Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In mine automatic driving scenes, ensuring safety is a key for realizing normalized unmanned operation. Mine work environments are extremely complex, in which an autonomous vehicle is required to cross work with a number of dynamic obstacles, which may be other autonomous vehicles or other types of auxiliary work equipment, manned vehicles, and the like.
The application provides a vehicle control method. Specifically, fig. 1 is a schematic flow chart of a vehicle control method according to an embodiment of the application. Illustratively, the method includes the following steps.
In step S110, in the case where it is determined that there is a target obstacle that collides with the traveling of the target vehicle, the target attribute information is determined.
The target attribute information includes scene type of the region where the target obstacle is located and/or obstacle attribute information of the target obstacle. Specifically, the scene type refers to a scene type to which the intersection region belongs when there is an intersection between the predicted travel locus of the target obstacle and the predicted travel locus of the target vehicle. Illustratively, the scene type includes a road type and/or a non-road type, the non-road type including an open area and/or an intersection. The obstacle attribute information includes the size, shape, type (e.g., vehicle, pedestrian, rider, etc.), travel speed, travel position, travel direction, and the like of the obstacle. In some embodiments, the obstacle attribute information further includes whether the target obstacle is capable of communicating with the target vehicle, and the like.
Further, the travel conflict indicates that the predicted movement locus of the target obstacle intersects with the predicted travel locus of the target vehicle. For example, at an intersection, a target obstacle is expected to enter a lane where a target vehicle is to travel, both of which constitute a potential travel conflict.
In some embodiments, the method for determining whether the obstacle is a target obstacle comprises the steps of obtaining basic information corresponding to each of a plurality of pre-selected obstacles in a target area, selecting the 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 the obstacle to be confirmed as the target obstacle when the obstacle to be confirmed is judged to belong to the target obstacle type.
Specifically, the obstacle to be confirmed includes a dynamic obstacle, an obstacle whose movement state is unpredictable, and a partially identifiable obstacle. Target obstacle types include vehicles, people, and other moving objects.
Illustratively, the basic information corresponding to the preselected obstacle includes physical properties, location information, movement status and pattern of the obstacle, and other relevant information affecting the driving safety of the vehicle. The physical attributes include the size, shape, and type of the obstacle, and the location information includes the exact location of the obstacle in the environment of the target vehicle, such as latitude and longitude coordinates or a specific location relative to the target vehicle. Dynamic obstacles are those obstacles that are in motion in the surroundings of the target vehicle, such as other traveling vehicles, pedestrians, riders, etc. Unpredictable obstacles in the motion state refer to those obstacles whose motion trajectories or behavior patterns are difficult to be accurately predicted, such as pedestrians or mobile devices whose behavior is irregular. Partially identifiable obstructions are those that can only obtain partial information and are difficult to identify accurately, e.g., an obstruction that is only partially detected due to insufficient light or occlusion.
The target obstacle types are of particular concern to the present application because they may interact with the target vehicle or affect the path of travel of the target vehicle. In some embodiments, if an obstacle to be identified is identified as a target obstacle type for a vehicle, pedestrian, or other moving object, it is classified as a target obstacle. Illustratively, other activity objects include work devices and other non-standard traffic participants.
The scheme not only can improve the perception capability of the target vehicle to the surrounding environment, but also can ensure to respond to various obstacles timely and accurately. In addition, through accurate recognition of the target obstacle, the driving path can be planned more effectively and decision can be made, so that driving safety is improved.
Step S120, based on the target attribute information, obtains a vehicle speed control policy corresponding to the target attribute information.
Different target attribute information corresponds to different vehicle speed control strategies. For example, if the target obstacle is a traveling vehicle and its motion state is predictable, a smoother vehicle speed control strategy may be selected to maintain the safe distance and adjust accordingly to the speed change of the preceding vehicle. Conversely, if the target obstacle is a pedestrian or other moving object whose motion state is unpredictable, a more conservative strategy, such as slowing down or stopping if necessary, may be taken to avoid potential collision risk. In addition, if the target obstacle is a partially identifiable obstacle, other sensors or algorithms may need to be relied upon to more accurately evaluate the properties of the obstacle to determine an appropriate vehicle speed control strategy. For example, in severe weather or low visibility situations, it is desirable to reduce the vehicle speed to ensure that there is enough reaction time to handle any emergency.
Step S130, performing vehicle speed control on the target vehicle according to the vehicle speed control strategy.
In some embodiments, throttle, brake, etc. are automatically adjusted according to a determined vehicle speed control strategy to achieve accurate vehicle speed control of the target vehicle. In addition, this process requires real-time response to changes in the external environment and adjustments in internal strategies to ensure that the target vehicle remains within a safe and appropriate speed range throughout the ride.
The scheme in the embodiment mainly aims at the running strategy provided by the mine automatic driving scene, and can remarkably improve the running safety and the working efficiency of the automatic driving vehicle in the complex mine environment. Specifically, through accurately identifying the target obstacle and the scene type thereof, potential driving conflict and risk can be more accurately predicted, and driving safety is ensured. By customizing corresponding vehicle speed control strategies for different target attribute information, the target vehicle can more flexibly cope with various traffic conditions, and the dynamic adjustment of the vehicle speed is realized. Meanwhile, because the target vehicle can select the optimal running speed according to the real-time traffic condition and the obstacle information, the method can also improve the traffic efficiency of the road.
Fig. 2 is a flowchart illustrating a process of acquiring a vehicle speed control strategy according to an embodiment of the present application. The embodiment shown in fig. 2 is extended from the embodiment shown in fig. 1, and differences between the embodiment shown in fig. 2 and the embodiment shown in fig. 1 are described with emphasis, and the details of the differences are not repeated.
As shown in fig. 2, in the present embodiment, a vehicle speed control strategy corresponding to target attribute information is acquired based on the target attribute information, including step S210.
Step S210, in the case where the scene type is a non-road type, acquiring a vehicle speed control strategy based on the travel information of the target obstacle included in the obstacle attribute information.
The vehicle speed control strategy includes determining whether to speed limit the target vehicle. It should be noted that, in the mining area, the speed requirements of each scene type on the target vehicle are different. The non-road environment usually does not contain lane lines, and the vehicle is complex to run, for example, obstacles such as irregularly moving mechanical equipment, vehicles, pedestrians and the like exist, and the uncertainty of the obstacles is high, so that the running safety of the target vehicle is threatened greatly. Therefore, the vehicle speed control strategy in this case needs to dynamically determine whether to speed limit the target vehicle based on the traveling information of the target obstacle. Furthermore, in some embodiments, the vehicle speed control strategy also needs to take into account the jitter amplitude of the target vehicle in the off-road scenario of the mine, which requires that the vehicle speed control strategy must have high adaptability and flexibility. For example, at turning points and corners of the mining area, the speed per hour of driving is particularly limited to adapt to the special traffic environment and safety requirements of the mining area.
Through the measures, a safe and reliable vehicle speed adjusting scheme with strong adaptability is provided for the automatic driving vehicle in the non-road scene in the mining area. The method is not only beneficial to improving the operation efficiency of the mining area, but also greatly enhances the traffic safety of the mining area, accords with different scenes of the mining area on the vehicle speed requirement, and ensures that effective obstacle avoidance and safe running are realized in complex and changeable mining area environments.
Fig. 3 is a schematic flow chart of determining whether to limit the speed of the target vehicle according to an embodiment of the application. The embodiment shown in fig. 3 is extended from the embodiment shown in fig. 2, and differences between the embodiment shown in fig. 3 and the embodiment shown in fig. 2 are described with emphasis, and the details of the differences are not repeated.
As shown in fig. 3, in the present embodiment, it is determined whether or not to speed limit the target vehicle, including the following steps.
Step S310, determining whether the target obstacle satisfies the crosscut condition based on the position and/or the traveling direction included in the traveling information of the target obstacle.
For example, if the position of the target obstacle is within a certain range in front of the target vehicle and the traveling direction of the target obstacle is from a first side of the traveling direction of the target vehicle to a second side of the traveling direction of the target vehicle, it is determined that the target obstacle satisfies the cross cut condition.
Fig. 4 is a schematic diagram of driving of a target vehicle and a target obstacle according to an embodiment of the present application. As shown in fig. 4, the traveling direction of the target vehicle is from a to B, at this time, with the traveling direction of the target vehicle 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, and the traveling direction of the target obstacle crosses from the left side of the target vehicle to the right side of the target vehicle, and therefore, it is determined that the target obstacle satisfies the cross cut condition.
Illustratively, in the case where the target obstacle does not satisfy the crosscut condition, step S320 is performed to control the target vehicle to continue traveling based on the current vehicle speed.
It will be appreciated that if the target obstacle does not meet the cross cut condition, then the movement of the target obstacle is considered not to be a threat to the travel of the target vehicle. In this case, the target vehicle is not limited in speed, but the vehicle is controlled to continue traveling at the current vehicle speed. This not only improves the driving efficiency but also reduces traffic congestion that may be caused by unnecessary deceleration or parking. In addition, the scheme of the application has flexibility on environment perception and decision making capability, and is convenient for making more reasonable driving behavior decisions.
It should be noted that, during the execution of step S320, the autopilot system may still continuously monitor the new change of the target obstacle. If the behavior or state of the target obstacle changes, the situation will be re-evaluated and the speed control strategy of the target vehicle will be adjusted if necessary. The dynamic decision process can ensure that the system is suitable for changeable traffic environments on the premise of keeping safety.
Illustratively, in the event that the target obstacle meets the transection condition, step S330 is performed to determine a first target speed limit value using a first speed limit model.
The first speed limit model is a mathematical model for determining the speed limit value that the target vehicle should follow under certain traffic conditions. In the case of a target obstacle meeting the transverse conditions, i.e. having a potential driving collision or collision risk, a safe driving speed, i.e. a first target speed limit value, can be calculated using the first speed limit model, in order to reduce the risk and ensure driving safety. Illustratively, in the present embodiment, parameters such as the position, speed, traveling direction, etc. of the target obstacle are first acquired, and based on these parameters, the first speed limit model adopts an appropriate mathematical formula to determine the recommended traveling speed of the target vehicle under the current traveling condition.
The first speed limiting model in this embodiment provides a systematic and quantitative way to evaluate and control the vehicle speed, ensure that the target vehicle can dynamically adapt to the change of the traffic environment, and provide a safe driving speed range for the target vehicle in real time. In addition, the method for calculating the target speed limit value can reduce dependence on an emergency avoidance system, reduce collision risk and improve traffic flow efficiency.
In some embodiments, the first speed limit model includes a distance speed limit model and/or a time distance speed limit model. The distance speed limiting model comprises a mapping relation between a transverse distance and a speed limiting value, and the time distance speed limiting model comprises a mapping relation between an intrusion time distance and the speed limiting value. The above-described map is obtained by fitting obstacle behavior data and a vehicle running speed, for example. Fig. 5 is a schematic diagram of a time-distance speed-limiting model according to an embodiment of the application. Fig. 6 is a schematic diagram of a distance speed limiting model according to an embodiment of the application. Illustratively, the time-distance speed limit model and the distance speed limit model are exponential function models with different coefficients. It will be appreciated that the time-distance speed-limiting model and the distance speed-limiting model provided by the application are only examples, and do not represent the corresponding relationship between the actual speed-limiting value and the time distance or the transverse distance.
Further, the intrusion time distance is determined based on a ratio of the lateral distance and the lateral speed of the target obstacle, and the intrusion time distance represents a time when the target obstacle coincides with the predicted travel locus of the target vehicle in the lateral direction. The lateral distance represents a projection of the predicted motion trajectory of the target obstacle in a direction perpendicular to the predicted travel trajectory of the target vehicle, and the lateral speed represents a projection of the current speed vector of the target obstacle in a direction perpendicular to the predicted travel trajectory of the target vehicle. Illustratively, referring to FIG. 4, S represents the lateral distance and Vy represents the lateral velocity.
The lateral distance in this embodiment characterizes the positional relationship of the target obstacle with respect to the predicted travel track of the target vehicle in the vertical direction, reflecting the lateral distance between the target obstacle and the target vehicle, and its size directly affects whether the target vehicle has sufficient space to avoid a potential collision. The intrusion time distance characterizes the time required for the target obstacle to transversely coincide with the predicted travel track of the target vehicle, and reflects the time required for the target obstacle to transversely traverse the predicted travel track of the target vehicle at the current vehicle speed.
In combination with the two models, the application provides a more accurate and flexible speed limiting strategy, so that safer and more effective vehicle speed control can be realized under different traffic scenes, and meanwhile, the adaptability and the safety of the automatic driving vehicle are improved, and the traffic flow efficiency is optimized.
Fig. 7 is a schematic flow chart of determining a first target speed limit value by using a first speed limit model according to an embodiment of the application. The embodiment shown in fig. 7 is extended from the embodiment shown in fig. 4, and differences between the embodiment shown in fig. 7 and the embodiment shown in fig. 4 are described with emphasis, and the details of the differences are not repeated.
As shown in fig. 7, in the present embodiment, the first target speed limit value is determined using the first speed limit model, including the following steps.
Step S710, when the lateral distance is smaller than the preset distance threshold and the intrusion time interval is smaller than the preset time interval threshold, determining a first speed limit value by using the lateral distance and distance speed limit model, and determining a second speed limit value by using the intrusion time interval and time interval speed limit model.
In some embodiments, the lateral distance is input to a distance speed limit model to obtain a first speed limit value, and the intrusion time distance is input to a time distance speed limit model to obtain a second speed limit value. Illustratively, the preset distance threshold is 5 meters and the preset time distance threshold is 1m/s.
Step S720, calculating a first target speed limit value according to the first speed limit value, a first weight value corresponding to the first speed limit value, and a second weight value corresponding to the second speed limit value.
In some embodiments, the magnitudes of the first weight value and the second weight value may be adjusted according to a specific driving scenario, information of the target obstacle, and job requirements of the target vehicle, and so on.
In one example, if the type of target obstacle is predictable, traffic flow is normative, or the target vehicle is doing fine work in a small space, requiring greater sensitivity to the instantaneous location and lateral distance of the target obstacle, then the first weight value is set relatively large and the second weight value is set relatively small. For example, on narrow mine roads, or in obstacle-dense environments, the target vehicle needs to rely more on the current location of the target obstacle to react quickly, where the first weight value is greater.
In another example, where the behavior of the target obstacle is difficult to predict, or where the traffic environment is complex, the second weight value may be set relatively large while the first weight value is set relatively small. For example, at a mine intersection, or in an area where multiple obstacles work simultaneously, the target vehicle needs to rely more on the movement of the target obstacle for prediction and time response, and the second weight value will be larger.
Fig. 8 is a schematic diagram illustrating a calculation of a speed limit value by using a distance speed limit model and a time distance speed limit model according to an embodiment of the present application. It shows the relationship between the speed limit value and the corresponding lateral distance and speed of the target obstacle under the interaction of the two models.
As can be seen from fig. 8, the distance limit model has a greater impact on the limit value when the target obstacle is closer to the lateral distance of the target vehicle, because the presence of the target obstacle constitutes a direct threat to the immediate safety of the target vehicle in close range situations, and thus an immediate reduction in vehicle speed is required to avoid potential collisions.
As the lateral distance between the target obstacle and the target vehicle increases, the time-distance speed limit model begins to have a greater impact on the speed limit value. This is because at large lateral distances, while the target obstacle does not immediately pose a threat to the target vehicle, its state of motion and speed may have an impact on the safety of the target vehicle in the foreseeable future. Therefore, the time-lapse speed-limiting model adjusts the speed-limit value in consideration of the intrusion time-lapse of the target obstacle more, ensuring that the vehicle has enough time to respond to the movement change of the obstacle.
As can be seen from a combination of fig. 8, in some embodiments, in the case where the lateral distance is less than the preset distance threshold and the intrusion time distance is less than the preset time distance threshold, the first weight value increases with decreasing lateral distance and the second weight value increases with increasing lateral distance.
In this embodiment, when the lateral distance is smaller than the preset distance threshold, it is indicated that the target obstacle is closer to the target vehicle, and the intrusion time interval is smaller than the preset time interval threshold, it is indicated that the time required for the target obstacle to coincide with the predicted travel track of the target vehicle in the lateral direction is shorter. At this time, the potential collision risk is high, the first speed limit value is determined by using the distance speed limit model, and the second speed limit value is determined by using the time distance speed limit model, so that the target vehicle can keep a safe distance under different conditions, and meanwhile, the time collision is avoided. In addition, the embodiment also allows the weight value to be flexibly adjusted according to the characteristics of the target obstacle and the actual condition of the traffic environment, so that more accurate and timely response is realized, and an effective safety management strategy is provided for the target vehicle.
In other embodiments of the present application, in conjunction with the embodiment shown in fig. 7, 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 is capable of communicating with the target vehicle.
It should be noted that, when the target obstacle is capable of performing effective communication with the target vehicle, the position location indicating the target obstacle is very accurate, and therefore, the target vehicle can acquire more accurate obstacle attribute information including its position, speed, and movement intention. In this case, the second weight value will increase accordingly and respond more timely accordingly.
In some embodiments, the time-distance speed-limiting model comprehensively considers the current speed of the target obstacle and the transverse distance between the target obstacle and the target vehicle, and directly evaluates whether the target obstacle can cross the predicted running track of the target vehicle at a specific time point, thereby providing a more visual and flexible speed-limiting strategy. The time-distance model is particularly suitable for the situations of variable obstacle behaviors and complex traffic environments, such as intersections or open areas, and can be used for rapidly adapting to sudden movement changes of target obstacles. Therefore, the time-lapse speed-limiting model can utilize this accurate information to make more accurate predictions and responses when the target obstacle has the ability to communicate with the target vehicle. Therefore, in this case, the first weight value is set relatively small, and the second weight value is set relatively large.
In some embodiments, if the target obstacle is capable of active communication with the target vehicle, the first weight value may be set to zero directly and the second weight value may be set to 1, i.e., the first speed limit value of the target vehicle may be calculated using only the time-distance speed limit model.
In the embodiment, by considering the communication capability of the target obstacle, the information provided by the target obstacle can be more effectively utilized, the weight setting is optimized, and the accuracy and reliability of the speed limit decision are improved. This approach allows the target vehicle to pass through the potentially dangerous area in a manner closer to normal travel speed with sufficient information support to improve ride safety.
Fig. 9 is a schematic flow chart of determining a first target speed limit value by using a first speed limit model according to another embodiment of the application. The embodiment shown in fig. 9 is extended from the embodiment shown in fig. 4, and differences between the embodiment shown in fig. 9 and the embodiment shown in fig. 4 are described with emphasis, and the details of the differences are not repeated.
As shown in fig. 9, in the present embodiment, the first target speed limit value is determined using the first speed limit model, including the following steps.
In step S910, when the lateral distance is greater than or equal to the 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 according to the intrusion time interval and the time interval speed limit model.
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, it means that although the target obstacle is not very close to the target vehicle in space, there is a risk of colliding with the target vehicle in a limited time, depending on its state of motion and direction. While intrusion time provides a time-based assessment that enables the target vehicle to calculate a speed limit based on how urgent the target obstacle may enter its predicted travel path. Therefore, in step S910, the speed limit value may be calculated using only the time-distance speed limit model. This method allows for early reaction to calculate a suitable speed limit value when the target obstacle has not constituted an immediate threat, ensuring that the target vehicle has enough time to slow down before the target obstacle may enter its path of travel, thereby maintaining safe travel.
In step S920, when the lateral distance is smaller 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.
The lateral distance being less than the preset distance threshold indicates that the target obstacle is in close spatial proximity to the target vehicle with a potential collision risk. However, the intrusion time interval being greater than or equal to the preset time interval threshold means that the target obstacle intersects the trajectory of the target vehicle for a period of time from the time dimension, and the target vehicle is not currently immediately exposed to the threat of collision. In this case, the target vehicle is required to reduce the vehicle speed to increase the safety buffer, thereby reducing the risk of collision that may be caused by too close a distance.
The distance-limiting model focuses on the spatial relationship between the current position of the obstacle and the vehicle, and it is able to adjust the speed limit value according to the lateral distance. Therefore, in step S920, the speed limit value may be calculated only by using the distance speed limit model, so that the road resource can be effectively utilized while the target vehicle maintains the safe distance, and the influence on the traffic smoothness due to early or urgent deceleration is avoided.
The scheme of step S920 provides a driving strategy balancing safety and efficiency for the autonomous vehicle. This approach allows the target vehicle to slow down in time as the target obstacle approaches, while avoiding unnecessary traffic disturbances that may result from excessive reactions to time factors.
Fig. 10 is a schematic flow chart of obtaining a vehicle speed control strategy according to another embodiment of the present application. The embodiment shown in fig. 10 is extended from the embodiment shown in fig. 1, and differences between the embodiment shown in fig. 10 and the embodiment shown in fig. 1 are described with emphasis, and the details of the differences are not repeated.
As shown in fig. 10, in the present embodiment, a vehicle speed control strategy corresponding to target attribute information is acquired based on the target attribute information, including the following steps.
In step S1010, in the case where the scene type is the road type, the lateral distance of the target obstacle from the target vehicle is determined.
Specifically, the lateral distance of the target obstacle from the target vehicle may be determined according to the method in the foregoing embodiment.
Step S1020, determining a second target speed limit value using the lateral distance and the second speed limit model.
The second speed limit model is another specific model in the vehicle speed control strategy for determining the speed limit value of the target vehicle in a specific situation. Compared with the first speed limit model, the second speed limit model is more focused on making speed limit decisions by utilizing the spatial position information of the obstacle, and is suitable for road scenes with clear lane division and traffic rules. Through the model, the target vehicle can control the vehicle speed more accurately so as to adapt to the surrounding environment and ensure the driving safety.
In some embodiments, the second speed limit model includes a distance speed limit model that includes a mapping between lateral distance and speed limit values.
It should be reiterated that the road scene contains well-defined lane lines that provide structured road boundaries and driving directions for the target vehicle, thereby making the driving path of the target vehicle more predictable. In this scenario, lateral distance becomes a major factor in assessing potential collision risk. When the target obstacle approaches the target vehicle, the distance limit model calculates a lower limit value to ensure that the target vehicle has enough time and space to respond to any potential threat.
In addition, in a road scene, particularly in the case of a meeting, the lateral distance between vehicles may become small, and at the same time, since both vehicles are moving, the relative speed of the obstacle vehicle may be increased from the viewpoint of the target vehicle. This means that the point where the two vehicles meet reaches faster and therefore the intrusion time (i.e. the time the target obstacle is expected to collide with the target vehicle) is reduced. If the time interval speed limit model is relied on, the model can calculate an excessively low speed limit value due to the reduction of the intrusion time interval, so that the normal running efficiency of the target vehicle is affected. That is, when the target vehicle approaches the target obstacle meeting, the time-distance model may over react, resulting in an over-low speed limit, even if the actual risk is not high. In contrast, the distance speed limit model calculates a speed limit value by taking into account the lateral distance between the target obstacle and the target vehicle, and this method is more stable, and is not affected by the drastic decrease in intrusion time when a vehicle is involved. As long as the transverse distance is kept within the safety range, the target vehicle can keep relatively high vehicle speed, so that the use efficiency and the driving fluency of the road are improved.
In addition, the distance speed limiting model is more suitable for a structured road environment, because lane lines and traffic rules provide clear driving paths and space separation for vehicles, and uncertainty and potential conflict are reduced. In this case, the target vehicle can more efficiently utilize the available road space while maintaining a safe distance from other obstacles.
The vehicle control method embodiment of the present application is described above in detail with reference to fig. 1 to 10, and the vehicle control apparatus embodiment of the present application is described below in detail with reference to fig. 11. It should be understood that the description of the vehicle control method embodiment corresponds to the description of the vehicle control apparatus embodiment, and therefore, a portion not described in detail may be referred to the foregoing method embodiment.
Fig. 11 is a schematic structural view of a vehicle control apparatus according to an exemplary embodiment of the present application. As shown in fig. 11, a vehicle control apparatus provided in an embodiment of the present application includes:
A determining module 1110, configured to determine target attribute information when it is determined that there is a target obstacle that has a running conflict with the target vehicle, where 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 running conflict indicates that a predicted movement track of the target obstacle intersects with a predicted running track of the target vehicle;
An obtaining module 1120, configured to obtain a vehicle speed control policy corresponding to the target attribute information based on the target attribute information, where different target attribute information corresponds to different vehicle speed control policies;
The control module 1130 is configured to perform vehicle speed control on the target vehicle according to a vehicle speed control strategy.
In one embodiment of the application, the obstacle attribute information includes at least one of whether the target obstacle is capable of communicating with the target vehicle, and travel information of the target obstacle, the travel information including a position and/or a travel direction.
In one embodiment of the application, the scene type of the region comprises a road type and/or a non-road type.
In one embodiment of the application, the non-road type includes an open area and/or an intersection.
In an embodiment of the present application, the obtaining module 1120 is further configured to obtain a vehicle speed control policy based on the driving information of the target obstacle included in the obstacle attribute information, where the vehicle speed control policy includes determining whether to limit the speed of the target vehicle.
In an embodiment of the present application, the obtaining module 1120 is further configured to determine whether the target obstacle meets a cross-cut condition based on a position and/or a driving direction included in the driving information of the target obstacle, control the target vehicle to continue driving based on the current vehicle speed if the target obstacle does not meet the cross-cut condition, and/or determine the first target speed limit value using the first speed limit model if the target obstacle meets the cross-cut condition.
In an embodiment of the present application, 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 a lateral distance and a speed limit value, the time-distance speed limit model includes a mapping relationship between an intrusion time distance and the speed limit value, wherein the intrusion time distance is determined based on a ratio of the lateral distance to a lateral speed of the target obstacle, the intrusion time distance represents a time when the target obstacle coincides with a predicted travel track of the target vehicle in a lateral direction, the lateral distance represents a projection of the predicted travel track of the target obstacle in a direction perpendicular to the predicted travel track of the target vehicle, and the lateral speed represents a projection of a current speed vector of the target obstacle in a direction perpendicular to the predicted travel track of the target vehicle.
In an embodiment of the present application, the obtaining module 1120 is further configured to determine, when the lateral distance is smaller than a preset distance threshold and the intrusion time interval is smaller than a preset time interval threshold, 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, and calculate the first target speed limit value according to the first weight value corresponding to the first speed limit value and the second weight value corresponding to the second speed limit value and the second speed limit value.
In an embodiment of the present application, the obtaining 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.
In an embodiment of the present application, the obtaining module 1120 is further configured to determine the first target speed limit value according to the intrusion time interval and the time interval speed limit model 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, or determine the first target speed limit value according to the lateral distance and the distance speed limit model 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.
In an embodiment of the present application, the obtaining module 1120 is further configured to determine a lateral distance between the target obstacle and the target vehicle when the scene type is a road type, and determine a second target speed limit value by using the lateral distance and a second speed limit model, where 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.
Next, an electronic device according to an embodiment of the present application is described with reference to fig. 12. Fig. 12 is a schematic structural diagram of an electronic device according to an exemplary embodiment of the present application.
As shown in fig. 12, the electronic device 120 includes one or more processors 1201 and memory 1202.
The processor 1201 may be a Central Processing Unit (CPU) or other form of processing unit having data processing and/or instruction execution capabilities, and may control other components in the electronic device 120 to perform desired functions.
Memory 1202 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or nonvolatile memory. The volatile memory may include, for example, random Access Memory (RAM) and/or cache memory (cache), and the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, and the like. One or more computer program instructions may be stored on the computer readable storage medium that can be executed by the processor 1201 to implement the vehicle control methods and/or other desired functions of the various embodiments of the application described above. Various contents such as including target attribute information, a vehicle speed control strategy, a predicted travel track, a predicted motion track, and the like may also be stored in the computer-readable storage medium.
In one example, the electronic device 120 may also include an input device 1203 and an output device 1204, which are interconnected via a bus system and/or other form of connection mechanism (not shown).
The input device 1203 may include, for example, a keyboard, a mouse, and the like.
The output device 1204 may output various information to the outside, including target attribute information, a vehicle speed control strategy, a predicted travel locus, a predicted motion locus, and the like. The output device 1204 may include, for example, a display, speakers, a printer, and a communication network and remote output devices connected thereto, etc.
Of course, only some of the components of the electronic device 120 that are relevant to the present application are shown in fig. 12 for simplicity, components such as buses, input/output interfaces, etc. are omitted. In addition, the electronic device 120 may include any other suitable components depending on the particular application.
In addition to the methods and apparatus described above, embodiments of the application may also be a computer program product comprising computer program instructions which, when executed by a processor, cause the processor to perform the steps in the vehicle control method according to the various embodiments of the application described above in this specification.
The computer program product may write program code for performing operations of embodiments of the present application in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, embodiments of the present application may also be a computer-readable storage medium, on which computer program instructions are stored, which, when being executed by a processor, cause the processor to perform the steps in the vehicle control method according to the various embodiments of the present application described in the present specification.
The computer readable storage medium may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may include, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of a readable storage medium include an electrical connection having 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The basic principles of the present application have been described above in connection with specific embodiments, but it should be noted that the advantages, benefits, effects, etc. mentioned in the present application are merely examples and not intended to be limiting, and these advantages, benefits, effects, etc. are not to be construed as necessarily possessed by the various embodiments of the application. Furthermore, the specific details disclosed herein are for purposes of illustration and understanding only, and are not intended to be limiting, as the application is not necessarily limited to practice with the above described specific details.
The block diagrams of the devices, apparatuses, devices, systems referred to in the present application are only illustrative examples and are not intended to require or imply that the connections, arrangements, configurations must be made in the manner shown in the block diagrams. As will be appreciated by one of skill in the art, the devices, apparatuses, devices, systems may be connected, arranged, configured in any manner. Words such as "including," "comprising," "having," and the like are words of openness and mean "including but not limited to," and are used interchangeably therewith. The terms "or" and "as used herein refer to and are used interchangeably with the term" and/or "unless the context clearly indicates otherwise. The term "such as" as used herein refers to, and is used interchangeably with, the phrase "such as, but not limited to.
It is also noted that in the apparatus, devices and methods of the present application, the components or steps may be disassembled and/or assembled. Such decomposition and/or recombination should be considered as equivalent aspects of the present application.
The previous 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 generic principles defined herein may be applied to other aspects without departing from the scope of the application. Thus, the present application is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description has been presented for purposes of illustration and description. Furthermore, this description is not intended to limit embodiments of the application to the form disclosed herein. Although a number of example aspects and embodiments have been discussed above, a person of ordinary skill in the art will recognize certain variations, modifications, alterations, additions, and subcombinations thereof.