance condition is met based on the current position and the first predicted speed.

[0054] In practice, if the current road type is a slow road, it means there is no collision risk for the vehicle at that moment. The system then determines whether an expressway exists in the subsequent stages of the journey. If no expressway exists, it means the user's entire journey is on a slow road, and there is no collision risk throughout the entire process; in this case, the corresponding drone-assisted obstacle avoidance function is directly disabled. If an expressway exists, it means the vehicle will subsequently enter the expressway from the slow road. To ensure safety after entering the expressway, collision risk prediction is necessary in advance. This requires determining the first predicted speed based on navigation information. The first predicted speed is the predicted speed for the expressway obtained by the navigation software when the current road type is a slow road and an expressway exists subsequently.

[0055] Based on the initial predicted speed, a preliminary assessment can be made as to whether there is a collision risk after entering the expressway. If the predicted speed is greater than the speed threshold, it means that if the user drives at a speed greater than or equal to the predicted speed after entering the expressway, there will be a collision risk, thus meeting the first obstacle avoidance condition. Conversely, if the predicted speed is less than the threshold, there is no collision risk. Furthermore, the current location can be used to determine the distance the vehicle is from entering the expressway, thus determining the specific time to activate drone-assisted obstacle avoidance.

[0056] For slow roads, there is no risk of collision for the vehicle. Therefore, it is necessary to identify the expressways with collision risks in the driving path, and determine whether to activate drone-assisted obstacle avoidance when driving to the corresponding expressway based on the current position and the first predicted vehicle speed, so as to ensure that there is no risk of collision after the vehicle enters the expressway.

[0057] Step 103: In response to the current road type being an expressway, determine whether the second obstacle avoidance condition is met based on the current vehicle speed.

[0058] In practice, if the current road type is an expressway, it means the vehicle has entered a section where high-speed driving is permitted and most vehicles are traveling at speeds exceeding the speed threshold. In this case, the vehicle's current speed can be used to determine if a collision risk exists. This is because a lower speed results in a shorter braking distance, allowing the user to avoid a collision by braking when a collision risk is detected. Therefore, the current speed can be used to determine if the second obstacle avoidance condition is met. For expressways, where a collision risk already exists, the current speed is used to determine whether to activate a drone for obstacle avoidance assistance, ensuring the vehicle is not at risk of collision while traveling on the expressway.

[0059] Step 104: In response to the fulfillment of the first obstacle avoidance condition or the second obstacle avoidance condition, start the drone, and control the drone to monitor road conditions according to the preset detection distance and navigation information, and perform obstacle avoidance control based on the road condition images monitored by the drone.

[0060] In practice, if the first obstacle avoidance condition is met, it means that the vehicle is about to enter the expressway. It is necessary to determine the specific road conditions of the expressway. At this time, the engine needs to be started to assist in obstacle avoidance. The drone detects the distance in front of the vehicle to detect the road conditions, identify intersections and obstacles in the expressway, and display the road condition images sent by the drone to the driver. Based on the road condition images, the driver can predict the information of intersections and obstacles ahead in advance, so as to brake in advance, reduce the risk of collision, and ensure the safety of the vehicle when traveling at high speed on the expressway.

[0061] If the second obstacle avoidance condition is met, it means that the vehicle is already traveling at a relatively high speed on the expressway. It is necessary to determine the specific road conditions of the expressway. At this time, the engine needs to be started to assist obstacle avoidance. The drone detects the distance in front of the vehicle to detect the road conditions, identify intersections and obstacles in the expressway, and display the road condition images sent by the drone to the driver. Based on the road condition images, the driver can predict the information of intersections and obstacles ahead in advance, so as to brake in advance, reduce the risk of collision, and ensure the safety of the vehicle when traveling at high speed on the expressway.

[0062] Therefore, when the first or second obstacle avoidance conditions are met, it means that the vehicle is already or about to be traveling at high speed on the expressway, and the probability of a collision is high. Activating the drone for obstacle avoidance can help the driver predict the road conditions ahead in advance, so that the user can brake in advance when an obstacle or pedestrian is detected, reduce the risk of collision, and improve driving safety.

[0063] In summary, the vehicle obstacle avoidance method provided in this application can determine the current road type based on the vehicle's current position and provide different obstacle avoidance control strategies for different roads, thereby improving the safety of the vehicle driving on different roads. For slow roads, where there is no collision risk, it is necessary to identify expressways with collision risks along the driving path and determine whether drone-assisted obstacle avoidance is needed when reaching the corresponding expressway based on the current position and the first predicted vehicle speed. For expressways where there is already a collision risk, the decision to activate the drone for obstacle avoidance is directly based on the current vehicle speed. When the first or second obstacle avoidance condition is met, it indicates that the vehicle is already or about to be traveling at high speed on an expressway, with a high probability of collision. Activating the drone for obstacle avoidance can help the driver predict road conditions ahead, allowing the user to brake in advance when obstacles or pedestrians are detected, reducing the risk of collision and improving driving safety.

[0064] In some embodiments, such as Figure 3 As shown, determining whether the first obstacle avoidance condition is met based on the current position and the first predicted vehicle speed includes:

[0065] Step 301: Determine the junction of the slow road and the express road.

[0066] In practice, when driving on the slow road, the average speed of vehicles on the first segment of the slow road is relatively low, prompting the user to be reminded that the current road conditions are more suitable for slow driving. Furthermore, given that other vehicles are also driving at low speeds, the user is unlikely to drive at a high speed. Therefore, users generally drive at a relatively slow speed (e.g., less than 50 km / h) on the slow road. If the traffic conditions on the second segment of the planned route improve, the second segment objectively allows users to drive at a faster speed (e.g., greater than 50 km / h). At this point, the slow road becomes an expressway. The junction between the first and second segments is the intersection of the slow road and the expressway. After passing the junction, the user enters the expressway, where higher speeds are permitted, but the braking distance is greater, posing a corresponding collision risk. Therefore, determining the junction location is necessary to switch between the assessment of a collision risk and the absence of a collision risk.

[0067] Step 302: Determine the distance between the current position and the handover position.

[0068] In practice, since there is a risk of collision after passing the handover point, it is necessary to take precautions to avoid the risk in advance. Therefore, it is necessary to determine the distance between the current position and the handover point to determine how far away there is from entering the expressway where there is a risk of collision.

[0069] Step 303: In response to the interval distance being less than or equal to a preset distance threshold, determine that the first obstacle avoidance condition is met.

[0070] In practice, if the interval distance is less than or equal to a preset distance threshold (e.g., 2km), it means that the vehicle is about to enter the expressway. Since the first predicted speed corresponding to the expressway is greater than the speed threshold, it means that there is a risk of collision after entering the expressway, and risk avoidance needs to be carried out in advance. At this time, it is necessary to activate the drone for obstacle avoidance assistance, so as to obtain the road condition image of the expressway in advance, so that the driver can understand the road condition of the expressway in advance, so as to slow down or brake in advance, avoid collisions with obstacles or pedestrians, and ensure driving safety.

[0071] Step 304: In response to an interval distance greater than a preset distance threshold, when the interval distance is less than or equal to the distance threshold, determine the second predicted vehicle speed based on navigation information, and determine whether the first obstacle avoidance condition is met based on the first predicted vehicle speed and the second predicted vehicle speed.

[0072] In practice, if the interval distance is less than or equal to a preset distance threshold (e.g., 2km), it indicates that the vehicle is still a considerable distance from entering the expressway and will need to wait a longer period before entering. However, the traffic conditions on the second segment of the planned route will change over time (e.g., during rush hour, there are many vehicles, traffic conditions are poor, and the predicted speed is low). Because the speed prediction is based on statistics of the real-time speeds of vehicles on the road, the predicted speed is dynamic. Therefore, when the interval distance is less than or equal to the distance threshold, a second, closer speed prediction is needed to obtain the second predicted speed. The second predicted speed is then used to verify the effectiveness of the first predicted speed, confirming that there is indeed a collision risk after entering the expressway. This avoids the need to activate drone-assisted obstacle avoidance on low-speed sections, prevents frequent drone start-stop cycles caused by short-distance switching between high and low speed sections, improves overall obstacle avoidance performance, and enhances driving safety. The specific effectiveness verification process is as follows:

[0073] In some embodiments, determining whether a first obstacle avoidance condition is met based on a first predicted vehicle speed and a second predicted vehicle speed includes:

[0074] Step 3041: In response to the first predicted vehicle speed being less than or equal to the second predicted vehicle speed, determine that the first obstacle avoidance condition is met.

[0075] In practice, if the first predicted speed is less than or equal to the second predicted speed, it means that the speed predicted at close range is greater than that predicted at long distance. This indicates that vehicles on the expressway are traveling at higher speeds, resulting in longer braking distances and a higher probability of delayed braking, thus increasing the risk of collision. Therefore, there is still a risk of collision on the expressway. Before entering the expressway, it is necessary to take precautions to avoid the risk. Therefore, if the first obstacle avoidance condition is met, it is necessary to activate a drone for obstacle avoidance assistance. The drone can obtain images of the expressway's road conditions in advance so that the driver can understand the road conditions in advance, slow down or brake in advance, avoid collisions with obstacles or pedestrians, and ensure driving safety.

[0076] Step 3042: In response to the first predicted vehicle speed being greater than the second predicted vehicle speed, determine the speed difference between the first predicted vehicle speed and the second predicted vehicle speed.

[0077] In practice, if the first predicted speed is greater than the second predicted speed, it means the speed predicted at closer distances is greater than the speed predicted at farther distances. This indicates that vehicles on the expressway are traveling at slower speeds, resulting in reduced braking distances and potentially causing the expressway to transition into a slower road. In this case, no advance risk avoidance is necessary. There are two scenarios where the second predicted speed might decrease. First, since predicted speeds are dynamic, fluctuations are permissible. These fluctuations might be reasonable dynamic changes, indicating the first predicted speed is valid and the expressway hasn't changed into a slower road, requiring advance collision avoidance. Second, the expressway might change its road type due to special circumstances, becoming a slower road. For example, if an accident occurs on the second section, which was initially an expressway, vehicles passing through this section must slow down to avoid the accident. In this case, since vehicles are traveling at low speeds on the second section, the road type has changed from expressway to slower road, and no advance collision avoidance is needed.

[0078] Therefore, it is necessary to determine the speed difference between the first and second predicted vehicle speeds, and to determine the reason why the second predicted vehicle speed is less than the first predicted vehicle speed based on the magnitude of the speed difference, so as to avoid launching drones to assist in obstacle avoidance on low-speed roads and avoid wasting resources.

[0079] Step 3043: In response to the speed difference being greater than or equal to a preset difference threshold, it is determined that the first obstacle avoidance condition is not met.

[0080] In practice, if the speed difference is greater than or equal to the preset difference threshold and exceeds the normal fluctuation range, it indicates that the second section of the expressway has changed its road type for other reasons. At this time, the expressway has become a slow road. It is determined that there is no collision risk in the second section and there is no need to avoid collision risks in advance. It is determined that the first obstacle avoidance condition is not met and there is no need to avoid risks in advance.

[0081] If the speed difference exceeds the difference threshold and the preset maximum difference, it indicates a significant deviation between the second predicted speed and the first predicted speed on the second road segment. This suggests a potentially serious traffic accident may have occurred on the second road segment. In this case, the user can be proactively prompted to activate the drone for road condition monitoring. The drone can then acquire images of the road ahead in advance, allowing the driver to anticipate the road conditions and avoid serious accidents that could prevent passage. This guides the user to change their planned route, ensuring they reach their destination within the accurately estimated time and improving the user experience while prioritizing driving safety.

[0082] Step 3044: In response to the speed difference being less than a preset difference threshold, determine that the first obstacle avoidance condition is met.

[0083] In practice, if the speed difference is less than the preset difference threshold, it means that the decrease in the second predicted speed is due to normal fluctuations. The judgment of the first predicted speed is accurate, and risk avoidance needs to be carried out in advance before entering the expressway to ensure that the first obstacle avoidance condition is met.

[0084] By verifying the second predicted vehicle speed against the first predicted vehicle speed, it is ensured that drone-assisted obstacle avoidance will not be activated on slow roads, thus avoiding ineffective obstacle avoidance control and reducing resource waste.

[0085] In some embodiments, such as Figure 4 As shown, determining whether the second obstacle avoidance condition is met based on the current vehicle speed includes:

[0086] Step 401: In response to the current vehicle speed being less than a preset vehicle speed threshold, it is determined that the second obstacle avoidance condition is not met.

[0087] In practice, if the current vehicle speed is less than the preset vehicle speed threshold, it means that the user has chosen to drive slowly on the expressway, the braking distance is small, the risk is controllable, there is no need to turn on the drone for obstacle avoidance assistance, and it is determined that the second obstacle avoidance condition is not met.

[0088] Step 402: In response to the current vehicle speed being greater than or equal to a preset vehicle speed threshold, determine whether the vehicle is in a flight restriction area based on the current location.

[0089] In practice, if the current vehicle speed is greater than or equal to the preset speed threshold, it means that the user has chosen to drive at high speed on the expressway, the braking distance is large, and there is a risk of loss of control and collision. It is necessary to activate the drone for obstacle avoidance assistance. However, it is necessary to first determine whether the vehicle is in a restricted flight area to avoid illegal activation of the drone.

[0090] Step 403: In response to the vehicle being in a flight restricted area, determine that the second obstacle avoidance condition is not met, and issue a deceleration warning.

[0091] In practice, if the vehicle is in a restricted flight area, it indicates that the drone is prohibited from taking off at the current location, confirming that the second obstacle avoidance condition is not met, and a deceleration prompt is given to remind the user to slow down, so as to reduce the braking distance, avoid the risk of collision, and improve driving safety.

[0092] Step 404: In response to the vehicle not being in the flight restriction area, determine that the second obstacle avoidance condition is met.

[0093] In practice, if the vehicle is not in a restricted flight area, it means that the current location allows the drone to take off. Once the second obstacle avoidance condition is met, the drone is activated to assist in obstacle avoidance. The drone is controlled to detect road conditions at a preset distance in front of the vehicle, and information such as the images and distances of intersections and obstacles is fed back to the user. The user can predict the road intersections and obstacles ahead in advance, and then brake or slow down in advance when there are obstacles, thereby reducing the risk of collision and improving driving safety.

[0094] In some embodiments, such as Figure 5 As shown, after determining that the current road type is an expressway, the process also includes:

[0095] Step 501: Determine whether there is an intersection within the preset navigation distance based on the current location and navigation information.

[0096] In practice, some road sections have no or indistinct intersection warnings, or blind spots (buildings / trees) that obstruct the view, making it difficult for users to perceive the presence of intersections ahead. If vehicles or pedestrians appear at the intersection, the increased braking distance at high speeds can lead to delayed avoidance and a significant risk of collision, causing losses to both parties. Therefore, it is necessary to understand the road conditions at intersections on highways in advance. First, it is necessary to determine whether there are intersections within a preset navigation distance (e.g., 1.5km). Monitoring intersections that are too far away in advance is not of practical reference value, because as the distance to the intersection increases, the visible length of other roads within the intersection decreases within the user's field of vision, and the user's perception of whether vehicles or pedestrians will appear at other intersections weakens. Therefore, it is only necessary to monitor the corresponding intersection when the vehicle is about to enter the intersection to ensure the effectiveness of advance monitoring.

[0097] Step 502: In response to the existence of an intersection and the current vehicle speed being greater than or equal to a preset vehicle speed threshold, the drone is activated to monitor the intersection and perform obstacle avoidance control based on the intersection images.

[0098] In practice, if there is an intersection and the current vehicle speed is greater than or equal to the preset speed threshold, it means that for intersections that are not easily perceived, if a vehicle or pedestrian appears at the intersection, the large braking distance will cause the driver to be unable to avoid the collision in time, which may lead to a collision. Therefore, it is necessary to activate the drone to assist in obstacle avoidance so that the driver can be aware of the intersection ahead in advance and avoid collisions with vehicles or pedestrians that suddenly appear at the intersection, thus ensuring the driver's safety.

[0099] In some embodiments, such as Figure 6 As shown, before determining the current road type based on the vehicle's current location, the following steps are also included:

[0100] Step 601: Determine the signal strength of the navigation signal.

[0101] In practice, determining the predicted vehicle speed requires ensuring the signal strength of the navigation signal so that the navigation software can collect the driving speed of different vehicles in real time and then make a speed prediction based on the collected speed. Only a good signal strength can guarantee the accuracy and reliability of the first and second predicted vehicle speeds. Therefore, it is necessary to first determine the signal strength of the navigation signal to determine whether the navigation information is reliable.

[0102] Step 602: In response to a signal strength less than or equal to a preset strength threshold and a current vehicle speed greater than or equal to a preset vehicle speed threshold, start the drone and control the drone to monitor road conditions according to the preset detection distance and navigation information, and perform obstacle avoidance control based on the road condition images monitored by the drone.

[0103] In practice, if the signal strength is less than or equal to a preset strength threshold, navigation can be performed using locally cached maps. However, in this case, it will be impossible to collect speed information of other vehicles, making dynamic speed prediction unreliable. The system will then determine whether risk avoidance is necessary based solely on the current speed. If the speed of the vehicle ahead is greater than or equal to the preset speed threshold, the drone will be activated. Based on the preset detection distance and navigation information, the drone will be controlled to monitor road conditions ahead, allowing the driver to understand the road conditions and perform obstacle avoidance control based on the road imagery monitored by the drone, thus preventing collisions and ensuring user driving safety.

[0104] In some embodiments, such as Figure 7 The aforementioned obstacle avoidance control based on road condition images monitored by the drone includes:

[0105] Step 701: Identify obstacles in the road condition images based on the preset obstacle image library.

[0106] In practice, since users cannot constantly watch the display screen, it is necessary to identify obstacles in the road condition images based on a preset obstacle image library to help users identify obstacles in the road condition images and prevent users from not being able to fully utilize the road condition images.

[0107] Step 702: In response to the detection of an obstacle in the road condition image, provide obstacle avoidance prompts based on the obstacle distance and the obstacle avoidance distance between the vehicle, and provide deceleration prompts.

[0108] In practice, if an obstacle is detected in the road condition image, it indicates a collision risk. The obstacle avoidance distance between the obstacle and the vehicle is determined. If the obstacle avoidance distance is less than the preset safe distance (e.g., 300m), an obstacle avoidance warning is issued to inform the user that there is an obstacle ahead, and a deceleration warning is issued to reduce the braking distance, ensuring that the user can brake in time to avoid a collision and ensure driving safety.

[0109] The drone can be retrieved when leaving the expressway or when the user actively recalls it, and then recharged to ensure it can continue to perform obstacle avoidance assistance. Alternatively, it can automatically retrieve the drone when its battery is low and notify the user that the low battery prevents obstacle avoidance assistance, advising the user to drive carefully.

[0110] It should be noted that the method in this embodiment can be executed by a single device, such as a computer or server. The method can also be applied in a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method in this embodiment, and the multiple devices will interact with each other to complete the method described.

[0111] It should be noted that the above description describes some embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0112] Based on the same inventive concept, and corresponding to any of the above embodiments, this application also provides a vehicle obstacle avoidance device.

[0113] refer to Figure 8 Vehicle obstacle avoidance device, including:

[0114] The road classification module 10 is configured to determine the current road type based on the vehicle's current location.

[0115] The slow road obstacle avoidance module 20 is configured to: in response to the current road type being a slow road, determine whether there is an expressway in the driving path based on navigation information; if there is an expressway, determine a first predicted speed based on navigation information; and determine whether the first obstacle avoidance condition is met based on the current position and the first predicted speed.

[0116] The expressway obstacle avoidance module 30 is configured to: in response to the current road type being an expressway, determine whether the second obstacle avoidance condition is met based on the current vehicle speed;

[0117] The drone control module 40 is configured to: start the drone in response to the fulfillment of a first obstacle avoidance condition or a second obstacle avoidance condition, control the drone to monitor road conditions according to a preset detection distance and navigation information, and perform obstacle avoidance control based on the road condition images monitored by the drone.

[0118] For ease of description, the above devices are described in terms of function, divided into various modules. Of course, in implementing this application, the functions of each module can be implemented in one or more software and / or hardware.

[0119] The apparatus of the above embodiments is used to implement the corresponding vehicle obstacle avoidance method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0120] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle obstacle avoidance method described in any of the above embodiments.

[0121] Figure 9 This embodiment illustrates a more specific hardware structure of an electronic device, which may include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, memory 1020, input / output interface 1030, and communication interface 1040 are interconnected internally via the bus 1050.

[0122] The processor 1010 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 the embodiments of this specification.

[0123] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0124] The input / output interface 1030 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components within the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touchscreens, microphones, various sensors, etc., while output devices may include displays, speakers, vibrators, indicator lights, etc.

[0125] The communication interface 1040 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.).

[0126] Bus 1050 includes a pathway for transmitting information between various components of the device, such as processor 1010, memory 1020, input / output interface 1030, and communication interface 1040.

[0127] It should be noted that although the above-described device only shows the processor 1010, memory 1020, input / output interface 1030, communication interface 1040, and bus 1050, 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 embodiments of this specification, and not necessarily all the components shown in the figures.

[0128] The electronic devices described above are used to implement the corresponding vehicle obstacle avoidance methods in any of the foregoing embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0129] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this application also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the vehicle obstacle avoidance method as described in any of the above embodiments.

[0130] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0131] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to execute the vehicle obstacle avoidance method as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0132] Based on the same inventive concept, corresponding to any of the above embodiments, this application also provides a vehicle, including the electronic device or vehicle obstacle avoidance device of the above embodiments, and executes the vehicle obstacle avoidance method as described in any of the above embodiments through the electronic device or vehicle obstacle avoidance device of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0133] It is understood that before using the technical solutions of the various embodiments in this disclosure, users will be informed of the type, scope of use, and usage scenarios of the personal information involved in an appropriate manner, and user authorization will be obtained.

[0134] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose, based on the prompt message, whether to provide personal information to the software or hardware such as electronic devices, applications, servers, or storage media performing the operations of this disclosed technical solution.

[0135] As an optional but not limited implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0136] It is understood that the above notification and user authorization process are merely illustrative and do not constitute a limitation on the implementation of this disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this disclosure.

[0137] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this application is limited to these examples; under the concept of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of this application as described above, which are not provided in detail for the sake of brevity.

[0138] Additionally, to simplify the description and discussion, and to avoid obscuring the embodiments of this application, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Furthermore, the apparatus may be shown in block diagram form to avoid obscuring the embodiments of this application, and this also takes into account the fact that the details of the implementation of these block diagram apparatuses are highly dependent on the platform on which the embodiments of this application will be implemented (i.e., these details should be fully understood by those skilled in the art). While specific details (e.g., circuits) have been set forth to describe exemplary embodiments of this application, it will be apparent to those skilled in the art that the embodiments of this application can be implemented without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0139] Although this application has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0140] The embodiments of this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the claims of this application. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this application should be included within the protection scope of this application.

Claims

1. A vehicle obstacle avoidance method, characterized in that, include: Determine the current road type based on the vehicle's current location; The determination of the current road type based on the vehicle's current location includes: The planned route and its speed information are determined based on navigation data. The planned route is then segmented based on the speed information to obtain multiple road segments with different predicted speeds, and a target road segment containing the current location is identified. If the target road segment is not a highway, a target predicted speed corresponding to the target road segment is determined. If the target predicted speed is less than or equal to a preset speed threshold, a slow road is identified as the current road type. If the target predicted speed is greater than the preset speed threshold, an expressway is identified as the current road type. In response to the current road type being a slow road, the system determines whether there is an expressway in the driving path based on navigation information. If the expressway exists, the system determines a first predicted speed based on navigation information and determines whether the first obstacle avoidance condition is met based on the current position and the first predicted speed. The first predicted speed is the predicted speed of the expressway obtained by the navigation software when the current road type is a slow road and an expressway exists subsequently. In response to the current road type being an expressway, determine whether the second obstacle avoidance condition is met based on the current vehicle speed; In response to the fulfillment of the first obstacle avoidance condition or the second obstacle avoidance condition, the drone is activated, and the drone is controlled to perform road condition monitoring based on the preset detection distance and navigation information, and obstacle avoidance control is performed based on the road condition images monitored by the drone.

2. The vehicle obstacle avoidance method according to claim 1, characterized in that, The step of determining whether the first obstacle avoidance condition is met based on the current position and the first predicted vehicle speed includes: Determine the junction location between the slow road and the fast road; Determine the interval distance between the current position and the intersection position; In response to the interval distance being less than or equal to a preset distance threshold, it is determined that the first obstacle avoidance condition is met; In response to the interval distance being greater than a preset distance threshold, when the interval distance is less than or equal to the distance threshold, a second predicted vehicle speed is determined based on the navigation information, and the first predicted vehicle speed and the second predicted vehicle speed are used to determine whether the first obstacle avoidance condition is met; wherein, the second predicted vehicle speed is the predicted vehicle speed of the expressway obtained by the navigation software performing speed prediction again when the interval distance is less than or equal to the distance threshold.

3. The vehicle obstacle avoidance method according to claim 2, characterized in that, The step of determining whether the first obstacle avoidance condition is met based on the first predicted vehicle speed and the second predicted vehicle speed includes: In response to the first predicted vehicle speed being less than or equal to the second predicted vehicle speed, it is determined that the first obstacle avoidance condition is met; In response to the first predicted vehicle speed being greater than the second predicted vehicle speed, the speed difference between the first predicted vehicle speed and the second predicted vehicle speed is determined; In response to the speed difference being greater than or equal to a preset difference threshold, it is determined that the first obstacle avoidance condition is not met. In response to the speed difference being less than a preset difference threshold, it is determined that the first obstacle avoidance condition is met.

4. The vehicle obstacle avoidance method according to claim 1, characterized in that, The step of determining whether the second obstacle avoidance condition is met based on the current vehicle speed includes: In response to the current vehicle speed being less than a preset vehicle speed threshold, it is determined that the second obstacle avoidance condition is not met; In response to the current vehicle speed being greater than or equal to a preset vehicle speed threshold, the vehicle is determined to be in a flight restriction zone based on its current location. In response to the vehicle being in a restricted flight area, it is determined that the second obstacle avoidance condition is not met, and a deceleration warning is issued; In response to the vehicle not being in a flight restriction area, it is determined that the second obstacle avoidance condition is met.

5. The vehicle obstacle avoidance method according to claim 1, characterized in that, After determining that the current road type is an expressway, the following steps are also included: Based on the current location and the navigation information, determine whether there is an intersection within the preset navigation distance; In response to the existence of an intersection and the current vehicle speed being greater than or equal to a preset vehicle speed threshold, the drone is activated to monitor the intersection and perform obstacle avoidance control based on the intersection imagery.

6. The vehicle obstacle avoidance method according to claim 1, characterized in that, Before determining the current road type based on the vehicle's current location, the following steps are also included: Determine the signal strength of the navigation signal; In response to the signal strength being less than or equal to a preset strength threshold and the current vehicle speed being greater than or equal to a preset vehicle speed threshold, the drone is activated, and the drone is controlled to perform road condition monitoring based on the preset detection distance and navigation information, and obstacle avoidance control is performed based on the road condition images monitored by the drone.

7. The vehicle obstacle avoidance method according to claim 1, characterized in that, The obstacle avoidance control based on the road condition images monitored by the drone includes: Obstacles are identified in the road condition images based on a preset obstacle image library; In response to the detection of an obstacle in the road condition image, an obstacle avoidance prompt is given based on the obstacle avoidance distance between the vehicle and the obstacle, and a deceleration prompt is given.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 7.

9. A vehicle, characterized in that, Including the electronic device as described in claim 8.

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