Vehicle control method and apparatus, storage medium, and electronic device

Abstract

The present disclosure relates to the technical fields of intelligent mine, autonomous driving, and unmanned vehicles, and provides a vehicle control method and apparatus, a storage medium, and an electronic device. The method comprises: upon determination that there is a stationary obstacle that is to have a travel conflict against a target vehicle, detecting whether vehicle-to-everything (V2X) information related to the stationary obstacle is received, and obtaining a detection result; on the basis of the detection result, determining whether the stationary obstacle is a stationary vehicle; upon determination that the stationary obstacle is a stationary vehicle, determining a conflict area corresponding to the target vehicle and the stationary vehicle, and determining a relationship between a predicted advancing direction of the target vehicle and a predicted advancing direction of the stationary vehicle in the conflict area; and determining a travel strategy of the target vehicle on the basis of the relationship between the predicted advancing direction of the target vehicle and the predicted advancing direction of the stationary vehicle in the conflict area, and controlling the target vehicle to travel according to the travel strategy.

Description

Vehicle control method and device, storage medium and electronic equipment Technical Field

[0001] The present disclosure relates to the fields of smart mines, autonomous driving, and unmanned vehicle technology, and more particularly to a vehicle control method and device, a storage medium, and an electronic device. Background Art

[0002] In most unmanned driving mission scenarios, there are usually many autonomous vehicles performing tasks. These vehicles drive according to their respective predicted driving trajectories to perform their respective tasks.

[0003] However, since the vehicles are unmanned, it is inevitable that there will be deadlocks between vehicles while each vehicle is performing its own tasks. Deadlocks will affect the traffic efficiency of the vehicles and the timely execution of the tasks. Summary of the Invention

[0004] In view of this, embodiments of the present disclosure provide a vehicle control method and device, a storage medium, and an electronic device.

[0005] In a first aspect, an embodiment of the present disclosure provides a vehicle control method, comprising: upon determining that there is a static obstacle that is in a driving conflict with a target vehicle, detecting whether vehicle-to-everything (V2X) information related to the static obstacle is received, and obtaining a detection result, wherein the driving conflict indicates that the static obstacle is located on a predicted driving trajectory of the target vehicle or in an area that satisfies a specified distance condition from the predicted driving trajectory of the target vehicle, and the vehicle-to-everything (V2X) information carries the trajectory information; determining whether the static obstacle is a stationary vehicle based on the detection result; upon determining that the static obstacle is a stationary vehicle, determining a conflict area corresponding to the target vehicle and the stationary vehicle, and determining a relationship between a predicted direction of travel of the target vehicle and a predicted direction of travel of the stationary vehicle in the conflict area; determining a driving strategy of the target vehicle based on the relationship between the predicted direction of travel of the target vehicle and the predicted direction of travel of the stationary vehicle in the conflict area, and controlling the target vehicle to drive according to the driving strategy.

[0006] In combination with the first aspect, in certain implementations of the first aspect, determining whether the static obstacle is a stationary vehicle based on the detection result includes: when the detection result indicates that trajectory information related to the static obstacle is received, determining that the static obstacle is a stationary vehicle.

[0007] In combination with the first aspect, in certain implementations of the first aspect, when it is determined that there is a static obstacle that is in a driving conflict with the target vehicle, detecting whether vehicle network V2X information related to the static obstacle is received to obtain a detection result, including: when it is determined that there is a static obstacle that is in a driving conflict with the target vehicle, starting to receive broadcast messages, and detecting whether the received broadcast messages include vehicle network V2X information related to the static obstacle to obtain a detection result; or, when it is determined that there is a static obstacle that is in a driving conflict with the target vehicle, parsing historical broadcast messages received within a specified time range to determine whether the historical broadcast messages include vehicle network V2X information related to the static obstacle to obtain a detection result.

[0008] In combination with the first aspect, in certain implementations of the first aspect, a driving strategy of the target vehicle is determined based on a relationship between a predicted traveling direction of the target vehicle and a predicted traveling direction of the stationary vehicle in the conflict area, including: if the relationship between the predicted traveling direction of the target vehicle and the predicted traveling direction of the stationary vehicle in the conflict area is a facing relationship, determining a parking position of the target vehicle relative to the conflict area, and controlling the target vehicle to drive to the parking position and wait, and then continue driving after the stationary vehicle leaves the conflict area; preferably, the conflict area includes: an area where the predicted driving trajectories of the target vehicle and the stationary vehicle intersect; preferably, if there are multiple areas where the predicted driving trajectories of the target vehicle and the stationary vehicle intersect, the intersection area closest to the stationary vehicle is determined as the conflict area; or, an area where the predicted driving trajectories of the target vehicle and the stationary vehicle do not intersect, but the distance between the two trajectories meets a specified distance condition; preferably, both the target vehicle and the stationary vehicle are autonomous driving vehicles in the target scene.

[0009] In combination with the first aspect, in certain implementations of the first aspect, determining the relationship between the predicted traveling direction of the target vehicle and the predicted traveling direction of the stationary vehicle in the conflict area includes: determining a trajectory intersection angle of the predicted traveling trajectories of the target vehicle and the stationary vehicle along their respective traveling directions; if the trajectory intersection angle is greater than a target angle, determining that the relationship between the predicted traveling direction of the target vehicle and the predicted traveling direction of the stationary vehicle in the conflict area is a facing relationship; if the trajectory intersection angle is less than the target angle, determining that the relationship between the predicted traveling direction of the target vehicle and the predicted traveling direction of the stationary vehicle in the conflict area is a co-directional relationship; preferably, the target angle is less than 90 degrees, and further preferably, the target angle is greater than 85 degrees and less than 90 degrees.

[0010] In combination with the first aspect, in certain implementations of the first aspect, when it is determined that there is a static obstacle that is in a driving conflict with the target vehicle, before detecting whether V2X information related to the static obstacle is received, the method also includes: while the target vehicle is driving based on the predicted driving trajectory of the target vehicle, determining whether there is a static obstacle that is in a driving conflict with the target vehicle based on environmental perception information within the target range obtained by the target vehicle.

[0011] In combination with the first aspect, in certain implementations of the first aspect, the method further includes: if the relationship between the predicted moving direction of the target vehicle and the predicted moving direction of the stationary vehicle in the conflict area is a unidirectional relationship, controlling the target vehicle to continue traveling.

[0012] In a second aspect, an embodiment of the present application provides a vehicle control device, comprising: a detection module, configured to, when determining that there is a static obstacle that is in a driving conflict with a target vehicle, detect whether vehicle-to-everything (V2X) information related to the static obstacle is received, and obtain a detection result, wherein the driving conflict indicates that the static obstacle is located on the predicted driving trajectory of the target vehicle or in an area that satisfies a specified distance condition from the predicted driving trajectory of the target vehicle, and the vehicle-to-everything (V2X) information carries trajectory information; a first determination module, configured to determine, based on the detection result, whether the static obstacle is a stationary vehicle; a second determination module, configured to, when determining that the static obstacle is a stationary vehicle, determine the conflict area corresponding to the target vehicle and the stationary vehicle, and determine the relationship between the predicted traveling direction of the target vehicle and the predicted traveling direction of the stationary vehicle in the conflict area; a third determination module, configured to determine a driving strategy of the target vehicle based on the relationship between the predicted traveling direction of the target vehicle and the predicted traveling direction of the stationary vehicle in the conflict area, and control the target vehicle to drive according to the driving strategy.

[0013] In a third aspect, an embodiment of the present disclosure provides a computer-readable storage medium storing a computer program for executing the vehicle control method described in the first aspect.

[0014] In a fourth aspect, an embodiment of the present disclosure provides an electronic device, comprising: a processor; a memory for storing instructions executable by the processor; and the processor is used to execute the vehicle control method described in the first aspect.

[0015] In the present disclosure, through the V2X information of the Internet of Vehicles, static obstacles located on or in the vicinity of the predicted driving trajectory of the target vehicle can be perceived in advance, so that potential driving conflicts can be detected at an earlier time point, thereby allowing the target vehicle to have more time to react and adjust its driving strategy. Secondly, different types of obstacles have different effects on the driving of the target vehicle. This embodiment can accurately determine whether the static obstacle is a stationary vehicle that may cause a potential deadlock based on the received V2X information, which helps to more accurately assess the risk of potential conflicts. After determining that the static obstacle is a stationary vehicle, the location of the conflict area between the stationary vehicle and the target vehicle is identified, and the relationship between the predicted travel direction of the target vehicle and the stationary vehicle in the conflict area is analyzed. This can more accurately plan the driving strategy of the target vehicle, including but not limited to deceleration, stopping and waiting, etc., to ensure that the target vehicle can pass through the conflict area safely and efficiently, and reduce the risk of head-on collision with stationary vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The above and other purposes, features, and advantages of the present disclosure will become more apparent through a more detailed description of the embodiments of the present disclosure in conjunction with the accompanying drawings. The accompanying drawings are intended to provide a further understanding of the embodiments of the present disclosure and constitute a part of the specification. Together with the embodiments of the present disclosure, they are used to explain the present disclosure and are not intended to limit the present disclosure. In the drawings, the same reference numerals generally represent the same components or steps.

[0017] FIG1a is a schematic diagram showing a vehicle driving in an autonomous driving scenario provided by an embodiment of the present disclosure.

[0018] FIG1b is a schematic diagram showing a vehicle driving in an autonomous driving scenario provided by another embodiment of the present disclosure.

[0019] FIG2 is a flow chart showing a vehicle control method according to an embodiment of the present disclosure.

[0020] FIG3 is a schematic diagram of a process for obtaining detection results according to an embodiment of the present disclosure.

[0021] FIG4 is a schematic diagram showing a flow chart of determining a driving strategy of a target vehicle provided by an embodiment of the present disclosure.

[0022] FIG5 is a schematic diagram showing a flow chart of determining the relationship between the predicted moving direction of a target vehicle and the predicted moving direction of a stationary vehicle according to an embodiment of the present disclosure.

[0023] FIG6 is a schematic structural diagram of a vehicle control device provided in an embodiment of the present disclosure.

[0024] FIG7 is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present disclosure in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present disclosure without making any creative efforts shall fall within the scope of protection of the present disclosure.

[0026] In autonomous mining, vehicles operate in variable locations, and in some operational scenarios, such as crushing sites, the destination is relatively fixed. Due to limited space in these areas, a predefined global trajectory is often the optimal route. In these scenarios, obstacle avoidance is limited, and excessive obstacle avoidance can reduce production efficiency. Therefore, vehicles are expected to strictly adhere to a sequence and follow a global trajectory. In autonomous mining production environments, the preferred method is to have multiple vehicles yield or stop to wait, ensuring orderly passage of vehicles and improving overall operational efficiency.

[0027] Figure 1a illustrates a schematic diagram of vehicle movement in an autonomous driving scenario according to one embodiment of the present disclosure. As shown in Figure 1a , the driving trajectories of vehicles A and B intersect at point e. If both vehicles A and B simultaneously reach point e, a collision will occur, thereby impacting the normal movement of the vehicles. Figure 1b illustrates a schematic diagram of vehicle movement in an autonomous driving scenario according to another embodiment of the present disclosure. As shown in Figure 1b , although the driving trajectories of vehicles C and D do not intersect, in this driving environment, their driving trajectories are relatively close at points f and g, potentially causing a collision or locking of the vehicles.

[0028] Regarding the issues presented in Figures 1a and 1b, although vehicles have a yielding strategy in place to avoid collisions and deadlocks, they may still end up in the deadlock zone due to misdetection or malfunction. When this occurs, other vehicles will follow the rules for handling static obstacles and stop and wait at a prescribed distance. This leads to a problem: once the faulty vehicle has resumed normal function or the misdetection has resolved, the previously stopped and waiting vehicle may remain stationary, unable to determine that the obstacle has been removed, preventing both vehicles from continuing to move. Currently, there are two main strategies for preventing deadlocks between two intelligent agents.

[0029] First, vehicle detour. As mentioned above, this method can effectively solve the problem in large spaces or when there is room to bypass obstacles, but it is difficult to implement in environments with fixed routes and limited space.

[0030] Second, human intervention. This method relies on a human to actively observe whether the disabled vehicle is stuck in the deadlock area for some reason and issue real-time instructions to oncoming vehicles to stop outside the deadlock area. However, this method relies too much on human attention and the real-time nature of the instructions, increasing the workload of dispatchers.

[0031] Considering the particularity of mining scenarios, it is necessary to explore a new method for multi-agent deadlock avoidance in open areas of mines to ensure that multiple autonomous vehicles can operate efficiently and safely on fixed routes.

[0032] In order to solve the above problems, according to an embodiment of the present disclosure, a vehicle control method is provided, which may include: sending the driving route of the self-vehicle (target vehicle) to other vehicles through vehicle-to-vehicle communication technology (V2V), judging whether there is a static vehicle whose current position is on or near the driving route of the self-vehicle, and determining whether the self-vehicle and the static vehicle are in conflict; when it is determined that there is a conflict between the self-vehicle and the static vehicle, judging the relationship between the moving direction of the static vehicle and the moving direction of the self-vehicle, and when the relationship between the directions of the two meets the specified relationship condition, calculating the conflict area through the V2V trajectories of the two, detecting whether the self-vehicle meets the static yield condition, and if so, stopping the self-vehicle outside the conflict area to yield.

[0033] Optionally, when two conflicts are found in the same trajectory according to the above-mentioned v2v trajectory, for a static vehicle, only the first conflict area is effective, and the second conflict area is ignored.

[0034] Optionally, the presence of a static obstacle that conflicts with the ego vehicle can be determined through perception or roadside device detection, and then V2V driving routes transmitted by other vehicles can be received to determine whether the static obstacle is a stationary vehicle. For example, roadside devices located on different road sections can acquire at least one type of data, such as video, image, or point cloud data, for the corresponding road section. Based on this data, the presence of a static obstacle can be determined and transmitted to the ego vehicle. Alternatively, this data can be transmitted to the ego vehicle, which then processes the data to determine whether a static obstacle exists on the corresponding road section. Optionally, the roadside device transmitting this data to the ego vehicle can be one that meets specified conditions from the ego vehicle. The specified conditions can include the distance between the roadside device and the ego vehicle not exceeding a first specified distance, or the distance between the road section corresponding to the roadside device and the ego vehicle not exceeding a second specified distance. The roadside device and the ego vehicle can communicate using vehicle-to-everything (V2X) technology (V2V or vehicle-to-network (V2N) communication technology).

[0035] This embodiment avoids deadlock situations when vehicles are traveling in opposite directions in a mining scenario.

[0036] Figure 2 is a flow chart of a vehicle control method according to an embodiment of the present disclosure. As shown in Figure 2, the method includes the following steps.

[0037] Step S210: When it is determined that there is a static obstacle that conflicts with the target vehicle, it is detected whether V2X information related to the static obstacle is received to obtain a detection result.

[0038] Static obstacles refer to obstacles in a stationary state, including stationary vehicles and non-stationary vehicles, wherein non-stationary vehicles include objects that are fixed on the road but may affect the driving of the target vehicle or are potentially dangerous. For example, non-stationary vehicles include: road facilities, underground passages, roadblocks, fixed cameras, rocks, hills, etc. Driving conflict means that the static obstacle is located on the predicted driving trajectory of the target vehicle, or is located in an area that meets the specified distance condition from the predicted driving trajectory of the target vehicle. The predicted driving trajectory is an expected driving path calculated based on the current state of the target vehicle (such as position, speed, direction, etc.) and possible future actions (such as acceleration, deceleration, turning, etc.). The specified distance condition is a safety threshold used to determine which static obstacles may pose a potential risk to the driving of the target vehicle.

[0039] V2X information refers to the information that vehicles use to interact and communicate with their surroundings, other vehicles, and infrastructure. In this embodiment, V2X information also includes trajectory information. This information allows the target vehicle to understand the future movements of surrounding static vehicles, which are considered static obstacles, and more accurately determine whether there is a driving conflict.

[0040] Step S220: Determine whether the static obstacle is a stationary vehicle based on the detection result.

[0041] In one example, a check is first performed to determine whether V2X information related to a static obstacle that is in conflict with the target vehicle has been received. If the detection result indicates that the information has been received, key data such as the obstacle identification, location, and type in the V2X information can be further analyzed to determine whether the static obstacle is a stationary vehicle. If the detection result indicates that no V2X information related to the static obstacle has been received, this indicates that the static obstacle is a non-stationary vehicle. Furthermore, V2X information may be lost or blocked during transmission. In this case, the type and status of the static obstacle can be further determined based on sensors (such as radar, cameras, etc.).

[0042] In another example, in a vehicle operating environment in a mining scenario, pedestrians are less likely to move in the environment, and the infrastructure in the environment is generally immovable. Therefore, if trajectory information related to a static obstacle is received, it can be determined that the static obstacle is a stationary vehicle.

[0043] Step S230 , when it is determined that the static obstacle is a stationary vehicle, a conflict area corresponding to the target vehicle and the stationary vehicle is determined, and a relationship between the predicted moving direction of the target vehicle and the predicted moving direction of the stationary vehicle in the conflict area is determined.

[0044] A conflict area refers to an area where there is a risk of collision between a target vehicle and a stationary vehicle. Exemplarily, a method for determining the conflict area corresponding to a target vehicle and a stationary vehicle includes: determining the positions of the target vehicle and the stationary vehicle; calculating the minimum safe distance between the two based on the positions of the target vehicle and the stationary vehicle; and determining a circular area centered on the target vehicle and with a radius of the minimum safe distance as the conflict area. Alternatively, a predicted driving area of ​​the stationary vehicle may be determined based on the predicted trajectory of the stationary vehicle and its bounding box information; a target driving area of ​​the target vehicle may be determined based on the predicted driving trajectory of the target vehicle and vehicle driving safety parameters; and a collision risk area, also known as a conflict area, may be determined based on the intersection area between the predicted driving area and the target driving area. Optionally, determining the collision risk area based on the intersection area between the predicted driving area and the target driving area may also include: determining the intersection area between the predicted driving area and the target driving area, and correcting the intersection area based on the sensor coordinates on the vehicle and the vehicle coordinates to obtain a target collision risk area as the conflict area.

[0045] For example, within the conflict zone, the target vehicle and the stationary vehicle's direction of travel are predicted using a machine learning algorithm or motion model based on their motion states and historical data. In one example, if the predicted directions of the target vehicle and the stationary vehicle intersect or overlap, the risk of collision within the conflict zone is high, and appropriate measures must be taken to ensure safety.

[0046] Step S240 , determining a driving strategy of the target vehicle based on the relationship between the predicted moving direction of the target vehicle and the predicted moving direction of the stationary vehicle in the conflict area, and controlling the target vehicle to drive according to the driving strategy.

[0047] In one exemplary embodiment, if there is no conflict between the predicted moving direction of the target vehicle and the predicted moving direction of the stationary vehicle, the driving strategy of the target vehicle is to maintain the current driving state without taking special measures, where the current driving state includes the current driving speed, direction, trajectory, etc.

[0048] In another example, although the target vehicle's predicted direction of travel does not directly conflict with a stationary vehicle, the target vehicle still needs to slow down or stop to avoid the stationary vehicle due to the current driving environment (such as poor visibility or narrow road conditions). In this case, the target vehicle's driving strategy is to gradually reduce its speed until it comes to a complete stop, ensuring that it does not collide or encounter any other dangerous situations when passing through the conflict area.

[0049] Understandably, the target vehicle's driving strategy requires comprehensive consideration and optimization based on the specific mining environment, traffic regulations, and the performance of the autonomous driving system. Furthermore, to ensure operational safety and reliability, the autonomous driving system must be thoroughly tested and validated, with continuous feedback collected during actual operation to facilitate system optimization and updates.

[0050] In this embodiment, through the use of V2X information from the Internet of Vehicles (IoV), static obstacles located on or near the target vehicle's predicted driving trajectory can be perceived in advance, facilitating the detection of potential driving conflicts at an earlier point in time, thereby allowing the target vehicle more time to react and adjust its driving strategy. Secondly, different types of obstacles have different impacts on the target vehicle's driving. Based on the received V2X information, this embodiment can accurately determine whether a static obstacle is a stationary vehicle that may cause a potential deadlock, helping to more accurately assess the risk of potential conflicts. After determining that the static obstacle is a stationary vehicle, the location of the conflict area between the stationary vehicle and the target vehicle is identified, and the relationship between the predicted travel directions of the target vehicle and the stationary vehicle in the conflict area is analyzed. This allows for more intelligent planning of the target vehicle's driving strategy, including deceleration, stopping and waiting, etc., to ensure that the target vehicle can safely and efficiently pass through the conflict area, reducing the risk of collision with stationary vehicles.

[0051] In combination with the embodiment shown in Figure 2, in other embodiments of the present disclosure, before executing step S210, it also includes: in the process of the target vehicle driving based on the predicted driving trajectory of the target vehicle, based on the environmental perception information within the target range obtained by the target vehicle, determining whether there is a static obstacle that conflicts with the target vehicle.

[0052] Specifically, environmental perception information within the target range refers to key information such as the position, speed, and direction of dynamic and static objects such as roads, obstacles, other vehicles, and pedestrians within a certain range around the target vehicle. This information can help the target vehicle understand the current driving environment, thereby making correct decisions and planning a safe driving trajectory.

[0053] For example, a target vehicle can use sensor technology and perception algorithms to obtain environmental perception information within its target range. Regarding sensor technology, the target vehicle is equipped with a variety of sensors, such as lidar, cameras, and millimeter-wave radar. These sensors can scan the surrounding environment in real time and collect a large amount of data. For example, lidar emits a laser beam and measures the time it takes for it to reflect back, accurately determining the distance and position of the target object. Cameras capture images of the road and obstacles, providing rich visual information for the autonomous driving system. Perception algorithms process and analyze the raw data collected by the sensors to extract useful information. For example, object detection and recognition algorithms can analyze and identify surrounding vehicles, pedestrians, and other obstacles; object tracking algorithms can estimate and predict the motion trajectories of these objects; and environmental modeling algorithms can construct a model of the vehicle's surroundings based on the perceived information, providing a basis for decision-making and planning.

[0054] This embodiment uses real-time perception of static obstacles within the target range to enable the target vehicle to identify potential driving conflicts and adjust its driving trajectory to avoid collisions with these static obstacles, thereby improving road safety. By continuously collecting and analyzing environmental perception information, the target vehicle's ability to identify and respond to various static obstacles can be gradually improved, allowing the target vehicle's autonomous driving system to maintain stable performance in a variety of complex environments.

[0055] FIG3 is a flow chart of obtaining a detection result according to an embodiment of the present disclosure. FIG3 is an extension of the embodiment shown in FIG2 . The following focuses on the differences between the embodiment shown in FIG3 and the embodiment shown in FIG2 , and the similarities are not repeated.

[0056] As shown in FIG3 , when it is determined that there is a static obstacle that conflicts with the target vehicle, detecting whether V2X information related to the static obstacle is received and obtaining a detection result includes the following steps.

[0057] Step S310: When it is determined that there is a static obstacle that conflicts with the target vehicle, start receiving broadcast messages and detect whether the received broadcast messages include V2X information related to the static obstacle to obtain a detection result.

[0058] In the field of connected vehicles, broadcast messaging refers to the transmission of information between vehicles, road infrastructure, and other traffic participants to relevant recipients via wireless communication technology. These messages include traffic conditions, road safety warnings, and vehicle status. Broadcast messaging allows target vehicles to obtain real-time dynamic information about their surroundings, including the location, speed, and direction of other vehicles. Upon determining the presence of a static obstacle posing a driving conflict with the target vehicle, the target vehicle receives the broadcast message and detects V2X information related to the static obstacle, enabling it to react promptly and avoid potential collisions.

[0059] In one example, a method for detecting whether V2X information related to static obstacles is present in a received broadcast message includes: first, parsing the received broadcast message. After parsing the data, information related to static obstacles is filtered out based on predefined message types and identifiers. For example, specific fields or tags can be set to identify information related to static obstacles. By examining these fields or tags, relevant data segments can be quickly located. Furthermore, the received attributes, such as location and type, are matched and identified with known static obstacle databases. If a match is found, the information is confirmed to be V2X information related to static obstacles.

[0060] In step S320, if it is determined that there is a static obstacle that conflicts with the target vehicle, historical broadcast messages received within a specified time range are parsed to determine whether the historical broadcast messages include V2X information related to the static obstacle, and a detection result is obtained.

[0061] Historical broadcast messages received within a specified range are a record of broadcast messages received by the target vehicle from other vehicles or road infrastructure within a specified time span. These broadcast messages also contain various traffic-related information, such as vehicle location, speed, and direction of travel.

[0062] Generally speaking, non-vehicle static obstacles (i.e., non-stationary vehicles) do not transmit V2X information themselves, but other vehicles or infrastructure may detect these obstacles and transmit relevant information. Therefore, by parsing historical broadcast messages received within a specified time range and searching for information related to static obstacles, it is possible to determine whether there are static obstacles that conflict with the target vehicle, and thus make appropriate driving decisions or provide warnings.

[0063] In one example, a method for analyzing static obstacle information in historical broadcast messages includes: first, collecting historical broadcast messages within a specified time range, which may come from other vehicles, roadside units, or traffic management systems. The collected data is preprocessed to ensure the quality and consistency of the data. The content of the broadcast message is parsed to extract information related to the static obstacles, including location, size, type (such as buildings, trees, traffic facilities, etc.) and static attributes of the obstacle, timestamp, sender identity, and signal strength. The features in the historical broadcast messages are analyzed using pattern recognition or machine learning algorithms to identify static obstacles. For known types of static obstacles, predefined models or rules can be used for matching and identification, and for unknown types of obstacles, unsupervised learning methods can be used for clustering or anomaly detection.

[0064] It can be understood that step S310 and step S320 are two parallel schemes for determining the detection results. Among them, step S310 describes that after sensing a static obstacle, the detection broadcast message is started to obtain the detection result; step S320 describes that during the vehicle's driving process, V2X communication will be carried out in real time, that is, other vehicles will communicate with the target vehicle through V2X in real time, and then the target vehicle's perception system will determine the static obstacle. At this time, the detection result can be obtained based on the historical broadcast messages that have been received before. For step S320, especially when the target vehicle is blocked by a bend or other obstacles and its perception and recognition are affected, the accuracy of the detection result can be improved.

[0065] Step S310, after determining the presence of a static obstacle posing a conflicting threat to the target vehicle, initiates the process of receiving broadcast messages and detecting in real time whether the broadcast messages contain V2X information related to the static obstacle. This approach offers the advantage of high real-time performance. Upon receiving a broadcast message containing static obstacle information, the target vehicle can immediately respond, such as by adjusting its trajectory or speed, to avoid a collision with the static obstacle.

[0066] This implementation of step S320 focuses more on the comprehensiveness of information and the use of historical data. By analyzing historical data, the target vehicle can understand more details such as the location, size, and existence time of the static obstacle, which is very helpful for the vehicle to make more accurate decisions.

[0067] These two approaches complement each other, providing more comprehensive and accurate information. Real-time detection ensures the target vehicle's immediate response to static obstacles, while historical data analysis provides more detailed and in-depth information, helping the target vehicle make more accurate decisions and significantly improving vehicle safety and stability.

[0068] FIG4 is a flow chart illustrating a method for determining a target vehicle's driving strategy according to an embodiment of the present disclosure. FIG4 is an extension of FIG2 . The following focuses on the differences between FIG4 and FIG2 , and the similarities are omitted.

[0069] As shown in FIG4 , in this embodiment, the driving strategy of the target vehicle is determined according to the relationship between the predicted moving direction of the target vehicle and the predicted moving direction of the stationary vehicle in the conflict area, including the following steps.

[0070] Step S410 , determining the relationship between the predicted moving direction of the target vehicle and the predicted moving direction of the stationary vehicle in the conflict area.

[0071] The conflict area includes: the area where the predicted driving trajectories of the target vehicle and the stationary vehicle intersect. If the predicted driving trajectories of the target vehicle and the stationary vehicle intersect in multiple areas, the intersection area closest to the stationary vehicle is determined as the conflict area; or the area where the predicted driving trajectories of the target vehicle and the stationary vehicle do not intersect, but the distance between the two trajectories meets the specified distance condition.

[0072] Specifically, when the predicted driving trajectories of the target vehicle and the stationary vehicle intersect, it means that the two vehicles may meet at a certain time or place in the future. In this case, the intersection area is considered to be a conflict area. When there are multiple intersection areas, the closer to the stationary vehicle, the greater the potential collision risk. Therefore, the intersection area closest to the stationary vehicle is determined as the conflict area. In addition, sometimes although the predicted driving trajectories of the target vehicle and the stationary vehicle do not intersect, the distance between the two trajectories is close enough (that is, the specified distance condition is met), which may also lead to a collision in certain emergencies or operational errors. Therefore, this area is also determined as a conflict area.

[0073] The target vehicle and the stationary vehicle are both autonomous driving vehicles in the target scene. For example, the relationship between the predicted direction of travel of the target vehicle and the predicted direction of travel of the stationary vehicle includes a same-direction relationship, an opposite-direction relationship, an intersection relationship, a perpendicular relationship, and the like. Generally, a same-direction relationship means that the predicted directions of travel of the target vehicle and the stationary vehicle are roughly the same or similar; an opposite-direction relationship means that the predicted directions of travel of the target vehicle and the stationary vehicle are completely opposite; an intersection relationship means that the predicted directions of travel of the target vehicle and the stationary vehicle intersect, in which case the target vehicle needs to cross the direction of the stationary vehicle; a perpendicular relationship means that the predicted directions of travel of the target vehicle and the stationary vehicle are perpendicular, in which case the target vehicle needs to turn or go straight through the intersection or road where the stationary vehicle is located.

[0074] In actual situations, if the judgment result of step S410 is an opposite direction relationship, step S420 is executed; if the judgment result of step S410 is a same direction relationship, step S430 is executed.

[0075] Step S420: Determine the parking position of the target vehicle relative to the conflict area, and control the target vehicle to drive to the parking position and stop and wait until the stationary vehicle leaves the conflict area before continuing to drive.

[0076] The parking position refers to a safe location outside the conflict area where the target vehicle stops and waits to avoid collision with stationary vehicles while not hindering the normal driving of other vehicles.

[0077] For example, factors such as the size of the conflict zone, the target vehicle's size and maneuverability, road and traffic conditions, and the location and status of stationary vehicles can be considered when determining a parking location. Specifically, the size of the conflict zone determines how far the target vehicle must be from the stationary vehicle to ensure safety. Different vehicle sizes and maneuverability affect the required safe stopping distance. Road width, curvature, and traffic flow factors also influence the choice of parking location. Furthermore, the specific location of the stationary vehicle and its potential for movement are also factors to consider when determining the parking location. For example, the parking location is 10 to 30 meters from the conflict zone.

[0078] Once the parking position is determined, the target vehicle needs to control itself to drive to the position and then safely stop and wait. During the parking and waiting period, the target vehicle can turn on hazard warnings to alert other road users.

[0079] Step S430: Control the target vehicle to continue traveling.

[0080] Because the target vehicle and the stationary vehicle are traveling in the same direction, they are not heading directly towards each other, so there is no direct risk of collision. In this case, if the target vehicle stops or slows down, it may cause unnecessary traffic congestion or interfere with other vehicles traveling normally. Therefore, the target vehicle can safely continue to travel in its predicted direction of travel. However, the target vehicle must still remain alert to its surroundings and be prepared to respond to possible emergencies, such as the stationary vehicle suddenly starting or changing lanes. In addition, if the target vehicle is traveling too fast or too close to the stationary vehicle, it will also need to slow down or adjust its driving trajectory appropriately to ensure safety.

[0081] In this embodiment, when the predicted travel directions of the target vehicle and a stationary vehicle are opposite, the target vehicle can effectively avoid the potential collision risk between the two vehicles by determining a parking position and controlling the target vehicle to stop and wait at that location. This strategy reduces the probability of traffic accidents and improves driving safety. While the target vehicle is waiting, the stationary vehicle has the opportunity to leave the conflict area. Once the stationary vehicle leaves, the target vehicle can continue driving, thus avoiding traffic congestion caused by the two vehicles facing each other. When facing stationary vehicles traveling in opposite directions, determining a parking position and controlling the target vehicle to stop and wait allows the target vehicle to more easily navigate complex traffic situations. When the predicted travel paths of the target vehicle and the stationary vehicle do not intersect, but the distance between the two paths meets a specified distance condition, this means that even in seemingly non-conflicting situations, potential risks can be identified in advance and preventive measures can be taken. This helps reduce the need for emergency braking or lane changes due to unexpected situations, thereby improving overall driving efficiency.

[0082] FIG5 is a schematic diagram illustrating a flow chart for determining the relationship between the predicted travel direction of a target vehicle and the predicted travel direction of a stationary vehicle, according to an embodiment of the present disclosure. The embodiment of FIG5 is an extension of the embodiment of FIG2 . The following focuses on the differences between the embodiment of FIG5 and the embodiment of FIG2 , and the similarities are not repeated.

[0083] As shown in FIG5 , in this embodiment, determining the relationship between the predicted moving direction of the target vehicle and the predicted moving direction of the stationary vehicle in the conflict area includes the following steps.

[0084] Step S510 , determining the trajectory intersection angle of the predicted driving trajectories of the target vehicle and the stationary vehicle along their respective driving directions.

[0085] For example, the current position, speed, direction and other motion data of the target vehicle and the stationary vehicle are obtained through the vehicle's sensors and the Global Positioning System (GPS) system. Based on the acquired vehicle motion data, a kinematic model or a machine learning algorithm is used to predict the vehicle's driving trajectory within a certain period of time in the future. By comparing the predicted driving trajectories of the target vehicle and the stationary vehicle, it can be determined whether they will intersect in the future. If they do intersect, it is necessary to calculate the specific trajectory intersection point. Once the trajectory intersection point is determined, the angle between the driving directions of the target vehicle and the stationary vehicle at the intersection point can be calculated. This angle is the trajectory intersection angle. Usually, this angle can be obtained by calculating the angle between two straight lines, where the two straight lines represent the driving directions of the target vehicle and the stationary vehicle at the intersection point, respectively.

[0086] It’s important to note that because vehicle motion is affected by multiple factors (such as road conditions, driver operation, and wind speed), the predicted trajectory and intersection angle will contain certain errors and uncertainties. Therefore, in practical applications, methods such as probabilistic models or fuzzy logic can be used to handle these uncertainties.

[0087] Step S520: Determine the relationship between the track intersection angle and the target angle.

[0088] Specifically, the target angle selection requires comprehensive consideration of factors such as road conditions and vehicle speed to ensure traffic safety and driving efficiency. Generally speaking, the target angle should be set to clearly distinguish between vehicles traveling in opposite and same directions. For example, the target angle is less than 90 degrees. More preferably, the target angle is greater than 85 degrees and less than 90 degrees.

[0089] In actual situations, if the result of step S520 is that the track intersection angle is greater than the target angle, step S530 is executed; if the result of step S530 is that the track intersection angle is less than the target angle, step S540 is executed.

[0090] Step S530 : determining that the relationship between the predicted moving direction of the target vehicle and the predicted moving direction of the stationary vehicle in the conflict area is a facing relationship.

[0091] Specifically, when the trajectory intersection angle is greater than the target angle, it means the target vehicle and the stationary vehicle are traveling in significantly different directions, indicating a clear trend of moving toward each other. In this case, the two vehicles may collide in the future, so it is necessary to determine that they are moving toward each other so that appropriate safety measures can be taken, such as stopping and waiting or slowing down to avoid the problem.

[0092] Step S540 : determining that the relationship between the predicted moving direction of the target vehicle and the predicted moving direction of the stationary vehicle in the conflict area is a same-direction relationship.

[0093] When the trajectory intersection angle is less than the target angle, the target vehicle and the stationary vehicle are traveling in the same direction, indicating that they are traveling in the same direction. In this case, a direct collision between the two vehicles is unlikely, and the target vehicle is allowed to continue traveling.

[0094] In this embodiment, by precisely calculating the trajectory intersection angle, the direction of travel relationship between the target vehicle and the stationary vehicle can be accurately determined. When it is determined that they are in opposite directions, preventive measures such as slowing down, avoiding, or stopping to wait can be taken in a timely manner, thereby avoiding potential collision risks and significantly improving driving safety. When it is determined that they are in the same direction, it means that the target vehicle and the stationary vehicle are traveling in roughly the same direction. In this case, the target vehicle can continue to drive without taking additional avoidance measures, thereby maintaining the smooth flow of traffic and reducing unnecessary traffic congestion. Using this judgment basis helps to optimize traffic flow, reduce the occurrence of traffic accidents, and improve overall traffic management efficiency.

[0095] The vehicle control method embodiment of the present disclosure is described in detail above in conjunction with Figures 2 to 5 . The vehicle control device embodiment of the present disclosure is described in detail below in conjunction with Figure 6 . It should be understood that the description of the vehicle control method embodiment corresponds to the description of the vehicle control device embodiment. Therefore, for portions not described in detail, reference can be made to the aforementioned method embodiment.

[0096] FIG6 is a schematic diagram of the structure of a vehicle control device according to an embodiment of the present disclosure. As shown in FIG6 , a vehicle control device 60 according to an embodiment of the present disclosure includes:

[0097] a detection module 610 configured to, upon determining that a static obstacle is in a driving conflict with the target vehicle, detect whether V2X information related to the static obstacle is received, and obtain a detection result, wherein a driving conflict indicates that the static obstacle is located on the predicted driving trajectory of the target vehicle or within an area that satisfies a specified distance condition from the predicted driving trajectory of the target vehicle, and the V2X information carries trajectory information;

[0098] A first determination module 620 is configured to determine whether the static obstacle is a stationary vehicle based on the detection result;

[0099] The second determining module 630 is configured to determine a conflict area corresponding to the target vehicle and the stationary vehicle when the static obstacle is determined to be a stationary vehicle, and determine a relationship between a predicted traveling direction of the target vehicle and a predicted traveling direction of the stationary vehicle in the conflict area;

[0100] The third determination module 640 is configured to determine a driving strategy of the target vehicle based on a relationship between the predicted moving direction of the target vehicle and the predicted moving direction of the stationary vehicle in the conflict area, and control the target vehicle to drive according to the driving strategy.

[0101] In an embodiment of the present disclosure, the first determining module 620 is further configured to determine that the static obstacle is a stationary vehicle when the detection result indicates that trajectory information related to the static obstacle is received.

[0102] In one embodiment of the present disclosure, the detection module 610 is further configured to, when it is determined that there is a static obstacle that is in a driving conflict with the target vehicle, start receiving broadcast messages, and detect whether the received broadcast messages include vehicle-to-everything (V2X) information related to the static obstacle to obtain a detection result; or, when it is determined that there is a static obstacle that is in a driving conflict with the target vehicle, parse historical broadcast messages received within a specified time range to determine whether the historical broadcast messages include vehicle-to-everything (V2X) information related to the static obstacle to obtain a detection result.

[0103] In one embodiment of the present disclosure, the third determination module 640 is further configured to, if the relationship between the predicted traveling direction of the target vehicle and the predicted traveling direction of the stationary vehicle in the conflict area is a facing relationship, determine the parking position of the target vehicle relative to the conflict area, and control the target vehicle to drive to the parking position and stop and wait, and continue driving after the stationary vehicle leaves the conflict area; preferably, the conflict area includes: an area where the predicted driving trajectories of the target vehicle and the stationary vehicle intersect, preferably, if there are multiple areas where the predicted driving trajectories of the target vehicle and the stationary vehicle intersect, then the intersection area closest to the stationary vehicle is determined as the conflict area; or, an area where the predicted driving trajectories of the target vehicle and the stationary vehicle do not intersect, but the distance between the two trajectories meets a specified distance condition.

[0104] In one embodiment of the present disclosure, the second determination module 630 is further configured to determine a trajectory intersection angle of the predicted driving trajectories of the target vehicle and the stationary vehicle along their respective driving directions; if the trajectory intersection angle is greater than a target angle, it is determined that the relationship between the predicted traveling direction of the target vehicle and the predicted traveling direction of the stationary vehicle in the conflict area is a facing relationship; if the trajectory intersection angle is less than the target angle, it is determined that the relationship between the predicted traveling direction of the target vehicle and the predicted traveling direction of the stationary vehicle in the conflict area is a same-direction relationship; preferably, the target angle is less than 90 degrees, and further preferably, the target angle is greater than 85 degrees and less than 90 degrees.

[0105] In one embodiment of the present disclosure, the detection module 610 is also configured to determine whether there is a static obstacle that conflicts with the target vehicle based on the environmental perception information within the target range obtained by the target vehicle during the target vehicle's driving based on the predicted driving trajectory of the target vehicle.

[0106] In one embodiment of the present disclosure, the third determination module 630 is further configured to control the target vehicle to continue traveling if the relationship between the predicted moving direction of the target vehicle and the predicted moving direction of the stationary vehicle in the conflict area is a unidirectional relationship.

[0107] The electronic device according to an embodiment of the present disclosure is described below with reference to Figure 7. Figure 7 is a schematic structural diagram of an electronic device provided by an exemplary embodiment of the present disclosure.

[0108] As shown in FIG. 7 , the electronic device 70 includes one or more processors 701 and a memory 702 .

[0109] The processor 701 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 70 to perform desired functions.

[0110] The memory 702 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include, for example, read-only memory (ROM), a hard disk, a flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 701 may execute the program instructions to implement the vehicle control method of each embodiment of the present disclosure described above and / or other desired functions. Various contents such as the predicted driving trajectory of the target vehicle, vehicle-to-vehicle (V2X) information of static obstacles, detection results, and the driving strategy of the target vehicle may also be stored in the computer-readable storage medium.

[0111] In one example, the electronic device 70 may further include an input device 703 and an output device 704 , and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0112] The input device 703 may include, for example, a keyboard, a mouse, and the like.

[0113] The output device 704 can output various information to the outside, including the predicted driving trajectory of the target vehicle, V2X information about static obstacles, detection results, the driving strategy of the target vehicle, etc. The output device 704 can include, for example, a display, a speaker, a printer, a communication network and its connected remote output devices, etc.

[0114] Of course, for simplicity, FIG7 only shows some of the components related to the present disclosure in the electronic device 70, omitting components such as buses, input / output interfaces, etc. In addition, the electronic device 70 may further include any other appropriate components depending on the specific application.

[0115] In addition to the above-mentioned methods and devices, an embodiment of the present disclosure may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the vehicle control method according to various embodiments of the present disclosure described above in this specification.

[0116] The computer program product may be written in any combination of one or more programming languages ​​to implement the operations of the disclosed embodiments, including object-oriented programming languages ​​such as Java, C++, and conventional procedural programming languages ​​such as C or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0117] In addition, an embodiment of the present disclosure may also be a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, enable the processor to execute the steps of the vehicle control method according to various embodiments of the present disclosure described above in this specification.

[0118] The computer-readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can, for example, include but is not limited to a system, device or component of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0119] The basic principles of the present disclosure have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, and effects mentioned in this disclosure are merely illustrative and not restrictive, and should not be construed as necessarily possessed by each embodiment of the present disclosure. Furthermore, the specific details disclosed above are provided for illustrative purposes and to facilitate understanding, rather than as limitations. These details do not limit the present disclosure to necessarily being implemented using these specific details.

[0120] The block diagrams of the devices, devices, equipment, and systems involved in this disclosure are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, devices, equipment, and systems can be connected, arranged, or configured in any manner. Words such as "include," "comprise," "have," and the like are open-ended words, meaning "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or" and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith.

[0121] It should also be noted that in the apparatus, device, and method of the present disclosure, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present disclosure.

[0122] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0123] The above description has been provided for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present disclosure to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A vehicle control method, comprising: If it is determined that a static obstacle is in a driving conflict with the target vehicle, detecting whether V2X information related to the static obstacle is received, and obtaining a detection result, wherein the driving conflict indicates that the static obstacle is located on the predicted driving trajectory of the target vehicle or is located within an area that satisfies a specified distance condition from the predicted driving trajectory of the target vehicle, and the V2X information carries trajectory information; determining, based on the detection result, whether the static obstacle is a stationary vehicle; In a case where the static obstacle is determined to be a stationary vehicle, determining a conflict area corresponding to the target vehicle and the stationary vehicle, and determining a relationship between a predicted traveling direction of the target vehicle and a predicted traveling direction of the stationary vehicle in the conflict area; A driving strategy of the target vehicle is determined according to a relationship between the predicted traveling direction of the target vehicle and the predicted traveling direction of the stationary vehicle in the conflict area, and the target vehicle is controlled to travel according to the driving strategy.

2. The vehicle control method according to claim 1, wherein: Determining whether the static obstacle is a stationary vehicle according to the detection result includes: If the detection result indicates that trajectory information related to the static obstacle is received, it is determined that the static obstacle is a stationary vehicle.

3. The vehicle control method according to claim 1, wherein: When it is determined that there is a static obstacle that conflicts with the target vehicle, detecting whether V2X information related to the static obstacle is received to obtain a detection result includes: When it is determined that there is a static obstacle that conflicts with the target vehicle, start receiving a broadcast message, and detect whether the received broadcast message includes V2X information related to the static obstacle to obtain the detection result; or When it is determined that there is a static obstacle that conflicts with the target vehicle, historical broadcast messages received within a specified time range are parsed to determine whether the historical broadcast messages include V2X information related to the static obstacle, thereby obtaining the detection result.

4. The vehicle control method according to any one of claims 1 to 3, wherein: The determining of the driving strategy of the target vehicle according to the relationship between the predicted traveling direction of the target vehicle and the predicted traveling direction of the stationary vehicle in the conflict area includes: If the predicted moving direction of the target vehicle in the conflict area is in an opposite direction to the predicted moving direction of the stationary vehicle, determining a parking position of the target vehicle relative to the conflict area, and controlling the target vehicle to drive to the parking position and stop and wait until the stationary vehicle leaves the conflict area before continuing to drive; Preferably, the conflict area includes: an area where the predicted driving trajectories of the target vehicle and the stationary vehicle intersect; preferably, if there are multiple areas where the predicted driving trajectories of the target vehicle and the stationary vehicle intersect, the intersection area closest to the stationary vehicle is determined as the conflict area; or an area where the predicted driving trajectories of the target vehicle and the stationary vehicle do not intersect, but the distance between the two trajectories meets the specified distance condition; Preferably, the target vehicle and the stationary vehicle are both autonomous driving vehicles in the target scene.

5. The vehicle control method according to any one of claims 1 to 4, wherein: Determining a relationship between a predicted direction of travel of the target vehicle and a predicted direction of travel of the stationary vehicle in the conflict area includes: determining a trajectory intersection angle of each of the predicted travel trajectories of the target vehicle and the stationary vehicle along their respective travel directions; If the trajectory intersection angle is greater than the target angle, determining that the relationship between the predicted traveling direction of the target vehicle and the predicted traveling direction of the stationary vehicle in the conflict area is a facing relationship; If the trajectory intersection angle is less than the target angle, determining that the relationship between the predicted traveling direction of the target vehicle and the predicted traveling direction of the stationary vehicle in the conflict area is a unidirectional relationship; Preferably, the target angle is less than 90 degrees, and further preferably, the target angle is greater than 85 degrees and less than 90 degrees.

6. The vehicle control method according to any one of claims 1 to 5, wherein: Before detecting whether V2X information related to the static obstacle is received when it is determined that there is a static obstacle that conflicts with the target vehicle, the method further includes: During the process of the target vehicle traveling based on the predicted driving trajectory of the target vehicle, it is determined whether there is a static obstacle that conflicts with the target vehicle based on the environmental perception information within the target range acquired by the target vehicle.

7. The vehicle control method according to any one of claims 1 to 6, wherein: Also includes: If the predicted moving direction of the target vehicle and the predicted moving direction of the stationary vehicle in the conflict area are in the same direction, the target vehicle is controlled to continue moving.

8. A vehicle control device comprising: a detection module configured to, upon determining that a static obstacle is in a driving conflict with a target vehicle, detect whether vehicle-to-everything (V2X) information related to the static obstacle is received, and obtain a detection result, wherein the driving conflict indicates that the static obstacle is located on a predicted driving trajectory of the target vehicle or within an area that satisfies a specified distance condition from the predicted driving trajectory of the target vehicle, and the vehicle-to-everything (V2X) information carries trajectory information; a first determining module configured to determine whether the static obstacle is a stationary vehicle based on the detection result; a second determining module configured to, when determining that the static obstacle is a stationary vehicle, determine a conflict area corresponding to the target vehicle and the stationary vehicle, and determine a relationship between a predicted traveling direction of the target vehicle and a predicted traveling direction of the stationary vehicle in the conflict area; The third determination module is configured to determine a driving strategy of the target vehicle according to a relationship between the predicted moving direction of the target vehicle and the predicted moving direction of the stationary vehicle in the conflict area, and control the target vehicle to drive according to the driving strategy. 9 . A computer-readable storage medium storing a computer program for executing the vehicle control method according to claim 1 .

10. An electronic device comprising: processor; a memory for storing instructions executable by the processor; The processor is used to execute the vehicle control method according to any one of claims 1 to 7.

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