Vehicle control method and device, storage medium and electronic equipment

Abstract

The present application provides a vehicle control method and device, storage medium and electronic equipment, which relate to the fields of smart mines, autonomous driving and unmanned vehicles. The method includes: in the case of determining that there is a target obstacle that has a driving conflict with the target vehicle, determining the target attribute information, wherein the target attribute information includes the scene type of the area where the target obstacle is located and / or the obstacle attribute information of the target obstacle, and the driving conflict indicates that the predicted motion trajectory of the target obstacle intersects with the predicted driving trajectory of the target vehicle; based on the target attribute information, obtaining a vehicle speed control strategy corresponding to the target attribute information, wherein different target attribute information corresponds to different vehicle speed control strategies; and controlling the speed of the target vehicle according to the vehicle speed control strategy. The present application realizes the dynamic adjustment of the vehicle speed, which significantly improves the driving safety and operating efficiency of autonomous driving vehicles in complex mining environments.

Description

Technical Field

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

[0002] For autonomous driving in mining scenarios, safety is paramount to ensuring normalized unmanned operations. Currently, safe interaction of autonomous vehicles in mining scenarios relies primarily on predictive trajectory yielding strategies, V2X (Vehicle to Everything)-based communication strategies, and right-of-way binding strategies. However, these approaches each have limitations, such as reliance on prediction accuracy, unstable communication, and a high dependence on maps and traffic regulations.

[0003] Therefore, for mining scenarios, a driving strategy that can adapt to various situations is needed to ensure the driving safety of autonomous vehicles. Summary of the Invention

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

[0005] In a first aspect, an embodiment of the present application provides a vehicle control method, comprising: determining target attribute information when it is determined that there is a target obstacle that is in a driving conflict with a target vehicle, wherein the target attribute information includes a scene type of an area where the target obstacle is located and / or obstacle attribute information of the target obstacle, and the driving conflict indicates that the predicted motion trajectory of the target obstacle intersects with the predicted driving trajectory of the target vehicle; based on the target attribute information, obtaining a vehicle speed control strategy corresponding to the target attribute information, wherein different target attribute information corresponds to different vehicle speed control strategies; and controlling the speed of the target vehicle according to the vehicle speed control strategy.

[0006] In combination with the first aspect, in certain implementations of the first aspect, the obstacle attribute information includes at least one of the following: whether the target obstacle can communicate with the target vehicle, and driving information of the target obstacle, where the driving information includes location and / or driving direction.

[0007] In combination with the first aspect, in certain implementations of the first aspect, the scene type of the area includes: a road type, and / or a non-road type.

[0008] In combination with the first aspect, in certain implementations of the first aspect, the non-road type includes an open area and / or an intersection.

[0009] In combination with the first aspect, in certain implementations of the first aspect, based on the target attribute information, a vehicle speed control strategy corresponding to the target attribute information is obtained, including: when the scene type is a non-road type, based on the driving information of the target obstacle included in the obstacle attribute information, obtaining the vehicle speed control strategy, wherein the vehicle speed control strategy includes: determining whether to limit the speed of the target vehicle.

[0010] In combination with the first aspect, in certain implementations of the first aspect, determining whether to limit the speed of the target vehicle includes: judging whether the target obstacle meets the cross-cutting condition based on the position and / or driving direction included in the driving information of the target obstacle; if the target obstacle does not meet the cross-cutting condition, controlling the target vehicle to continue traveling based on the current vehicle speed; and / or, if the target obstacle meets the cross-cutting condition, determining a first target speed limit value using a first speed limit model.

[0011] In combination with the first aspect, in certain implementations of the first aspect, the first speed limit model includes a distance speed limit model and / or a time distance speed limit model, the distance speed limit model includes a mapping relationship between the lateral distance and the speed limit value, and the time distance speed limit model includes a mapping relationship between the intrusion time distance and the speed limit value, wherein the intrusion time distance is determined based on the ratio of the lateral distance and the lateral speed of the target obstacle, the intrusion time distance represents the time when the target obstacle overlaps with the predicted driving trajectory of the target vehicle in the lateral direction, the lateral distance represents the projection of the predicted motion trajectory of the target obstacle in a direction perpendicular to the predicted driving trajectory of the target vehicle, and the lateral speed represents the projection of the current velocity vector of the target obstacle in a direction perpendicular to the predicted driving trajectory of the target vehicle.

[0012] In combination with the first aspect, in certain implementations of the first aspect, the first target speed limit value is determined using the first speed limit model, including: when the lateral distance is less than a preset distance threshold and the intrusion time interval is less than a preset time interval threshold, the first speed limit value is determined using the lateral distance and the distance speed limit model, and the second speed limit value is determined using the intrusion time interval and the time interval speed limit model; the first target speed limit value is calculated based on the first speed limit value and the first weight value corresponding to the first speed limit value, as well as the second speed limit value and the second weight value corresponding to the second speed limit value.

[0013] In combination with the first aspect, in certain implementations of the first aspect, the method further includes: determining a first weight value corresponding to the first speed limit value and a second weight value corresponding to the second speed limit value based on whether the target obstacle included in the obstacle attribute information can communicate with the target vehicle.

[0014] In combination with the first aspect, in certain implementations of the first aspect, a first target speed limit value is determined using a first speed limit model, including: when the lateral distance is greater than or equal to a preset distance threshold and the intrusion time interval is less than or equal to the preset time interval threshold, the first target speed limit value is determined based on the intrusion time interval and the time interval speed limit model; or, when the lateral distance is less than the preset distance threshold and the intrusion time interval is greater than or equal to the preset time interval threshold, the first target speed limit value is determined based on the lateral distance and the distance speed limit model.

[0015] In combination with the first aspect, in certain implementations of the first aspect, based on the target attribute information, a vehicle speed control strategy corresponding to the target attribute information is obtained, including: when the scene type is a road type, determining the lateral distance between the target obstacle and the target vehicle; using the lateral distance and the second speed limit model to determine the second target speed limit value, wherein the second speed limit model includes a distance speed limit model, and the distance speed limit model includes a mapping relationship between the lateral distance and the speed limit value.

[0016] In a second aspect, an embodiment of the present application provides a vehicle control device, comprising: a determination module for determining target attribute information when it is determined that there is a target obstacle that is in a driving conflict with a target vehicle, wherein the target attribute information includes the scene type of the area where the target obstacle is located and / or the obstacle attribute information of the target obstacle, and the driving conflict indicates that the predicted motion trajectory of the target obstacle intersects with the predicted driving trajectory of the target vehicle; an acquisition module for acquiring a vehicle speed control strategy corresponding to the target attribute information based on the target attribute information, wherein different target attribute information corresponds to different vehicle speed control strategies; and a control module for controlling the speed of the target vehicle according to the vehicle speed control strategy.

[0017] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program for executing the vehicle control method described in the first aspect.

[0018] In a fourth aspect, an embodiment of the present application provides an electronic device comprising: a processor; a memory for storing instructions executable by the processor; and the processor for executing the vehicle control method described in the first aspect.

[0019] The solution in this application is mainly a driving strategy proposed for autonomous driving scenarios in mines, which can significantly improve the driving safety and operating efficiency of autonomous driving vehicles in complex mining environments. Specifically, by accurately identifying target obstacles and their scene types, potential driving conflicts and risks can be predicted more accurately to ensure driving safety. By customizing corresponding vehicle speed control strategies for different target attribute information, the target vehicle can respond to various traffic conditions more flexibly and realize dynamic adjustment of vehicle speed. At the same time, because the target vehicle can select the optimal driving speed based on real-time traffic conditions and obstacle information, this method can also improve road traffic efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The above and other purposes, features, and advantages of the present application will become more apparent through a more detailed description of the embodiments of the present application in conjunction with the accompanying drawings. The accompanying drawings are intended to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the drawings, the same reference numerals generally represent the same components or steps.

[0021] Figure 1 Shown is a flow chart of a vehicle control method provided in one embodiment of the present application.

[0022] Figure 2 Shown is a flow chart of obtaining a vehicle speed control strategy provided in one embodiment of the present application.

[0023] Figure 3 The figure is a flow chart of determining whether to limit the speed of a target vehicle according to an embodiment of the present application.

[0024] Figure 4 Shown is a schematic diagram of a target vehicle and a target obstacle traveling separately according to an embodiment of the present application.

[0025] Figure 5 FIG2 is a schematic diagram of a time-distance speed limit model provided in an embodiment of the present application.

[0026] Figure 6 Shown is a schematic diagram of a distance speed limit model provided in an embodiment of the present application.

[0027] Figure 7 FIG2 is a flow chart of determining a first target speed limit value by using a first speed limit model according to an embodiment of the present application.

[0028] Figure 8 FIG2 is a schematic diagram showing a method of calculating a speed limit value by using a distance speed limit model and a time interval speed limit model according to an embodiment of the present application.

[0029] Figure 9FIG2 is a flow chart of determining a first target speed limit value by using a first speed limit model according to another embodiment of the present application.

[0030] Figure 10 Shown is a flow chart of obtaining a vehicle speed control strategy provided by another embodiment of the present application.

[0031] Figure 11 Shown is a schematic structural diagram of a vehicle control device provided by an exemplary embodiment of the present application.

[0032] Figure 12 Shown is a structural schematic diagram of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0034] In autonomous mining scenarios, ensuring safety is key to achieving normalized unmanned operations. Mining environments are extremely complex, and autonomous vehicles must navigate numerous dynamic obstacles, including other autonomous vehicles, other types of auxiliary equipment, and even manned vehicles.

[0035] This application proposes a vehicle control method. Specifically, Figure 1 FIG2 is a flow chart of a vehicle control method according to an embodiment of the present application.

[0036] Step S110 : When it is determined that there is a target obstacle that conflicts with the target vehicle, target attribute information is determined.

[0037] The target attribute information includes the scene type of the area where the target obstacle is located and / or the obstacle attribute information of the target obstacle. Specifically, the scene type refers to the scene type to which the intersection area belongs when the predicted running trajectory of the target obstacle and the predicted driving trajectory of the target vehicle intersect. Exemplarily, the scene type includes road type and / or non-road type, and the non-road type includes open areas and / or intersections. The obstacle attribute information includes the size, shape, type (such as vehicle, pedestrian, cyclist, etc.), driving speed, driving position and driving direction of the obstacle. In some embodiments, the obstacle attribute information also includes whether the target obstacle can communicate with the target vehicle.

[0038] Furthermore, a driving conflict indicates that the predicted motion trajectory of the target obstacle intersects the predicted driving trajectory of the target vehicle. For example, at an intersection, if the target obstacle is expected to enter the lane that the target vehicle is about to travel, the two constitute a potential driving conflict.

[0039] In some embodiments, a method for determining whether an obstacle is a target obstacle includes: obtaining basic information corresponding to each of a plurality of pre-selected obstacles within a target area; selecting an obstacle to be confirmed from the plurality of pre-selected obstacles based on the basic information corresponding to each of the plurality of pre-selected obstacles; and determining that the obstacle to be confirmed is a target obstacle if it is determined that the obstacle to be confirmed is of the target obstacle type.

[0040] Specifically, obstacles to be confirmed include dynamic obstacles, obstacles with unpredictable motion states, and partially identifiable obstacles. Target obstacle types include vehicles, people, and other moving objects.

[0041] Exemplarily, the basic information corresponding to the pre-selected obstacles includes the physical properties, location information, motion state and mode of the obstacles, as well as other relevant information that affects the safety of vehicle driving. Physical properties include the size, shape and type of the obstacle, and location information includes the exact location of the obstacle in the environment of the target vehicle, such as longitude and latitude coordinates or specific position relative to the target vehicle. Dynamic obstacles refer to those obstacles that are in motion in the environment surrounding the target vehicle, such as other moving vehicles, pedestrians, cyclists, etc. Obstacles with unpredictable motion states refer to those obstacles whose motion trajectories or behavior patterns are difficult to accurately predict, such as pedestrians or movable devices with irregular behavior. Partially identifiable obstacles refer to those obstacles that can only obtain partial information and are difficult to accurately identify. For example, an obstacle can only be partially detected due to insufficient light or obstruction.

[0042] Target obstacle types are of particular interest in this application because they may interact with or affect the target vehicle's path. In some embodiments, if a pending obstacle is identified as a target obstacle type such as a vehicle, pedestrian, or other active object, it is classified as a target obstacle. Exemplarily, other active objects include work equipment and other non-standard traffic participants.

[0043] This solution not only improves the target vehicle's awareness of its surroundings but also ensures timely and accurate responses to various obstacles. Furthermore, accurate identification of target obstacles enables more efficient route planning and decision-making, thereby improving driving safety.

[0044] Step S120 : Based on the target attribute information, a vehicle speed control strategy corresponding to the target attribute information is acquired.

[0045] Different target attribute information corresponds to different speed control strategies. For example, if the target obstacle is a moving vehicle and its motion state is predictable, a relatively stable speed control strategy will be selected to maintain a safe distance and adjust accordingly according to the speed changes of the vehicle in front. On the contrary, if the target obstacle is a pedestrian or other moving object with unpredictable motion state, a more conservative strategy will be adopted, such as slowing down or stopping when necessary to avoid potential collision risks. In addition, if the target obstacle is a partially identifiable obstacle, it is necessary to rely on other sensors or algorithms to more accurately evaluate the attributes of the obstacle and thus determine an appropriate speed control strategy. For example, in bad weather or low visibility, the speed needs to be reduced to ensure sufficient reaction time to deal with any emergencies.

[0046] Step S130: Control the speed of the target vehicle according to the speed control strategy.

[0047] In some embodiments, the throttle and brake pedals are automatically adjusted based on the determined speed control strategy to achieve precise speed control of the target vehicle. Furthermore, this process requires real-time response to changes in the external environment and adjustments to internal strategies to ensure the target vehicle maintains a safe and appropriate speed range throughout the entire driving process.

[0048] The solution in this embodiment is mainly a driving strategy proposed for autonomous driving scenarios in mines, which can significantly improve the driving safety and operating efficiency of autonomous driving vehicles in complex mining environments. Specifically, by accurately identifying target obstacles and their scene types, potential driving conflicts and risks can be predicted more accurately to ensure driving safety. By customizing corresponding vehicle speed control strategies for different target attribute information, the target vehicle can respond to various traffic conditions more flexibly and realize dynamic adjustment of vehicle speed. At the same time, because the target vehicle can select the optimal driving speed based on real-time traffic conditions and obstacle information, this method can also improve road traffic efficiency.

[0049] Figure 2 The figure shows a flow chart of obtaining a vehicle speed control strategy according to an embodiment of the present application. Figure 1 Based on the embodiment shown, Figure 2 The embodiment shown is described below in detail. Figure 2 The embodiment shown and Figure 1 The differences and similarities between the illustrated embodiments are not described in detail.

[0050] like Figure 2 As shown, in this embodiment, based on the target attribute information, obtaining the vehicle speed control strategy corresponding to the target attribute information includes step S210.

[0051] Step S210 : When the scene type is a non-road type, a vehicle speed control strategy is obtained based on the driving information of the target obstacle included in the obstacle attribute information.

[0052] The speed control strategy includes: determining whether to limit the speed of the target vehicle. It should be noted that in the mining area, the speed requirements for the target vehicle are different for each scenario type. Non-road environments usually do not contain lane lines, and vehicle driving is more complicated. For example, there are obstacles such as irregularly moving mechanical equipment, vehicles, and pedestrians. The uncertainty of these obstacles is high, posing a greater threat to the driving safety of the target vehicle. Therefore, the speed control strategy in this case needs to dynamically determine whether to limit the speed of the target vehicle based on the driving information of the target obstacle. In addition, in some embodiments, in the non-road scenario of the mining area, the speed control strategy also needs to consider the vibration amplitude of the target vehicle, which requires the speed control strategy to have high adaptability and flexibility. For example, at turning points and turns in the mining area, there will be special restrictions on driving speed to adapt to the special traffic environment and safety requirements of the mining area.

[0053] These measures provide a highly adaptable, safe, and reliable speed adjustment solution for autonomous vehicles operating in off-road scenarios within mining areas. This not only improves operational efficiency but also significantly enhances traffic safety. It meets the varying speed requirements of mining areas and ensures effective obstacle avoidance and safe operation in complex and changing mining environments.

[0054] Figure 3 The figure shows a flow chart of determining whether to limit the speed of a target vehicle according to an embodiment of the present application. Figure 2 Based on the embodiment shown, Figure 3 The embodiment shown is described below in detail. Figure 3 The embodiment shown and Figure 2 The differences and similarities between the illustrated embodiments are not described in detail.

[0055] like Figure 3 As shown, in this embodiment, determining whether to limit the speed of the target vehicle includes the following steps.

[0056] Step S310: Based on the location and / or driving direction included in the driving information of the target obstacle, it is determined whether the target obstacle meets the cross-cutting condition.

[0057] For example, if the target obstacle is located within a certain range in front of the target vehicle and the target obstacle's travel direction spans from a first side of the target vehicle's travel direction to a second side of the target vehicle's travel direction, then it is determined that the target obstacle meets the transverse condition.

[0058] Figure 4FIG. 1 is a schematic diagram showing the target vehicle and target obstacle driving independently according to an embodiment of the present application. Figure 4 As shown, the target vehicle is traveling from A to B. At this time, with the target vehicle's traveling direction as a reference, the target obstacle is located on the left side of the target vehicle, and the target obstacle is in front of the target vehicle. The traveling direction of the target obstacle spans from the left side of the target vehicle to the right side of the target vehicle. Therefore, it is determined that the target obstacle meets the transverse condition.

[0059] Illustratively, when the target obstacle does not meet the cross-cutting condition, step S320 is executed to control the target vehicle to continue traveling based on the current vehicle speed.

[0060] It is understood that if the target obstacle does not meet the transverse condition, the target obstacle's movement is deemed to pose no threat to the target vehicle's travel. In this case, there is no need to limit the target vehicle's speed; instead, the vehicle is controlled to continue traveling at its current speed. This not only improves driving efficiency but also reduces traffic congestion that could be caused by unnecessary deceleration or stopping. Furthermore, this demonstrates the flexibility of the solution in this application in environmental perception and decision-making, facilitating more rational driving behavior decisions.

[0061] It should be noted that during step S320, the autonomous driving system continues to monitor for new changes in the target obstacle. If the target obstacle's behavior or state changes, the system will reassess the situation and adjust the target vehicle's speed control strategy as necessary. This dynamic decision-making process ensures safe adaptation to changing traffic conditions.

[0062] Exemplarily, when the target obstacle meets the transverse condition, step S330 is executed to determine the first target speed limit value using the first speed limit model.

[0063] The first speed limit model is a mathematical model used to determine the speed limit that a target vehicle should follow under specific traffic conditions. When a target obstacle meets the cross-cutting condition, indicating a potential conflict or collision risk, the first speed limit model can be used to calculate a safe driving speed—the first target speed limit—to mitigate risk and ensure driving safety. For example, in this embodiment, parameters such as the target obstacle's location, speed, and direction of travel are first acquired. Based on these parameters, the first speed limit model uses an appropriate mathematical formula to determine the recommended speed for the target vehicle under the current driving conditions.

[0064] The first speed limit model in this embodiment provides a systematic and quantitative approach to assessing and controlling vehicle speed, ensuring that target vehicles can dynamically adapt to changes in the traffic environment and providing a safe speed range for them in real time. Furthermore, this method for calculating target speed limits can reduce reliance on emergency avoidance systems, lowering collision risk and improving traffic flow efficiency.

[0065] In some embodiments, the first speed limit model includes a distance speed limit model and / or a time-to-distance speed limit model. The distance speed limit model includes a mapping relationship between lateral distance and speed limit value, and the time-to-distance speed limit model includes a mapping relationship between intrusion time and speed limit value. Exemplarily, the above mapping relationships are obtained by fitting obstacle behavior data and vehicle speed. Figure 5 FIG2 is a schematic diagram of a time-distance speed limit model provided in an embodiment of the present application. Figure 6 Figure 2 shows a schematic diagram of a distance speed limit model provided by one embodiment of the present application. For example, the time-to-distance speed limit model and the distance speed limit model are exponential function models with different coefficients. It should be understood that the time-to-distance speed limit model and the distance speed limit model provided herein are merely examples and do not represent the actual correspondence between speed limits and time-to-distance or lateral distance.

[0066] Furthermore, the intrusion time interval is determined based on the ratio of the lateral distance and the lateral velocity of the target obstacle, wherein the intrusion time interval represents the time during which the target obstacle overlaps with the predicted driving trajectory of the target vehicle in the lateral direction. The lateral distance represents the projection of the predicted motion trajectory of the target obstacle in a direction perpendicular to the predicted driving trajectory of the target vehicle, and the lateral velocity represents the projection of the current velocity vector of the target obstacle in a direction perpendicular to the predicted driving trajectory of the target vehicle. For example, referring to Figure 4 As shown, S represents the horizontal distance, V y Indicates the lateral velocity.

[0067] In this embodiment, the lateral distance represents the vertical position of the target obstacle relative to the target vehicle's predicted trajectory, reflecting the lateral distance between the target obstacle and the target vehicle. Its size directly affects whether the target vehicle has sufficient space to avoid a potential collision. The intrusion headway represents the time required for the target obstacle to laterally overlap with the target vehicle's predicted trajectory, reflecting the time required for the target obstacle to laterally cross the target vehicle's predicted trajectory at the current vehicle speed.

[0068] Combining the above two models, this application provides a more accurate and flexible speed limit strategy, which can achieve safer and more effective vehicle speed control in different traffic scenarios. At the same time, it improves the adaptability and safety of autonomous vehicles and optimizes the efficiency of traffic flow.

[0069] Figure 7The figure shows a flow chart of determining the first target speed limit value by using the first speed limit model provided by an embodiment of the present application. Figure 4 Based on the embodiment shown, Figure 7 The embodiment shown is described below in detail. Figure 7 The embodiment shown and Figure 4 The differences and similarities between the illustrated embodiments are not described in detail.

[0070] like Figure 7 As shown, in this embodiment, determining the first target speed limit value using the first speed limit model includes the following steps.

[0071] Step S710: When the lateral distance is less than the preset distance threshold and the intrusion time interval is less than the preset time interval threshold, the first speed limit value is determined using the lateral distance and the distance speed limit model, and the second speed limit value is determined using the intrusion time interval and the time interval speed limit model.

[0072] In some embodiments, the lateral distance is input into the distance speed limit model to obtain a first speed limit value, and the intrusion time interval is input into the time interval speed limit model to obtain a second speed limit value. For example, the preset distance threshold is 5 meters and the preset time interval threshold is 1 m / s.

[0073] Step S720: Calculate a first target speed limit value based on the first speed limit value and the first weight value corresponding to the first speed limit value, and the second speed limit value and the second weight value corresponding to the second speed limit value.

[0074] In some embodiments, the first weight value and the second weight value may be adjusted according to a specific driving scenario, information about a target obstacle, and operational requirements of a target vehicle.

[0075] In one example, if the target obstacle type is predictable, traffic flow is standardized, or the target vehicle is performing precise maneuvers in a small space, requiring greater sensitivity to the target obstacle's immediate location and lateral distance, the first weight value is set relatively high, while the second weight value is set relatively low. For example, on narrow mining roads or in an environment with dense obstacles, the target vehicle needs to rely more on the target obstacle's current location to react quickly, so the first weight value is set higher.

[0076] In another example, if the target obstacle's behavior is difficult to predict or the traffic environment is complex, the second weight value may be set relatively high, while the first weight value may be set relatively low. For example, at a mining intersection or in an area with multiple obstacles, the target vehicle needs to rely more on the target obstacle's movement for prediction and time response, so the second weight value may be higher.

[0077] Figure 8The figure shows a schematic diagram of calculating the speed limit value using the distance speed limit model and the time distance speed limit model provided by an embodiment of the present application. It shows the relationship between the speed limit value under the interaction of the two models and the lateral distance and speed corresponding to the target obstacle.

[0078] from Figure 8 It can be observed that when the lateral distance between the target obstacle and the target vehicle is close, the distance speed limit model has a greater impact on the speed limit value. This is because in the close distance situation, the existence of the target obstacle poses a direct threat to the immediate safety of the target vehicle, so it is necessary to reduce the speed immediately to avoid a potential collision.

[0079] As the lateral distance between the target obstacle and the target vehicle increases, the time-to-distance speed limit model begins to have a greater impact on the speed limit. This is because at larger lateral distances, while the target obstacle may not pose an immediate threat to the target vehicle, its motion and speed may affect the target vehicle's safety in the foreseeable future. Therefore, the time-to-distance speed limit model takes more account of the target obstacle's intrusion time to adjust the speed limit, ensuring that the vehicle has sufficient time to respond to changes in the obstacle's motion.

[0080] comprehensive Figure 8 It can be seen that in some embodiments, when the lateral distance is less than the preset distance threshold and the intrusion time interval is less than the preset time interval threshold, the first weight value increases as the lateral distance decreases, and the second weight value increases as the lateral distance increases.

[0081] In this embodiment, when the lateral distance is less than a preset distance threshold, the target obstacle is relatively close to the target vehicle. When the intrusion time interval is less than a preset time interval threshold, the target obstacle's time required to laterally overlap with the target vehicle's predicted trajectory is relatively short. In this case, the potential collision risk is high. Using the distance speed limit model to determine the first speed limit and the time distance speed limit model to determine the second speed limit ensures that the target vehicle maintains a safe distance under various circumstances while avoiding time conflicts. Furthermore, this embodiment allows for flexible adjustment of weights based on the characteristics of the target obstacle and the actual traffic environment, enabling more accurate and timely responses and providing an effective safety management strategy for the target vehicle.

[0082] Combine Figure 7 In the illustrated embodiment, in other embodiments of the present application, a first weight value corresponding to the first speed limit value and a second weight value corresponding to the second speed limit value are determined based on whether the target obstacle included in the obstacle attribute information can communicate with the target vehicle.

[0083] It's important to note that when the target obstacle is able to effectively communicate with the target vehicle, its location is highly accurate. Therefore, the target vehicle can obtain more precise information about the obstacle's attributes, including its position, speed, and motion intent. In this case, the second weight value is increased accordingly, enabling a more timely response.

[0084] In some embodiments, the time-to-distance speed limit model comprehensively considers the current speed of the target obstacle and the lateral distance from the target vehicle, and directly evaluates whether the target obstacle will intersect with the predicted driving trajectory of the target vehicle at a specific point in time, thereby providing a more intuitive and flexible speed limit strategy. The time-to-distance model is particularly suitable for situations where the behavior of obstacles is changeable and the traffic environment is complex, such as intersections or open areas, and can quickly adapt to sudden changes in the movement of the target obstacle. Therefore, when the target obstacle has the ability to communicate with the target vehicle, the time-to-distance speed limit model can use this precise information to make more accurate predictions and responses. Therefore, in this case, the first weight value is set relatively small, and the second weight value is set relatively large.

[0085] In some embodiments, if the target obstacle can effectively communicate with the target vehicle, the first weight value can be directly set to zero and the second weight value can be set to 1. That is, only the time-to-distance speed limit model can be used to calculate the first speed limit value of the target vehicle.

[0086] In this embodiment, by considering the target obstacle's communication capabilities, the information provided by the target obstacle can be more effectively utilized, weighting can be optimized, and the accuracy and reliability of speed limit decisions can be improved. This approach allows the target vehicle to pass through potentially hazardous areas at a speed closer to normal driving speed, with sufficient information, thereby improving driving safety.

[0087] Figure 9 The figure shows a flow chart of determining a first target speed limit value using a first speed limit model according to another embodiment of the present application. Figure 4 Based on the embodiment shown, Figure 9 The embodiment shown is described below in detail. Figure 9 The embodiment shown and Figure 4 The differences and similarities between the illustrated embodiments are not described in detail.

[0088] like Figure 9 As shown, in this embodiment, determining the first target speed limit value using the first speed limit model includes the following steps.

[0089] Step S910 : When the lateral distance is greater than or equal to a preset distance threshold and the intrusion time interval is less than or equal to a preset time interval threshold, a first target speed limit value is determined according to the intrusion time interval and the time interval speed limit model.

[0090] When the lateral distance is greater than or equal to the preset distance threshold, and the intrusion time distance is less than or equal to the preset time distance threshold, it means that although the target obstacle is not very close to the target vehicle in space, there is a risk of collision with the target vehicle within a limited time based on its motion state and direction. The intrusion time distance provides a time-based assessment, which enables the target vehicle to calculate the speed limit value based on the urgency with which the target obstacle may enter its predicted driving trajectory. Therefore, in step S910, the speed limit value can be calculated using only the time distance speed limit model. This method allows for early response when the target obstacle has not yet posed an immediate threat, and calculates an appropriate speed limit value, ensuring that the target vehicle has enough time to slow down before the target obstacle may enter its driving path, thereby maintaining safe driving.

[0091] Step S920 : When the lateral distance is less than the preset distance threshold and the intrusion time interval is greater than or equal to the preset time interval threshold, a first target speed limit value is determined according to the lateral distance and the distance speed limit model.

[0092] If the lateral distance is less than the preset distance threshold, the target obstacle is very close to the target vehicle, posing a potential collision risk. However, if the intrusion distance is greater than or equal to the preset distance threshold, it means that there is still some time before the target obstacle and the target vehicle's trajectory intersect, and the target vehicle is not immediately threatened with a collision. In this case, the target vehicle is required to reduce its speed to increase the safety buffer, thereby reducing the risk of collision caused by the close proximity.

[0093] The distance speed limit model focuses on the spatial relationship between the current obstacle position and the vehicle, adjusting the speed limit based on lateral distance. Therefore, in step S920, the speed limit can be calculated using only the distance speed limit model, ensuring that the target vehicle maintains a safe distance while effectively utilizing road resources and avoiding premature or abrupt deceleration that could affect traffic flow.

[0094] The solution in step S920 provides a driving strategy for the autonomous vehicle that balances safety and efficiency. This approach enables the target vehicle to decelerate promptly when the target obstacle approaches, while avoiding unnecessary traffic disruptions that could result from overreacting to time constraints.

[0095] Figure 10 The figure shows a flow chart of obtaining a vehicle speed control strategy according to another embodiment of the present application. Figure 1 Based on the embodiment shown, Figure 10 The embodiment shown is described below in detail. Figure 10 The embodiment shown and Figure 1 The differences and similarities between the illustrated embodiments are not described in detail.

[0096] like Figure 10 As shown, in this embodiment, based on the target attribute information, obtaining the vehicle speed control strategy corresponding to the target attribute information includes the following steps.

[0097] Step S1010: When the scene type is a road type, determine the lateral distance between the target obstacle and the target vehicle.

[0098] Specifically, the lateral distance between the target obstacle and the target vehicle may be determined according to the method in the aforementioned embodiment.

[0099] Step S1020: Determine a second target speed limit value using the lateral distance and the second speed limit model.

[0100] The second speed limit model is another specialized model within the speed control strategy, used to determine the target vehicle's speed limit in specific situations. Compared to the first speed limit model, the second speed limit model prioritizes the use of the spatial location of obstacles to determine speed limits. It is suitable for roads with clear lane divisions and traffic regulations. This model enables the target vehicle to more precisely control its speed to adapt to its surroundings and ensure driving safety.

[0101] In some embodiments, the second speed limit model includes a distance speed limit model, which includes a mapping relationship between lateral distance and speed limit value.

[0102] It's important to reiterate that the road scene contains well-defined lane markings, which provide the target vehicle with structured road boundaries and driving guidance, making its path more predictable. In this scenario, lateral distance becomes the primary factor in assessing potential collision risk. When the target obstacle approaches the target vehicle, the distance speed limit model calculates a lower speed limit to ensure the target vehicle has sufficient time and space to respond to any potential threat.

[0103] Furthermore, in road scenarios, especially during meeting situations, the lateral distance between vehicles decreases. Furthermore, since both vehicles are moving, the relative speed of the obstacle vehicle from the perspective of the target vehicle may increase. This means that the two vehicles reach their point of intersection more quickly, and therefore the time-to-interference (TIM) (i.e., the estimated time between the target obstacle and the target vehicle) decreases. If a time-to-interference speed-limiting model were to be relied upon, the reduced TIM could cause the model to calculate an excessively low speed limit, impacting the target vehicle's normal driving efficiency. In other words, when the target vehicle and the target obstacle are approaching each other, the TIM model may overreact, resulting in an excessively low speed limit, even if the actual risk is not high. In contrast, the distance-to-interference speed-limiting model calculates the speed limit by taking into account the lateral distance between the target obstacle and the target vehicle. This approach is more stable and unaffected by the dramatic decrease in TIM during meeting. As long as the lateral distance remains within a safe range, the target vehicle can maintain a relatively high speed, thereby improving road efficiency and driving smoothness.

[0104] Furthermore, the distance-limited speed model is more suitable for structured road environments because lane lines and traffic rules provide clear driving paths and spatial separation for vehicles, reducing uncertainty and potential conflicts. In this case, the target vehicle can more efficiently utilize the available road space while maintaining a safe distance from other obstacles.

[0105] Combined with the above Figures 1 to 10 , describes in detail the vehicle control method embodiment of the present application, and the following is combined with Figure 11 , the vehicle control device embodiment of the present application is described in detail. It should be understood that the description of the vehicle control method embodiment corresponds to the description of the vehicle control device embodiment, so the parts not described in detail can be referred to the previous method embodiment.

[0106] Figure 11 The figure shows a schematic diagram of the structure of a vehicle control device provided by an exemplary embodiment of the present application. Figure 11 As shown, the vehicle control device provided in the embodiment of the present application includes:

[0107] A determination module 1110 is configured to determine target attribute information when it is determined that a target obstacle is in a driving conflict with the target vehicle, wherein the target attribute information includes a scene type of an area where the target obstacle is located and / or obstacle attribute information of the target obstacle. A driving conflict indicates that a predicted motion trajectory of the target obstacle intersects a predicted driving trajectory of the target vehicle.

[0108] An acquisition module 1120 is configured to acquire a vehicle speed control strategy corresponding to the target attribute information based on the target attribute information, wherein different target attribute information corresponds to different vehicle speed control strategies;

[0109] The control module 1130 is used to control the speed of the target vehicle according to the speed control strategy.

[0110] In one embodiment of the present application, the obstacle attribute information includes at least one of the following: whether the target obstacle can communicate with the target vehicle, and the driving information of the target obstacle, where the driving information includes the location and / or driving direction.

[0111] In an embodiment of the present application, the scene type of the area includes: road type, and / or non-road type.

[0112] In an embodiment of the present application, the non-road type includes an open area and / or an intersection.

[0113] In one embodiment of the present application, the acquisition module 1120 is also used to, when the scene type is a non-road type, obtain a vehicle speed control strategy based on the driving information of the target obstacle included in the obstacle attribute information, wherein the vehicle speed control strategy includes: determining whether to limit the speed of the target vehicle.

[0114] In one embodiment of the present application, the acquisition module 1120 is further used to determine whether the target obstacle meets the cross-cutting condition based on the position and / or driving direction included in the driving information of the target obstacle; if the target obstacle does not meet the cross-cutting condition, control the target vehicle to continue driving based on the current vehicle speed; and / or, if the target obstacle meets the cross-cutting condition, determine the first target speed limit value using the first speed limit model.

[0115] In one embodiment of the present application, the first speed limit model includes a distance speed limit model and / or a time speed limit model, the distance speed limit model includes a mapping relationship between the lateral distance and the speed limit value, and the time speed limit model includes a mapping relationship between the intrusion time and the speed limit value, wherein the intrusion time is determined based on the ratio of the lateral distance and the lateral speed of the target obstacle, the intrusion time represents the time when the target obstacle overlaps with the predicted driving trajectory of the target vehicle in the lateral direction, the lateral distance represents the projection of the predicted motion trajectory of the target obstacle in the direction perpendicular to the predicted driving trajectory of the target vehicle, and the lateral speed represents the projection of the current velocity vector of the target obstacle in the direction perpendicular to the predicted driving trajectory of the target vehicle.

[0116] In one embodiment of the present application, the acquisition module 1120 is also used to, when the lateral distance is less than a preset distance threshold and the intrusion time interval is less than a preset time interval threshold, determine the first speed limit value using the lateral distance and distance speed limit model, and determine the second speed limit value using the intrusion time interval and time interval speed limit model; calculate the first target speed limit value based on the first speed limit value and the first weight value corresponding to the first speed limit value, as well as the second speed limit value and the second weight value corresponding to the second speed limit value.

[0117] In an embodiment of the present application, the acquisition module 1120 is further configured to determine a first weight value corresponding to the first speed limit value and a second weight value corresponding to the second speed limit value based on whether the target obstacle included in the obstacle attribute information can communicate with the target vehicle.

[0118] In one embodiment of the present application, the acquisition module 1120 is further used to determine the first target speed limit value based on the intrusion time distance and the time distance speed limit model when the lateral distance is greater than or equal to the preset distance threshold and the intrusion time distance is less than or equal to the preset time distance threshold; or, when the lateral distance is less than the preset distance threshold and the intrusion time distance is greater than or equal to the preset time distance threshold, determine the first target speed limit value based on the lateral distance and the distance speed limit model.

[0119] In one embodiment of the present application, the acquisition module 1120 is further used to, when the scene type is a road type, determine the lateral distance between the target obstacle and the target vehicle; and determine the second target speed limit value using the lateral distance and the second speed limit model, wherein the second speed limit model includes a distance speed limit model, and the distance speed limit model includes a mapping relationship between the lateral distance and the speed limit value.

[0120] Below, reference Figure 12 To describe the electronic device according to the embodiment of the present application. Figure 12 Shown is a schematic structural diagram of an electronic device provided by an exemplary embodiment of the present application.

[0121] like Figure 12 As shown, the electronic device 120 includes one or more processors 1201 and a memory 1202 .

[0122] The processor 1201 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 120 to perform desired functions.

[0123] The memory 1202 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), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 1201 may run the program instructions to implement the vehicle control method of each embodiment of the present application described above and / or other desired functions. Various contents such as target attribute information, vehicle speed control strategy, predicted driving trajectory, predicted motion trajectory, etc. may also be stored in the computer-readable storage medium.

[0124] In one example, the electronic device 120 may further include an input device 1203 and an output device 1204 , and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0125] The input device 1203 may include, for example, a keyboard, a mouse, and the like.

[0126] The output device 1204 can output various information to the outside, including target attribute information, vehicle speed control strategy, predicted driving trajectory, predicted motion trajectory, etc. The output device 1204 can include, for example, a display, a speaker, a printer, a communication network and its connected remote output devices, etc.

[0127] Of course, to simplify, Figure 12 Only some of the components related to the present application in the electronic device 120 are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, the electronic device 120 may further include any other appropriate components according to specific application scenarios.

[0128] In addition to the above-mentioned methods and devices, an embodiment of the present application 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 application described above in this specification.

[0129] The computer program product may be written in any combination of one or more programming languages to implement the program code for performing the operations of the embodiments of the present application, 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.

[0130] In addition, an embodiment of the present application 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 application described above in this specification.

[0131] 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.

[0132] The basic principles of the present application have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, and effects mentioned in this application are merely illustrative and not restrictive, and it should not be assumed that these advantages, strengths, and effects are required of each embodiment of this application. In addition, the specific details disclosed above are merely illustrative and facilitating understanding, and are not restrictive. The above details do not limit this application to necessarily being implemented using the above specific details.

[0133] The block diagrams of the devices, devices, equipment, and systems involved in this application 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.

[0134] It should also be noted that in the apparatus, device, and method of the present application, each component or each step can be decomposed and / or recombined, and such decomposition and / or recombination should be regarded as equivalent solutions of the present application.

[0135] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. 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 application. Therefore, the present application 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.

[0136] The above description has been provided for the purpose of illustration and description. Furthermore, this description is not intended to limit the embodiments of the present application 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, characterized in that: include: If it is determined that there is a target obstacle that is in a driving conflict with the target vehicle, determining target attribute information, wherein the target attribute information includes a scene type of an area where the target obstacle is located and obstacle attribute information of the target obstacle, and the driving conflict indicates that a predicted motion trajectory of the target obstacle intersects a predicted driving trajectory of the target vehicle; Based on the target attribute information, a vehicle speed control strategy corresponding to the target attribute information is obtained, wherein different target attribute information corresponds to different vehicle speed control strategies. In a case where the scene type is a non-road type, based on the driving information of the target obstacle included in the obstacle attribute information, it is determined whether to limit the speed of the target vehicle; including: when the target obstacle meets the cross-cutting condition, when the lateral distance is less than a preset distance threshold, and when the intrusion time interval is less than a preset time interval threshold, using the lateral distance and distance speed limit model to determine a first speed limit value, and using the intrusion time interval and time interval speed limit model to determine a second speed limit value; based on whether the target obstacle included in the obstacle attribute information can communicate with the target vehicle, determining a first weight value and a second weight value corresponding to the first speed limit value. a second weight value corresponding to the speed value; calculating a first target speed limit value according to the first speed limit value and the first weight value corresponding to the first speed limit value, and the second speed limit value and the second weight value corresponding to the second speed limit value, wherein the distance speed limit model includes a mapping relationship between the lateral distance and the speed limit value, and the time distance speed limit model includes a mapping relationship between the intrusion time distance and the speed limit value; when the lateral distance is greater than or equal to the preset distance threshold and the intrusion time distance is less than or equal to the preset time distance threshold, determining the first target speed limit value according to the intrusion time distance and the time distance speed limit model; or, when the lateral distance is less than the preset distance threshold and the intrusion time distance is greater than or equal to the preset time distance threshold, determining the first target speed limit value according to the lateral distance and the distance speed limit model; The speed of the target vehicle is controlled according to the speed control strategy.

2. The vehicle control method according to claim 1, characterized in that: The obstacle attribute information includes at least one of the following: whether the target obstacle can communicate with the target vehicle, and driving information of the target obstacle, where the driving information includes a position and / or a driving direction.

3. The vehicle control method according to claim 1, characterized in that: The scene type of the area also includes: road type.

4. The vehicle control method according to claim 1, wherein: The non-road types include open areas and / or intersections.

5. The vehicle control method according to claim 1, characterized in that: The determining whether to limit the speed of the target vehicle further includes: Determining whether the target obstacle meets a cross-cutting condition based on the position and / or travel direction included in the travel information of the target obstacle; When the target obstacle does not meet the cross-cutting condition, the target vehicle is controlled to continue traveling based on the current vehicle speed.

6. The vehicle control method according to claim 1, characterized in that: The intrusion time interval is determined based on the ratio of the lateral distance and the lateral speed of the target obstacle. The intrusion time interval represents the time when the target obstacle overlaps with the predicted driving trajectory of the target vehicle in the lateral direction. The lateral distance represents the projection of the predicted motion trajectory of the target obstacle in a direction perpendicular to the predicted driving trajectory of the target vehicle. The lateral speed represents the projection of the current velocity vector of the target obstacle in a direction perpendicular to the predicted driving trajectory of the target vehicle.

7. The vehicle control method according to claim 3, characterized in that: The acquiring, based on the target attribute information, a vehicle speed control strategy corresponding to the target attribute information, includes: When the scene type is the road type, determining a lateral distance between the target obstacle and the target vehicle; A second target speed limit value is determined using the lateral distance and a second speed limit model, wherein the second speed limit model includes a distance speed limit model, and the distance speed limit model includes a mapping relationship between the lateral distance and the speed limit value.

8. A vehicle control device, characterized in that: include: a determination module, configured to, upon determining that a target obstacle is in a driving conflict with the target vehicle, determine target attribute information, wherein the target attribute information includes a scene type of an area where the target obstacle is located and obstacle attribute information of the target obstacle, and the driving conflict indicates that a predicted motion trajectory of the target obstacle intersects a predicted driving trajectory of the target vehicle; an acquisition module, configured to acquire, based on the target attribute information, a vehicle speed control strategy corresponding to the target attribute information, wherein different target attribute information corresponds to different vehicle speed control strategies, and, if the scene type is a non-road type, determine whether to limit the speed of the target vehicle based on the travel information of the target obstacle included in the obstacle attribute information; A control module, configured to control the speed of the target vehicle according to the speed control strategy; The acquisition module is further configured to, when the lateral distance is less than a preset distance threshold and the intrusion time interval is less than a preset time interval threshold, determine a first speed limit value by using the lateral distance and the distance speed limit model, and determine a second speed limit value by using the intrusion time interval and the time interval speed limit model; determine a first weight value corresponding to the first speed limit value and a second weight value corresponding to the second speed limit value based on whether the target obstacle included in the obstacle attribute information can communicate with the target vehicle; and calculate a first speed limit value and a first weight value corresponding to the first speed limit value, and a second speed limit value and a second weight value corresponding to the second speed limit value according to the first speed limit value and the first weight value corresponding to the first speed limit value, and the second speed limit value and the second weight value corresponding to the second speed limit value. A first target speed limit value is calculated, wherein the distance speed limit model includes a mapping relationship between a lateral distance and a speed limit value, and the time interval speed limit model includes a mapping relationship between an intrusion time interval and a speed limit value; when the lateral distance is greater than or equal to a preset distance threshold and the intrusion time interval is less than or equal to a preset time interval threshold, the first target speed limit value is determined according to the intrusion time interval and the time interval speed limit model; or, when the lateral distance is less than the preset distance threshold and the intrusion time interval is greater than or equal to the preset time interval threshold, the first target speed limit value is determined according to the lateral distance and the distance speed limit model.

9. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and the computer program is used to execute the vehicle control method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is used to execute the vehicle control method described in any one of claims 1 to 7.

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