A vehicle control method, apparatus, device, vehicle and medium

Abstract

The application discloses a vehicle control method, device, equipment, vehicle and medium. The method comprises the following steps: determining whether a surrounding vehicle has a cut-in behavior; when it is determined that the surrounding vehicle has the cut-in behavior, controlling the ego vehicle to enter an automatic avoidance mode, and the automatic avoidance mode is configured to control the ego vehicle to automatically decelerate and / or automatically steer based on vehicle condition information. The vehicle control method provided in the embodiment of the application can automatically determine whether the surrounding vehicle has the cut-in behavior during vehicle driving, without human intervention, and the discrimination efficiency is improved. When the surrounding vehicle has the cut-in behavior, the automatic avoidance operation can be performed according to the vehicle condition information of the current vehicle, the occurrence of accidents is reduced, and the safe driving performance is improved.

Description

[0001] This application claims priority to Chinese Patent Application No. 202211220133.0, filed with the State Intellectual Property Office of China on September 30, 2022, entitled "A Vehicle Control Method, Apparatus, Equipment, Vehicle and Medium", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application generally relates to vehicles, and specifically to a vehicle control method, device, equipment, vehicle, and medium. Background Technology

[0003] Cutting in line refers to the act of overtaking or using the opposite lane to cut into waiting vehicles when there is a queue of stopped or slow-moving vehicles ahead. Cutting in line not only severely impacts urban traffic efficiency but is also a major contributing factor to traffic accidents.

[0004] However, current technologies primarily focus on prevention when dealing with lane-cutting. But in real-world scenarios, there are many unexpected lane-cutting situations. Considering the varying emergency response capabilities of drivers, simply issuing warnings is insufficient to guarantee safe driving. Therefore, it is necessary to further consider how to ensure the vehicle remains safe after lane-cutting occurs. Summary of the Invention

[0005] In view of the above-mentioned defects or deficiencies in the prior art, it is desirable to provide a vehicle control method, device, equipment, vehicle and medium.

[0006] In a first aspect, this application provides a vehicle control method, the method comprising:

[0007] Determine if any surrounding vehicles are cutting in line;

[0008] When it is determined that the surrounding vehicles are cutting in, the vehicle is controlled to enter an automatic avoidance mode. The automatic avoidance mode is configured to control the vehicle to automatically decelerate and / or automatically steer based on vehicle condition information.

[0009] Optionally, the method for determining whether surrounding vehicles are cutting in line includes:

[0010] Obtain vehicle condition information for this vehicle and surrounding vehicles, including location information;

[0011] Based on the vehicle condition information, determine whether the risk conditions have been met;

[0012] When the vehicle condition information is confirmed to meet the risk conditions, it is determined that the surrounding vehicles are cutting in line.

[0013] Optionally, the vehicle condition information includes current lane information, and the step of determining whether a risk condition has been met based on the vehicle condition information includes:

[0014] Based on the current lane of this vehicle and the current lanes of surrounding vehicles, determine whether the surrounding vehicles are in the same lane as this vehicle.

[0015] When it is determined that surrounding vehicles are in the same lane as the vehicle, it is judged whether the longitudinal distance between the surrounding vehicles and the vehicle is less than a longitudinal distance threshold; if the longitudinal distance is less than the longitudinal distance threshold, it is determined that the vehicle condition information meets the risk condition. Optionally, obtaining the vehicle condition information of the vehicle and surrounding vehicles includes:

[0016] Lane environment information is constructed based on the collected environmental image information;

[0017] Based on the lane environment information, obtain the positioning information of this vehicle and surrounding vehicles;

[0018] Based on the location information, obtain the current lane of this vehicle and the current lanes of surrounding vehicles.

[0019] Optionally, the method for constructing lane environment information based on the acquired environmental image information includes:

[0020] Lane lines are obtained based on the acquired environmental image information, and lane environment information is constructed based on the lane lines; or

[0021] Based on the collected environmental image information, when it is confirmed that no lane line has been obtained, a virtual lane line of a preset width is constructed with the vehicle as the center, and lane environment information is constructed based on the virtual lane line.

[0022] Optionally, the method for determining whether surrounding vehicles are in the same lane as the vehicle includes:

[0023] Obtain the lane line of the current lane of the vehicle and the lateral distance between the vehicle and surrounding vehicles;

[0024] Based on the lateral distance, determine whether the distance between the surrounding vehicles and the lane line of the current lane of the vehicle is less than the lane threshold.

[0025] If the value is less than the value, then it is determined that the surrounding vehicles are in the same lane as this vehicle.

[0026] Optionally, determining whether the longitudinal distance between the vehicle and surrounding vehicles is less than a longitudinal distance threshold can be achieved through methods including:

[0027] Collect vehicle ranging images and radar ranging information;

[0028] The longitudinal distance between the vehicle and surrounding vehicles is obtained based on vehicle ranging images and radar ranging information.

[0029] Based on the longitudinal vehicle distance, it is determined whether it is less than the longitudinal vehicle distance threshold.

[0030] Optionally, the method for correcting the longitudinal distance between the vehicle and surrounding vehicles based on vehicle ranging images and radar ranging information includes:

[0031] The camera vehicle distance information is obtained based on the vehicle ranging image;

[0032] Obtain a first vehicle distance correction coefficient corresponding to the camera vehicle distance information and a second vehicle distance correction coefficient corresponding to the radar ranging information;

[0033] The longitudinal distance between the vehicle and surrounding vehicles is corrected based on the first vehicle distance correction coefficient and the second vehicle distance correction coefficient. The longitudinal distance is calculated as: camera distance information * first vehicle distance correction coefficient + radar ranging information * second vehicle distance correction coefficient.

[0034] Optionally, controlling the vehicle to enter automatic obstacle avoidance mode includes:

[0035] Based on the vehicle condition information, determine whether the longitudinal distance between this vehicle and vehicles in adjacent lanes is within the lane change threshold range;

[0036] If so, control the vehicle to perform a lane change operation;

[0037] If not, control the vehicle to reduce its speed to create distance from vehicles cutting in.

[0038] Optionally, the method further includes:

[0039] When it is determined that the surrounding vehicles are cutting in, it is determined whether the vehicle has performed a manual avoidance maneuver.

[0040] If it is confirmed that the vehicle has not performed a manual avoidance maneuver, then the vehicle is controlled to enter automatic avoidance operation. Optionally, the method further includes:

[0041] If it is confirmed that the vehicle has a first manual avoidance operation, then the vehicle is controlled to perform the first manual avoidance operation based on the first manual avoidance operation, wherein the first manual avoidance operation is at least one of steering operation and deceleration operation;

[0042] If it is confirmed that the vehicle has not performed a first manual avoidance maneuver, then the vehicle is controlled to enter the automatic avoidance mode.

[0043] Optionally, if it is confirmed that the vehicle has not performed a first manual avoidance maneuver, and after controlling the vehicle to enter automatic avoidance mode, the method further includes:

[0044] The determination continues to determine whether the vehicle has performed a second manual avoidance maneuver, wherein the second manual avoidance maneuver is at least one of a steering maneuver and a deceleration maneuver;

[0045] In response to the presence of the second manual avoidance operation of the vehicle, the vehicle exits the automatic avoidance mode and controls the vehicle to perform the second manual avoidance operation based on the second manual avoidance operation.

[0046] Optionally, determining whether the vehicle has performed a manual avoidance maneuver includes:

[0047] Obtain the vehicle's operating information, which includes at least one of steering wheel operation information and brake operation information;

[0048] If the vehicle's operating information changes, it is confirmed that the vehicle is in the process of manually avoiding a collision.

[0049] Optionally, the method further includes:

[0050] Based on the vehicle's operating information, it is determined whether the operating information meets the intervention conditions, and the intervention conditions are configured such that the operating information exceeds the operating safety threshold.

[0051] If the operating information is confirmed to meet the intervention conditions, the vehicle is controlled to perform an automatic intervention operation, which is configured to perform at least one of the following: increasing steering wheel resistance, adjusting steering angle, adjusting acceleration, and adjusting speed.

[0052] If it is confirmed that the operating information does not meet the intervention conditions, the vehicle is controlled to perform manual avoidance based on the manual avoidance operation.

[0053] Optionally, the method for determining whether the operational information meets the intervention conditions includes:

[0054] Based on the vehicle speed, a safety factor is constructed corresponding to different manual avoidance operations at different vehicle speeds, wherein the manual avoidance operation is at least one of steering operation and deceleration operation;

[0055] Based on the current operating information of the vehicle, obtain the current safety factor corresponding to the current operating information;

[0056] Determine whether the current security factor exceeds the security threshold;

[0057] If it is confirmed that the current safety factor exceeds the safety threshold, then it is confirmed that the operation information meets the intervention conditions.

[0058] Optionally, if it is confirmed that the vehicle has not performed a manual avoidance maneuver, and the vehicle is then controlled to enter automatic avoidance mode, the method further includes:

[0059] Obtain vehicle condition information for the vehicle itself and surrounding vehicles, including vehicle speed and location information;

[0060] Based on the vehicle condition information, determine whether the vehicle will collide with a vehicle that cuts in front;

[0061] If it is determined that the vehicle will not collide with the vehicle cutting in, the vehicle will be controlled to exit the automatic avoidance mode.

[0062] Optionally, the method further includes: when it is determined that the surrounding vehicles are cutting in line, entering a reminder mode, the reminder mode being configured to execute at least one of a warning sound and a light reminder.

[0063] Secondly, embodiments of this application provide a vehicle control device, including:

[0064] The data acquisition module is used to collect vehicle condition information;

[0065] The central processing module is used to determine whether surrounding vehicles are cutting in line; when it is determined that the surrounding vehicles are cutting in line, it controls the vehicle to enter an automatic avoidance mode.

[0066] The steering and braking systems are configured to execute an automatic avoidance mode, which is configured to control the vehicle to automatically decelerate and / or automatically steer based on vehicle condition information.

[0067] Thirdly, embodiments of this application provide a vehicle control device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described in the embodiments of this application.

[0068] Fourthly, embodiments of this application provide a vehicle, including the vehicle control device or vehicle control equipment as described above.

[0069] Fifthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in embodiments of this application.

[0070] The vehicle control method provided in this application embodiment automatically determines whether surrounding vehicles are cutting in line during vehicle operation without human intervention, thus improving the efficiency of judgment. When surrounding vehicles are cutting in line, automatic avoidance operations can be taken based on the current vehicle condition information to reduce the occurrence of accidents and improve safe driving performance.

[0071] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0072] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0073] Figure 1 A flowchart of a vehicle control method provided for an embodiment of this application;

[0074] Figure 2 A flowchart of another vehicle control method provided for embodiments of this application;

[0075] Figure 3 A schematic diagram illustrating an application scenario of a vehicle control method provided in an embodiment of this application;

[0076] Figure 4 A flowchart of yet another vehicle control method provided for embodiments of this application;

[0077] Figure 5 A schematic diagram of the structure of a vehicle control system provided for an embodiment of this application;

[0078] Figure 6 This is a schematic diagram of the structure of a vehicle control device provided for an embodiment of this application. Detailed Implementation

[0079] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0080] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0081] Please refer to Figure 1 This application provides a vehicle control method, the method comprising:

[0082] S01. Determine if any surrounding vehicles are cutting in line;

[0083] S02. When it is determined that the surrounding vehicles are cutting in, the vehicle is controlled to enter an automatic avoidance mode. The automatic avoidance mode is configured to control the vehicle to automatically decelerate and / or automatically steer based on vehicle condition information.

[0084] It should be noted that in this application, "deceleration" refers to increasing the longitudinal distance between the vehicle and surrounding vehicles to ensure a safe distance and keep the vehicle in a safe state. In this application, "steering" refers to increasing the lateral distance between the vehicle and surrounding vehicles by moving closer to the lane line in the vehicle's current lane away from surrounding vehicles, thus ensuring the vehicle is in a safe state. When performing automatic steering, the vehicle can deflect only within its current operating range, or it can deflect in a flank manner away from other lanes; this application does not limit this.

[0085] In this embodiment of the application, the method for determining whether surrounding vehicles are cutting in line in step S01 includes:

[0086] S110. Obtain vehicle condition information of the vehicle itself and surrounding vehicles, wherein the vehicle condition information includes location information.

[0087] S120. Based on the vehicle condition information, determine whether a risk condition has been met. The risk condition includes that the longitudinal distance between the vehicle and surrounding vehicles is less than a longitudinal distance threshold.

[0088] S130. When the vehicle condition information is confirmed to meet the risk conditions, it is determined that the surrounding vehicles have cut in line.

[0089] This application embodiment uses the acquisition of vehicle status information from an Advanced Driving Assistant System (ADAS) as an example for illustration. Typically, ADAS includes various sensors. During vehicle operation, ADAS can continuously sense the surrounding environment, collect data, identify, detect, and track static and dynamic objects, and combine this data with vehicle status map data for system calculations and analysis. ADAS can implement functions such as vehicle status monitoring, adaptive cruise control, lane information, intersection information, lane departure warning, lane keeping assist, collision avoidance, adaptive headlight control, pedestrian collision warning, automatic parking, traffic sign recognition, blind spot detection, driver fatigue detection, and downhill control.

[0090] In application, one or more vehicle status information can be used to determine whether there is cutting-in behavior. In one embodiment of the present invention, in order to more accurately determine whether surrounding vehicles have cut in, it is necessary to consider the vehicle's operating status (location information) to determine whether the vehicle has changed lanes, so as to avoid misjudgment due to changes in the vehicle's operating status, such as misjudging the target vehicle as cutting in due to the vehicle's own lane change behavior.

[0091] In this embodiment of the application, the vehicle condition information includes current lane information. Step S120, which determines whether a risk condition has been met based on the vehicle condition information, includes:

[0092] S121. Based on the current lane of the vehicle and the current lanes of surrounding vehicles, determine whether the surrounding vehicles are in the same lane as the vehicle.

[0093] Specifically, this includes: obtaining the lane line of the current lane of the vehicle and the lateral distance between the vehicle and surrounding vehicles; determining, based on the lateral distance, whether the distance between the surrounding vehicles and the lane line of the current lane of the vehicle is less than a lane threshold; if it is less, determining that the surrounding vehicles and the vehicle are in the same lane.

[0094] It should be noted that in this application, when a vehicle is crossing the lane, both the determination of the vehicle's current lane and the determination of the current lanes of surrounding vehicles can be done using an approximation method, such as... Figure 2 As shown, when the surrounding vehicles gradually move from lane B towards lane A, as... Figure 2 As shown in (I)-(II), and when there is a lane crossing with lane A, it is determined that the surrounding vehicles are... Figure 2 In state (III), surrounding vehicles are in the lane (lane A) where this vehicle is located. Of course, in some other embodiments, when both this vehicle and surrounding vehicles cross the same lane line, they can be assumed to be in the same lane. In this application, an example is provided assuming that this vehicle does not cross the lane line.

[0095] For example, by analyzing the distance of surrounding vehicles from the edge of the lane line of the current lane of the vehicle in real time through the constructed three-dimensional environmental information, when the distance is 0m (surrounding vehicles are crossing the lane line of the current lane of the vehicle), the system will identify the vehicle crossing the line as a potential lane-cutting vehicle. If surrounding vehicles continue to intrude into the current lane by more than 0.5m within a preset time, it will be determined that the vehicle will enter the current lane, that is, the current lane of the surrounding vehicles is the same as the current lane of the vehicle.

[0096] S122. When it is determined that the surrounding vehicles are in the same lane as the vehicle, determine whether the longitudinal distance between the surrounding vehicles and the vehicle is less than the longitudinal distance threshold.

[0097] If the distance between the surrounding vehicles and the vehicle is relatively far when the surrounding vehicles change lanes, for example, if the longitudinal distance between the surrounding vehicles and the vehicle is 15m when the surrounding vehicles change lanes, then the surrounding vehicles are changing lanes normally and have not affected the normal driving of the vehicle. Therefore, in order to determine whether the surrounding vehicles are cutting in, it is necessary to determine whether the longitudinal distance between the surrounding vehicles and the vehicle is less than the longitudinal distance threshold.

[0098] It should be noted that in this application, the longitudinal distance between the vehicle and surrounding vehicles refers to the longitudinal distance, that is, the distance along the direction of vehicle travel. The lateral distance in this application (e.g., the distance between the vehicle and surrounding vehicles) is different. Figure 2 As shown in X1, the determination is based on the vehicle's lane. This application illustrates first determining whether they are in the same lane and then determining the longitudinal distance (e.g., ...). Figure 2 As shown in X2, this application is not limited to this method. In some embodiments, there may be multiple vehicles around, and the longitudinal distance between vehicles can be used as a prediction of potential vehicles cutting in.

[0099] Furthermore, this application does not limit the size of the longitudinal vehicle distance threshold. In different situations, the judgment can be based on the road conditions at the time of the vehicle's movement. Of course, different thresholds can also be set in combination with other different conditions. For example, when the vehicle speed is in the first range, the longitudinal vehicle distance is determined by the first threshold; when the vehicle speed is in the second range, the longitudinal vehicle distance is determined by the second threshold. By further limiting the threshold based on vehicle speed, it is possible to better judge cutting-in behavior.

[0100] S123. If the longitudinal vehicle distance is less than the longitudinal vehicle distance threshold, then the vehicle condition information is determined to have reached the risk condition.

[0101] In this application, if surrounding vehicles are in the same lane as the vehicle and the longitudinal distance between the surrounding vehicles and the vehicle is less than the longitudinal distance threshold, then the vehicle condition information is confirmed to meet the risk conditions and the surrounding vehicles are determined to have cut-in behavior. If surrounding vehicles are not in the same lane as the vehicle or the longitudinal distance between the surrounding vehicles and the vehicle is not less than the longitudinal distance threshold, then the vehicle condition information is confirmed not to meet the risk conditions and the surrounding vehicles are determined not to have cut-in behavior.

[0102] It is understood that the examples described in this application illustrate a method for determining the cutting-in behavior of surrounding vehicles, but the application is not limited to this. The order of determining the longitudinal distance and the current lane is not restricted in this application. For example, the longitudinal distance can be determined first, followed by the current lane, or both can be considered as having cut-in behavior if certain conditions are met simultaneously. In other embodiments, other determination conditions can be combined for further determination of whether cutting-in behavior has occurred.

[0103] It should be further explained that there may be a variety of situations when obtaining the lane where the vehicle is located. For example, the vehicle may be in two lanes (crossing the lane). The method for determining the lane where the vehicle is located can be an approximation method, that is, within a preset time, the distance (lateral distance) between the vehicle and the lane line is collected. If the vehicle continues to move closer to a certain lane, it is considered that the vehicle is in the approaching lane.

[0104] This application also provides a method for obtaining the current lane, such as... Figure 2 As shown, the method for obtaining the current lane information of the vehicle and surrounding vehicles includes:

[0105] Lane environment information is constructed based on the collected environmental image information; the positioning information of the vehicle and surrounding vehicles is obtained based on the lane environment information; the current lane of the vehicle and the current lanes of surrounding vehicles are obtained based on the positioning information.

[0106] Specifically, constructing lane environment information based on the collected environmental image information includes: obtaining lane lines based on the collected environmental image information and constructing lane environment information based on the lane lines; or, when it is confirmed that no lane lines have been obtained based on the collected environmental image information, constructing a virtual lane line of a preset width centered on the vehicle and constructing lane environment information based on the virtual lane lines.

[0107] It should be noted that, in this embodiment, the lane environment information can be either two-dimensional or three-dimensional image information. In an exemplary embodiment of this application, the camera module acquires road and vehicle information in front of and to both sides of the vehicle at a rate of 30 frames per second, and transmits it to the central processing module via a coaxial cable. The lidar module emits laser pulses to generate millions of point cloud data, which are transmitted to the central processing module via Ethernet. The acquired road and vehicle information is analyzed by the central processing module to construct a complete three-dimensional image of the surrounding environment.

[0108] The lane recognition module in the central processing module extracts lane information from the 3D image. If there are no lane lines in the road information captured by the camera, a virtual lane line with a width of 3m is constructed centered on the vehicle. If there are identifiable lane lines, the actual lane lines are used as the reference. Figure 3 As shown, the lane lines in the diagram can be either physical lane lines or virtual lane lines.

[0109] It should be noted that the technology for identifying lane line information in environmental image information in this application can be SVM (Support Vector Machine), CNN (Convolutional Neural Networks), or LSD (Line Segment Detector). Of course, it is not limited to these, and can be any of the existing image recognition lane line technologies, which will not be elaborated here.

[0110] In this embodiment, the method for obtaining the lane where the vehicle is located can employ image processing and analysis techniques, such as tracking each vehicle in the image captured by the camera module. The technology for tracking vehicles in the image can be any existing vehicle tracking technology, which will not be elaborated here. After tracking each vehicle, the location information of each vehicle (the vehicle itself and surrounding vehicles) can be obtained. The technology for obtaining the location information of each vehicle can be any existing vehicle trajectory behavior analysis technology, which will not be elaborated here.

[0111] Since there may be multiple vehicles in the surrounding area of ​​the lane ahead, in order to determine whether a cutting-in behavior has occurred, it is necessary to determine whether the vehicle closest to the vehicle is the one that cut in front. Therefore, the first vehicle in the lane ahead with the smallest longitudinal distance from the vehicle is identified as the target vehicle for subsequent steps.

[0112] In one embodiment of this application, a method for determining the longitudinal distance between the vehicle and surrounding vehicles is shown, comprising:

[0113] Collect vehicle ranging images and radar ranging information; correct the longitudinal distance between the vehicle and surrounding vehicles based on the vehicle ranging images and radar ranging information; determine whether the surrounding vehicles have cut in line based on the corrected longitudinal distance between the vehicle and surrounding vehicles.

[0114] To further improve ranging accuracy, this application employs a dual testing method using both a camera module and a lidar module, correcting the obtained ranging information through these two methods. Specifically, this includes:

[0115] Based on the vehicle ranging image, obtain camera distance information; obtain a first distance correction coefficient corresponding to the camera distance information and a second distance correction coefficient corresponding to the radar ranging information; correct the longitudinal distance between the vehicle and surrounding vehicles based on the first distance correction coefficient and the second distance correction coefficient, wherein the longitudinal distance = camera distance information * first distance correction coefficient + radar ranging information * second distance correction coefficient.

[0116] In this embodiment, by using camera ranging and lidar ranging, the ranging error caused by the shooting angle problem when using camera ranging is avoided. In this application, the vehicle distance correction coefficient can be obtained through various methods such as simulation experiments, mathematical models, and machine learning. By adjusting the vehicle distance correction coefficient, the ranging accuracy is improved, thereby timely and accurately predicting vehicles cutting in and taking avoidance measures.

[0117] For example, based on the focal length of the camera module, the baseline longitudinal distance between vehicles, and the parallax (the correspondence between each pixel in the left camera and the corresponding point in the right camera in the forward-looking binocular camera), the longitudinal distance L1 between the surrounding vehicles and the vehicle itself is calculated; the lidar module indirectly obtains the laser flight time based on the frequency difference obtained by linearly modulating the laser light frequency, and calculates the longitudinal distance L2 between the surrounding vehicles and the vehicle itself.

[0118] For example, the longitudinal distance data L1 and L2 are weighted to obtain the longitudinal distance L between the vehicle and surrounding vehicles: L = [(L1 × 20%) + (L2 × 80%)]. For instance, if the longitudinal distance calculated through correction exceeds 15m, the surrounding vehicles are considered to be changing lanes normally, with no risk of cutting in. It is understood that the risk threshold for the longitudinal distance between the vehicle and surrounding vehicles can be set differently in different embodiments, or the effect of risk prevention can be improved by setting segmented risk thresholds under different operating conditions (e.g., different vehicle speeds).

[0119] In step S02 of this application, the method of controlling the vehicle to enter the automatic avoidance mode includes:

[0120] S210. Obtain the vehicle condition information of the vehicle, wherein the vehicle condition information includes at least one of vehicle speed, location information, and current lane information;

[0121] S220. Based on the vehicle's condition information, select an automatic avoidance operation, wherein the automatic avoidance operation includes at least one of automatic steering and automatic deceleration.

[0122] S230. Based on the automatic avoidance operation, control the vehicle to perform automatic avoidance.

[0123] It is understood that the automatic obstacle avoidance mode of this vehicle can be set in the system to be enabled or disabled, and is enabled by default. This application does not limit the execution method of the automatic obstacle avoidance operation. In different embodiments, the automatic obstacle avoidance operation can be determined based on operating information or environmental information, for example, controlling the vehicle to enter the automatic obstacle avoidance mode includes:

[0124] Based on the vehicle condition information, it is determined whether the longitudinal distance between the vehicle and vehicles in adjacent lanes is within the lane-changing threshold range. If the longitudinal distance is within the safe distance range, the turn signal is automatically activated, and the vehicle's steering system is controlled to complete the lane-changing operation. If the longitudinal distance is not within the lane-changing threshold range, it is determined that a lane change is not possible, and the vehicle's braking system is controlled to reduce the vehicle speed and increase the distance from vehicles cutting in.

[0125] In this application, various operating coefficients corresponding to different automatic avoidance operation information can be preset based on different vehicle condition information. These coefficients are used to execute different automatic avoidance operations after a lane-cutting behavior is detected. For example, when the vehicle speed is less than 10 km / h, the steering coefficient operating coefficient is 0.9 to 1; when the vehicle speed is greater than 10 km / h but less than 30 km / h, the steering coefficient operating coefficient is Z = 0.0375v. 2 +0.0375v +0.9875 (v is vehicle speed); when the vehicle speed is greater than 30km / h and less than 160km / h, the steering coefficient is Z = 1.8687e. -0.399v (v is the vehicle speed).

[0126] For example, to analyze whether braking deceleration will cause vehicle instability, when the vehicle speed is less than 5 km / h, the maximum deceleration operation coefficient is a ≤ 5 m / s²; when the vehicle speed is greater than 5 km / h but less than 20 km / h, the deceleration operation coefficient is a = -0.1v + 5.5 (v is the vehicle speed); when the vehicle speed is greater than 20 km / h, the maximum deceleration operation coefficient is a ≤ 3.5 m / s².

[0127] It should be noted that, in the embodiments of this application, when performing automatic avoidance operation on the vehicle, the execution method of automatic deceleration and automatic steering includes the calculation of operation coefficients. By using the calculation method of operation coefficients provided in this application, the automatic avoidance operation suitable for the current vehicle condition is selected, that is, one or more of automatic steering and automatic deceleration are selected.

[0128] like Figure 4 As shown, the method described in this application further includes: S03, when it is determined that the surrounding vehicles are cutting in, determining whether the vehicle has performed a manual avoidance operation;

[0129] S04. If it is confirmed that the vehicle has not performed a manual avoidance operation, then control the vehicle to enter the automatic avoidance operation.

[0130] In this embodiment of the application, in the automatic avoidance mode, it is continuously determined whether the driver takes manual avoidance operation. The priority of manual avoidance operation is higher than that of automatic avoidance operation. This method enables the driver to take active avoidance based on the current vehicle condition or the surrounding environment, avoiding the limitations of using image acquisition, artificial intelligence and other methods to achieve autonomous driving judgment, and improving user experience and safety performance.

[0131] In steps S03 and S04 of this application, the manual avoidance operation includes a first manual avoidance operation, and the method includes:

[0132] S310. Based on the operational information, if it is confirmed that the vehicle has a first manual avoidance operation, then control the vehicle to perform the first manual avoidance operation, wherein the first manual avoidance operation is at least one of a steering operation and a deceleration operation. In specific processing, for example, the method includes:

[0133] S301. Obtain the vehicle's operating information, which includes at least one of steering wheel operation information and brake operation information; S302. In response to a change in the vehicle's operating information, confirm that the vehicle is performing a manual avoidance maneuver.

[0134] In this application, the detection methods for operational information include, but are not limited to, detecting the torque of the steering system and the pedal pressure of the braking system. When a sudden increase in steering torque or brake pedal pressure occurs, the system determines that the driver has intervened, that is, confirms that the vehicle is performing a manual obstacle avoidance maneuver. If the system is in automatic obstacle avoidance mode at this time, it will exit automatic obstacle avoidance mode and perform a manual obstacle avoidance maneuver.

[0135] It should be noted that the information used in this application to determine steering wheel operation includes, but is not limited to, the method of steering torque, and the information used to determine braking operation includes, but is not limited to, the method of brake pedal pressure. In application, other methods may also be used to determine whether a manual avoidance operation has occurred.

[0136] In some embodiments, the vehicle's steering angle is used to determine if a steering operation has been performed, and the steering operation process is characterized by recognizing the change in vehicle angle. In other embodiments, the vehicle's positioning information is used to determine whether a steering or braking operation has been performed. For example, the presence of a steering operation can be determined by judging whether the lateral distance to other vehicles has increased, and the presence of a braking operation can be determined by judging whether the longitudinal distance to other vehicles has increased.

[0137] S320. If it is confirmed that the vehicle has not performed a first manual avoidance operation, then control the vehicle to enter the automatic avoidance mode.

[0138] The automatic obstacle avoidance mode in this application can also correct driver operations. If the system detects that the driver has made steering or braking operations, it determines whether the steering torque and braking deceleration exceed the operating safety threshold based on the vehicle's operating information. If they exceed the threshold, compensation and correction are performed. If they do not exceed the threshold, the automatic obstacle avoidance mode is exited, and the vehicle operation is controlled based on the driver's operation.

[0139] Optionally, the method further includes:

[0140] S330. Continue to determine whether the vehicle has a second manual avoidance operation, wherein the second manual avoidance operation is at least one of a steering operation and a deceleration operation;

[0141] S340. In response to the existence of the second manual avoidance operation of the vehicle, the vehicle exits the automatic avoidance mode and controls the vehicle to perform the second manual avoidance based on the second manual avoidance operation.

[0142] It is understood that the specific operating conditions of the first and second manual obstacle avoidance operations are not limited in the embodiments of this application. In different embodiments, various manual vehicle handling operations can be adopted depending on the vehicle's operating environment. Both the first and second manual obstacle avoidance operations involve the driver taking proactive avoidance actions based on the current vehicle condition or surrounding environment, thus improving vehicle intelligence and user-friendliness. Whether during the first or second manual obstacle avoidance operation, this application continuously and automatically intervenes to determine the vehicle's behavior, improving installation performance.

[0143] Specifically, the method further includes:

[0144] S05. Based on the vehicle's operating information, determine whether the operating information meets the intervention conditions, wherein the intervention conditions are configured such that the operating information exceeds a safety threshold;

[0145] S06. If it is confirmed that the operation information meets the intervention conditions, the vehicle is controlled to perform an automatic intervention operation, wherein the automatic intervention operation is configured to perform at least one of increasing steering wheel resistance, adjusting steering angle, adjusting acceleration, and adjusting speed.

[0146] S07. If it is confirmed that the operation information does not meet the intervention conditions, then control the vehicle to perform manual avoidance based on the manual avoidance operation.

[0147] It is understood that in this embodiment, steps S05-S07 can occur before or during the vehicle's automatic obstacle avoidance operation. That is, in step S03, after determining whether the vehicle is performing a manual obstacle avoidance operation, if it is confirmed that the vehicle is performing a manual obstacle avoidance operation, then steps S05-S07 are executed. Whether before or during automatic obstacle avoidance mode, this effectively monitors and corrects the driver's operation information, improving driving safety.

[0148] This application also provides a method for determining whether the operational information meets the intervention conditions, including:

[0149] S410. Based on the vehicle speed, construct a safety factor corresponding to different manual avoidance operations at different vehicle speeds, wherein the manual avoidance operation is at least one of steering operation and deceleration operation;

[0150] S420. Based on the current operating information of the vehicle, obtain the current safety factor corresponding to the current operating information;

[0151] S430. Determine whether the current security factor exceeds the security threshold;

[0152] S440. If it is confirmed that the current safety factor exceeds the safety threshold, then it is confirmed that the operation information meets the intervention conditions.

[0153] This application sets different intervention conditions for different operating information. For example, it analyzes whether the steering coefficient (steering wheel rotation travel) will cause a collision with adjacent vehicles. When the vehicle speed is less than 10 km / h, the safe threshold for the steering coefficient is 0.9 to 1; when the vehicle speed is greater than 10 km / h but less than 30 km / h, the safe threshold for the steering coefficient is Z = 0.0375v. 2 +0.0375v + 0.9875 (v is vehicle speed); when the vehicle speed is greater than 30km / h but less than 160km / h, the safe threshold for the steering coefficient is Z = 1.8687e. -0.399v (v is the vehicle speed).

[0154] For example, to analyze whether braking deceleration will cause vehicle instability, when the vehicle speed is less than 5 km / h, the maximum deceleration safety threshold is a ≤ 5 m / s²; when the vehicle speed is greater than 5 km / h but less than 20 km / h, the deceleration safety threshold is a = -0.1v + 5.5 (v is the vehicle speed); when the vehicle speed is greater than 20 km / h, the maximum deceleration safety threshold is a ≤ 3.5 m / s². If, after system analysis, the steering coefficient and braking deceleration are both within the safety thresholds, the vehicle exits the automatic avoidance mode.

[0155] The automatic avoidance mode in this application includes, but is not limited to, the following operation methods for automatic intervention: when it is determined that the steering system torque exceeds the safety threshold, the steering wheel resistance is increased to suppress oversteering; when it is determined that the braking system deceleration exceeds the safety threshold, the braking system is controlled to perform rapid intermittent braking to maintain the vehicle's attitude and prevent vehicle instability.

[0156] The method described in this application also includes:

[0157] The system acquires vehicle condition information of itself and surrounding vehicles, including vehicle speed and location information; based on the vehicle condition information, it determines whether the vehicle will collide with a vehicle cutting in front of it; if it determines that the vehicle will not collide with the vehicle cutting in front of it, it determines whether the vehicle is in a safe operating state; if it confirms that the vehicle is in the safe operating state, it controls the vehicle to exit the automatic avoidance mode.

[0158] It is understood that various methods can be used in this application embodiment to determine whether a collision will occur between the vehicle and the vehicle cutting in. For example, based on the current speed of the vehicle, the current speed of the vehicle cutting in, and the current distance between the two vehicles, the collision time between the vehicle and the vehicle cutting in can be calculated. If the collision time is greater than a preset time threshold, it is determined that the vehicle will not collide with the vehicle cutting in. If the collision time is not greater than the preset time threshold, it is determined that the vehicle will collide with the vehicle cutting in, and the automatic avoidance mode continues to be executed.

[0159] In addition, this embodiment also includes confirming whether the vehicle is in a safe operating state after the automatic avoidance operation is performed. This involves confirming the vehicle's operating state in relation to other surrounding vehicles (excluding those cutting in), such as whether a collision will occur after the automatic avoidance operation. If it is confirmed that a collision with other surrounding vehicles will not occur, the vehicle is considered to be in a safe operating state, and the automatic avoidance mode is exited. If a collision with other surrounding vehicles is possible, the automatic avoidance operation continues.

[0160] In other embodiments of this application, the method further includes: when it is determined that the surrounding vehicles are cutting in line, entering a reminder mode, the reminder mode being configured to execute at least one of a warning sound, a display screen, a light reminder, and an operation reminder, the operation reminder including at least one of a turn reminder and a lane change reminder.

[0161] In this application embodiment, the warning prompt sound is implemented by emitting a prompt sound through a speaker module. It can be understood that the warning prompt sound in this application can be set in the system to enable or disable the prompt sound.

[0162] The display screen can be implemented by: displaying the vehicle cutting in line in an animated form on the display screen. The display screen includes, but is not limited to, displaying the driving status of the vehicle and surrounding vehicles. The display method for the vehicle cutting in line includes, but is not limited to, highlighting it or displaying it in a conspicuous color (e.g., normal vehicles are displayed in green, and vehicles cutting in line are displayed in red). Of course, other marking methods can also be used in different embodiments, and this application does not limit the specific marking method.

[0163] The implementation of light alerts includes illuminating indicator lights, such as left turn, right turn, lane change, and brake lights. It should be noted that this embodiment illustrates four types of light alerts, but the embodiment is not limited to this. In application, different methods can be selected based on different scenarios or needs, and this application does not impose any restrictions. Different control methods can be used depending on the indicator lights being set.

[0164] It is understood that the light alerts in this application can be displayed on a screen or located on the vehicle's dashboard. Different operations can correspond to multiple different lights, or different colors of the same light can be used as indicators. For example, turn signals can display green, blue, orange, yellow, and red respectively. This application does not limit the display color of the turn signals; different turn signals can use different display colors, or different colors of the same indicator light can be used for different operating conditions.

[0165] In another embodiment of this application, the method further includes:

[0166] Based on the vehicle's operating information, it is determined whether the vehicle has performed a manual avoidance maneuver. If it is confirmed that the vehicle has performed a manual avoidance maneuver, the vehicle exits the reminder mode.

[0167] In some other embodiments, the vehicle exits the warning mode after determining that surrounding vehicles have eliminated their cutting-in behavior. For example, when a vehicle that cut in front of the vehicle has completely entered the vehicle's current lane, it is determined that the vehicle has successfully entered the vehicle's current lane, the red marker on the display screen returns to its normal color, the speaker module stops its warning function, and the light warning module stops its warning function.

[0168] Compared to the above method embodiments, such as Figure 5 As shown in the figure, an embodiment of the present invention also provides a vehicle control device, the device comprising:

[0169] The acquisition module 10 for collecting vehicle condition information may include, for example, a camera module 1 and a lidar module 2 for collecting driving environment information.

[0170] The central processing module 3 is used to analyze and process the collected information and control the ADAS system. Specifically, the central processing module 3 is used to determine whether surrounding vehicles are cutting in line; when it is determined that the surrounding vehicles are cutting in line, it is used to determine whether the vehicle is performing a manual avoidance operation; and if it is confirmed that the vehicle is not performing a manual avoidance operation, it is used to control the vehicle to enter the automatic avoidance mode.

[0171] A steering system 7 for controlling the direction of the vehicle, and a braking system 8 for controlling the deceleration of the vehicle; the steering system and the braking system are also used to execute an automatic avoidance mode, which is configured to control the vehicle to automatically decelerate and / or automatically steer based on vehicle condition information.

[0172] In some embodiments, the vehicle control device further includes: a display screen 4 for displaying warning information, a speaker module 5 for voice prompts, and a storage device 6 for storing driving information and environmental information.

[0173] The following is for reference. Figure 6 This application provides a vehicle control device. Figure 6 A schematic diagram of a vehicle control device suitable for implementing embodiments of this application is shown. It includes a processor and a memory interconnected, wherein the memory stores a computer program comprising program instructions, and the processor is configured to invoke the program instructions to execute the method described in any of the preceding embodiments.

[0174] In this embodiment, the processor is a processing device capable of performing logical operations, such as a central processing unit (CPU), field-programmable array (FPGA), digital signal processor (DSP), microcontroller (MCU), application-specific logic circuit (ASIC), graphics processing unit (GPU), or other devices with data processing and / or program execution capabilities. It is readily understood that the processor is typically communicatively connected to memory, where any combination of one or more computer program products is stored. The memory can include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, erasable programmable read-only memory (EPROM), USB memory, flash memory, etc. One or more computer instructions can be stored in the memory, and the processor can execute these computer instructions to implement related analysis functions. Various applications and various data, such as various data used and / or generated by applications, can also be stored in the computer-readable storage medium.

[0175] In the embodiments of this application, each module can be implemented by the processor executing relevant computer instructions. For example, the image processing module can be implemented by the processor executing instructions for image transformation algorithms, the machine learning module can be implemented by the processor executing instructions for machine learning algorithms, and the neural network can be implemented by the processor executing instructions for neural network algorithms.

[0176] In the embodiments of this application, each module can run on the same processor or on multiple processors; each module can run on a processor of the same architecture, such as all running on an 886-based processor, or it can run on processors of different architectures, such as the image processing module running on an x86-based CPU and the machine learning module running on a GPU. Each module can be packaged in a computer product, such as each module being packaged in a computer software and running on a computer (server), or each module or part of it can be packaged in different computer products, such as the image processing module being packaged in a computer software and running on a computer (server), and the machine learning modules being packaged in separate computer software and running on one or more computers (servers); the computing platform for each module to execute can be local computing, cloud computing, or a hybrid computing consisting of local computing and cloud computing.

[0177] like Figure 6 As shown, the computer system includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 602 or programs loaded from storage section 608 into random access memory (RAM) 603. RAM 603 also stores various programs and data required for the system's operating instructions. CPU 601, ROM 602, and RAM 603 are interconnected via bus 604. Input / output (I / O) interface 605 is also connected to bus 604.

[0178] The following components are connected to I / O interface 605: an input section 606 including a keyboard, mouse, etc.; an output section 607 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to I / O interface 605 as needed. A removable medium 611, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 610 as needed so that computer programs read from it can be installed into storage section 608 as needed.

[0179] Specifically, according to embodiments of this application, the flowchart above refers to... Figure 2The described process can be implemented as a computer software program. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowchart. In such an embodiment, the computer program contains program code for performing the methods shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via communication section 609, and / or installed from removable medium 611. When the computer program is executed by central processing module (CPU) 601, it performs the functions defined in the system of this application.

[0180] This application also provides a vehicle, including the aforementioned vehicle control device or the aforementioned vehicle control equipment.

[0181] This application also provides a computer-readable storage medium storing a computer program that is executed by a processor to implement the methods described in any of the above claims.

[0182] It should be noted that the computer-readable medium shown in this application may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0183] In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wireline, optical fiber, RF, etc., or any suitable combination thereof.

[0184] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operational instructions of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two connected blocks may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved.

[0185] It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified functions or operational instructions, or using a combination of dedicated hardware and computer instructions. The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed.

[0186] Those skilled in the art should understand that the scope of disclosure in this application is not limited to technical solutions formed by specific combinations of the above-mentioned technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-mentioned technical features or their equivalent features without departing from the aforementioned disclosed concept. For example, technical solutions formed by substituting the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A vehicle control method characterized by, The method comprises: determining whether the surrounding vehicle has a cut-in behavior; when it is determined that the surrounding vehicle has a cut-in behavior, controlling the ego vehicle to enter an automatic avoidance mode, the automatic avoidance mode being configured to control the ego vehicle to automatically decelerate and / or automatically steer based on vehicle state information; when it is determined that the surrounding vehicle has a cut-in behavior and the ego vehicle has a manual avoidance operation, the method further comprises: obtaining running information of the ego vehicle, and determining whether the running information meets an intervention condition, the intervention condition being configured such that the running information exceeds a safety threshold; if it is determined that the running information meets the intervention condition, controlling the ego vehicle to perform an automatic intervention operation; and if it is determined that the running information does not meet the intervention condition, controlling the ego vehicle to perform a manual avoidance based on the manual avoidance operation; wherein the determination of whether the running information meets the intervention condition comprises: based on the vehicle speed of the ego vehicle, constructing safety factors corresponding to different manual avoidance operations of the ego vehicle at different vehicle speeds, the manual avoidance operation being at least one of a steering operation and a deceleration operation; based on the current running information of the ego vehicle, obtaining a current safety factor corresponding to the current running information; determining whether the current safety factor exceeds the safety threshold; if it is determined that the current safety factor exceeds the safety threshold, determining that the running information meets the intervention condition.

2. The method of claim 1, wherein, The determination of whether the surrounding vehicle has a cut-in behavior comprises: obtaining vehicle state information of the ego vehicle and the surrounding vehicle, the vehicle state information comprising positioning information; based on the vehicle state information, determining whether a risk condition is met; when it is determined that the vehicle state information meets the risk condition, determining that the surrounding vehicle has a cut-in behavior.

3. The method of claim 2, wherein, The vehicle state information comprises current lane information, and the determination of whether the risk condition is met based on the vehicle state information comprises: based on the current lane of the ego vehicle and the current lane of the surrounding vehicle, determining whether the surrounding vehicle and the ego vehicle are in the same lane; when it is determined that the surrounding vehicle and the ego vehicle are in the same lane, determining whether a longitudinal distance between the surrounding vehicle and the ego vehicle is less than a longitudinal distance threshold; and if the longitudinal distance is less than the longitudinal distance threshold, determining that the vehicle state information meets the risk condition.

4. The method of claim 3, wherein, The obtaining of the vehicle state information of the ego vehicle and the surrounding vehicle comprises: constructing lane environment information based on collected environmental image information; obtaining positioning information of the ego vehicle and the surrounding vehicle based on the lane environment information; obtaining a current lane of the ego vehicle and a current lane of the surrounding vehicle based on the positioning information.

5. The method of claim 4, wherein, The construction of the lane environment information based on the collected environmental image information comprises: obtaining lane lines based on the collected environmental image information, and constructing the lane environment information based on the lane lines; or when it is determined that no lane line is obtained based on the collected environmental image information, constructing a virtual lane line of a preset width centered on the ego vehicle, and constructing the lane environment information based on the virtual lane line.

6. The method of claim 5, wherein, The determination of whether the surrounding vehicle and the ego vehicle are in the same lane comprises: obtaining lane lines of the current lane of the ego vehicle and a lateral distance between the ego vehicle and the surrounding vehicle; determining whether a distance between the surrounding vehicle and a lane line of a current lane of the ego vehicle is less than a lane threshold based on the lateral distance; if yes, determining that the surrounding vehicle and the ego vehicle are in the same lane.

7. The method of claim 3, wherein, determining whether a longitudinal distance between a surrounding vehicle and an ego vehicle is less than a longitudinal distance threshold, the method comprising: collecting vehicle ranging images and radar ranging information; obtaining the longitudinal distance between the surrounding vehicle and the ego vehicle based on the vehicle ranging images and the radar ranging information; determining whether the longitudinal distance is less than the longitudinal distance threshold based on the longitudinal distance.

8. The method of claim 7, wherein, correcting the longitudinal distance between the surrounding vehicle and the ego vehicle based on the vehicle ranging images and the radar ranging information, the method comprising: obtaining camera ranging information based on the vehicle ranging images; obtaining a first distance correction coefficient corresponding to the camera ranging information and a second distance correction coefficient corresponding to the radar ranging information; correcting the longitudinal distance between the surrounding vehicle and the ego vehicle based on the first distance correction coefficient and the second distance correction coefficient, the longitudinal distance = camera ranging information * first distance correction coefficient + radar ranging information * second distance correction coefficient.

9. The method of claim 2, wherein, controlling the ego vehicle to enter an automatic avoidance mode, the method comprising: determining whether a longitudinal distance between the ego vehicle and a vehicle in a neighboring lane is within a lane-changing threshold range based on the vehicle condition information; if yes, controlling the ego vehicle to perform a lane-changing operation; if no, controlling the ego vehicle to reduce a vehicle speed to pull away from the vehicle cutting in.

10. The method of claim 1, wherein, the method further comprising: determining whether the ego vehicle has a manual avoidance operation when it is determined that the surrounding vehicle has a cutting-in behavior; if it is confirmed that the ego vehicle does not have a manual avoidance operation, controlling the ego vehicle to enter an automatic avoidance operation.

11. The method of claim 10, wherein, the manual avoidance operation comprises a first manual avoidance operation, the method comprising: if it is confirmed that the ego vehicle has a first manual avoidance operation, controlling the ego vehicle to perform a first manual avoidance based on the first manual avoidance operation, the first manual avoidance operation being at least one of a steering operation and a deceleration operation; if it is confirmed that the ego vehicle does not have a first manual avoidance operation, controlling the ego vehicle to enter an automatic avoidance mode.

12. The method of claim 11, wherein, if it is confirmed that the ego vehicle does not have a first manual avoidance operation, after the ego vehicle enters the automatic avoidance mode, the method further comprising: continuing to determine whether the ego vehicle has a second manual avoidance operation, the second manual avoidance operation being at least one of a steering operation and a deceleration operation; in response to the ego vehicle having the second manual avoidance operation, the ego vehicle exits the automatic avoidance mode and controls the ego vehicle to perform a second manual avoidance based on the second manual avoidance operation.

13. The method of claim 10, wherein, determining whether the ego vehicle has a manual avoidance operation, the method comprising: obtaining running information of the ego vehicle, the running information comprising at least one of steering wheel operation information and brake operation information; in response to a change in the running information of the ego vehicle, it is confirmed that the ego vehicle has a manual avoidance operation.

14. The method of claim 13, wherein, the automatic intervention operation is configured to perform at least one of increasing a steering wheel steering resistance, adjusting a steering angle, adjusting an acceleration, and adjusting a speed.

15. The method of claim 1, wherein, if it is confirmed that the ego vehicle does not have a manual avoidance operation, when the ego vehicle enters the automatic avoidance mode, the method further comprising: Obtaining vehicle condition information of the vehicle and surrounding vehicles, the vehicle condition information including vehicle speed, positioning information; Determining whether the vehicle will collide with the cut-in vehicle based on the vehicle condition information; If it is determined that the vehicle will not collide with the cut-in vehicle, controlling the vehicle to exit the automatic avoidance mode.

16. The method of claim 1, wherein, The method further includes: when it is determined that the surrounding vehicle has the cut-in behavior, entering a reminding mode, the reminding mode being configured to perform at least one of a warning prompt sound and a light reminder.

17. A vehicle control device characterized by comprising: The vehicle control method according to any one of claims 1-16, comprising: A collection module for collecting vehicle condition information; A central processing module for determining whether the surrounding vehicle has the cut-in behavior, and for controlling the vehicle to enter the automatic avoidance mode when it is determined that the surrounding vehicle has the cut-in behavior; A steering system and a braking system for performing the automatic avoidance mode, the automatic avoidance mode being configured to control the vehicle to automatically decelerate and / or automatically steer based on the vehicle condition information.

18. A vehicle control apparatus comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the program to implement the method according to any one of claims 1-16.

19. A vehicle comprising the vehicle control device according to claim 17 or the vehicle control apparatus according to claim 18.

20. A computer readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the method according to any one of claims 1-16. The program is executed by the processor to implement the method according to any one of claims 1-16.

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