A control method and device of an autonomous vehicle, a medium and an electronic device

Abstract

The application discloses a control method and device of an automatic driving vehicle, a medium and an electronic device. The control method comprises the following steps: acquiring a state parameter of a current vehicle and a road condition parameter of a current driving road where the current vehicle is located; calculating a road surface adhesion coefficient according to the state parameter of the current vehicle and the road condition parameter of the current driving road; and determining a driving strategy of the current vehicle in combination with an operation parameter of other vehicles on the current driving road, a vehicle distance between the current vehicle and the other vehicles and the like, so as to take the road surface adhesion coefficient as an automatic driving decision control variable, make the automatic driving control more accurate and safer, improve the automatic driving following safety, and improve the operation efficiency of the automatic driving vehicle under the premise of ensuring safety.

Description

Technical Field

[0001] This application relates to the field of autonomous driving technology, specifically to a control method, device, medium, and electronic equipment for an autonomous vehicle. Background Technology

[0002] Autonomous vehicles, also known as driverless cars, computer-driven cars, or wheeled mobile robots, are intelligent vehicles that achieve driverless operation through computer systems. In autonomous driving technology, active safety is the foundation for realizing autonomous driving technology.

[0003] Current autonomous driving technologies primarily consider the vehicle's handling performance and information such as the position and speed of third-party vehicles obtained through perception during following and active braking. However, in environments with poor road conditions (low coefficient of friction), relying solely on this information can lead to serious driving safety issues during automatic following and lane changes. Therefore, a method to improve the safety control of autonomous vehicles in various environments is needed. Summary of the Invention

[0004] To address the aforementioned technical problems, this application is proposed. Embodiments of this application provide a control method, apparatus, medium, and electronic device for an autonomous vehicle.

[0005] According to one aspect of this application, a control method for an autonomous vehicle is provided, comprising: acquiring current vehicle state parameters and road condition parameters of the current driving road where the current vehicle is located; calculating a vehicle adhesion coefficient of the current driving road based on the current vehicle state parameters and the road condition parameters of the current driving road; acquiring nearby environmental parameters of the current vehicle; wherein the nearby environmental parameters include operating parameters of other vehicles on the current driving road and the distance between the other vehicles and the current vehicle; and determining a driving strategy for the current vehicle based on the nearby environmental parameters, the operating parameters of the current vehicle, and the vehicle adhesion coefficient of the current driving road.

[0006] In one embodiment, the current vehicle state parameters include the tire lateral stiffness of the current vehicle, and the road condition parameters of the current driving road include a road surface image of the current driving road; wherein, calculating the vehicle adhesion coefficient of the current driving road based on the current vehicle state parameters and the road condition parameters of the current driving road includes: inputting the current vehicle tire lateral stiffness and the road surface image of the current driving road into a vehicle adhesion coefficient calculation model to obtain the vehicle adhesion coefficient of the current driving road.

[0007] In one embodiment, determining the driving strategy of the current vehicle based on the nearby environmental parameters, the operating parameters of the current vehicle, and the vehicle adhesion coefficient of the current driving road includes: calculating the braking distance of the current vehicle based on the operating parameters of the current vehicle and the vehicle adhesion coefficient of the current driving road; calculating the inter-vehicle distance based on the distance between the current vehicle and the vehicle in front of the current vehicle and the operating speed of the current vehicle; if the distance between the current vehicle and the vehicle in front of the current vehicle is less than the braking distance of the current vehicle, or the inter-vehicle distance is less than a preset time threshold, then reducing the operating speed of the current vehicle to increase the distance between the current vehicle and the vehicle in front of the current vehicle.

[0008] In one embodiment, determining the driving strategy of the current vehicle based on the nearby environmental parameters of the current vehicle, the operating parameters of the current vehicle, and the vehicle adhesion coefficient of the current driving road includes: if there is an obstacle in front of the current vehicle, calculating the braking distance of the current vehicle based on the operating parameters of the current vehicle and the vehicle adhesion coefficient of the current driving road; if the braking distance of the current vehicle is less than the distance between the current vehicle and the obstacle, controlling the current vehicle to brake to avoid colliding with the obstacle.

[0009] In one embodiment, determining the driving strategy of the current vehicle based on the nearby environmental parameters of the current vehicle, the operating parameters of the current vehicle, and the vehicle adhesion coefficient of the current driving road includes: calculating the lateral force generated by the current vehicle changing lanes based on the operating parameters of the current vehicle and the road condition parameters of the current driving road; calculating the road surface adhesion force of the current vehicle based on the state parameters of the current vehicle and the vehicle adhesion coefficient of the current driving road; and controlling the current vehicle to change lanes to avoid the obstacle if the braking distance of the current vehicle is greater than or equal to the distance between the current vehicle and the obstacle, and the lateral force generated by the current vehicle changing lanes is less than the road surface adhesion force of the current vehicle.

[0010] In one embodiment, determining the driving strategy of the current vehicle based on the nearby environmental parameters of the current vehicle, the operating parameters of the current vehicle, and the vehicle adhesion coefficient of the current driving road includes: if the braking distance of the current vehicle is greater than or equal to the distance between the current vehicle and the obstacle, and the lateral force generated by the current vehicle changing lanes is greater than or equal to the road surface adhesion of the current vehicle, then the current vehicle is controlled to maintain its lane and the operating speed of the current vehicle is reduced.

[0011] In one embodiment, determining the driving strategy of the current vehicle based on the nearby environmental parameters, the operating parameters of the current vehicle, and the vehicle adhesion coefficient of the current driving road includes: calculating the dangerous braking distance of the following vehicle based on the operating parameters of the vehicle behind the current vehicle and the operating parameters of the current vehicle; wherein, the dangerous braking distance represents the shortest braking distance for the following vehicle to avoid colliding with the current vehicle when it uses maximum braking force; if the distance between the following vehicle and the current vehicle is less than the dangerous braking distance, then the distance between the vehicle in front of the current vehicle and the current vehicle is obtained; if the distance between the vehicle in front of the current vehicle and the current vehicle is greater than a preset distance threshold, then the operating speed of the current vehicle is increased to increase the distance between the current vehicle and the following vehicle.

[0012] In one embodiment, determining the driving strategy of the current vehicle based on the nearby environmental parameters, the operating parameters of the current vehicle, and the vehicle adhesion coefficient of the current driving road includes: if the distance between the current vehicle and the vehicle in front of the current vehicle is less than or equal to the distance threshold, then calculating the lateral force generated by the current vehicle changing lanes based on the operating parameters of the current vehicle and the road condition parameters of the current driving road; calculating the road surface adhesion force of the current vehicle based on the state parameters of the current vehicle and the vehicle adhesion coefficient of the current driving road; if the lateral force generated by the current vehicle changing lanes is less than the road surface adhesion force of the current vehicle, then controlling the current vehicle to change lanes to avoid the vehicle behind.

[0013] According to another aspect of this application, a control device for an autonomous vehicle is provided, comprising: a vehicle parameter acquisition module for acquiring current vehicle state parameters and road condition parameters of the current driving road where the current vehicle is located; an adhesion coefficient calculation module for calculating a vehicle adhesion coefficient of the current driving road based on the current vehicle state parameters and the road condition parameters of the current driving road; an environmental parameter acquisition module for acquiring nearby environmental parameters of the current vehicle; wherein the nearby environmental parameters include operating parameters of other vehicles on the current driving road and the distance between the other vehicles and the current vehicle; and a driving strategy determination module for determining a driving strategy of the current vehicle based on the nearby environmental parameters of the current vehicle, the operating parameters of the current vehicle, and the vehicle adhesion coefficient of the current driving road.

[0014] According to another aspect of this application, a computer-readable storage medium is provided, the storage medium storing a computer program for performing the control method of any of the above-described autonomous vehicles.

[0015] According to another aspect of this application, an electronic device is provided, comprising: a processor; a memory for storing processor-executable instructions; the processor being configured to execute any of the above-described control methods for an autonomous vehicle.

[0016] This application provides a control method, device, medium, and electronic device for an autonomous vehicle. The method involves acquiring the current vehicle's state parameters and the road condition parameters of the current driving road; calculating the vehicle adhesion coefficient based on these parameters; acquiring the vehicle's proximity environment parameters, including the operating parameters of other vehicles on the current driving road and the distance between the vehicle and other vehicles; and determining the vehicle's driving strategy based on these parameters. Specifically, the method calculates the road adhesion coefficient based on the vehicle's own state parameters and the road condition parameters, and combines this with information such as the operating parameters of other vehicles on the current driving road and the distance between the vehicle and other vehicles to determine the driving strategy. By using the road adhesion coefficient as a control variable for autonomous driving decision-making, the method achieves more precise and safer autonomous driving control, improves the safety of following other vehicles, and enhances the operational efficiency of the autonomous vehicle while ensuring safety. Attached Figure Description

[0017] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0018] Figure 1 This is a flowchart illustrating an exemplary embodiment of the control method for an autonomous vehicle provided in this application.

[0019] Figure 2 This is a flowchart illustrating a following control method for an autonomous vehicle provided in an exemplary embodiment of this application.

[0020] Figure 3 This is a flowchart illustrating an exemplary embodiment of the control method for an autonomous vehicle to avoid rear-end collisions provided in this application.

[0021] Figure 4 This is a flowchart illustrating an exemplary embodiment of the control method for an autonomous vehicle to avoid being rear-ended, provided in this application.

[0022] Figure 5This is a schematic diagram of the structure of a control device for an autonomous vehicle provided in an exemplary embodiment of this application.

[0023] Figure 6 This is a structural diagram of an electronic device provided in an exemplary embodiment of this application. Detailed Implementation

[0024] Hereinafter, exemplary embodiments according to this application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments of this application. It should be understood that this application is not limited to the exemplary embodiments described herein.

[0025] Figure 1 This is a flowchart illustrating an exemplary embodiment of the control method for an autonomous vehicle provided in this application. Figure 1 As shown, the control method for this autonomous vehicle includes the following steps:

[0026] Step 110: Obtain the current vehicle status parameters and the road condition parameters of the current road where the vehicle is currently traveling.

[0027] This application collects real-time status parameters of the current vehicle through sensors installed on the vehicle, including tire wear parameters, braking performance parameters, etc.; and collects real-time road condition parameters of the current road surface (dry, snow, slippery, etc.) through image acquisition devices such as cameras installed on the vehicle.

[0028] Step 120: Calculate the vehicle adhesion coefficient for the current driving road based on the current vehicle state parameters and the road condition parameters of the current driving road.

[0029] After obtaining the current vehicle's state parameters and the road condition parameters of the current driving road, this application calculates the vehicle adhesion coefficient of the current driving road based on the current vehicle's state parameters and the road condition parameters of the current driving road, so as to determine the current vehicle's grip state.

[0030] Step 130: Obtain the surrounding environmental parameters of the current vehicle.

[0031] The nearby environmental parameters include the operating parameters of other vehicles on the current driving road and the distance between other vehicles and the current vehicle. This application provides a nearby environmental reference for the autonomous driving control of the current vehicle by obtaining the operating parameters of other vehicles near the current vehicle and relative information such as the distance between other vehicles and the current vehicle, thereby ensuring the driving safety of the current vehicle.

[0032] Step 140: Determine the driving strategy of the current vehicle based on the current vehicle's nearby environmental parameters, the current vehicle's operating parameters, and the vehicle adhesion coefficient of the current driving road.

[0033] This application, based on the comprehensive consideration of nearby environmental parameters and the current vehicle operating parameters, further considers the vehicle adhesion coefficient of the current driving road to ensure the safety of the current vehicle during braking or lane changing, thereby reducing the risk of vehicle drift caused by excessive braking force or excessive lane changing speed, and thus further improving the safety of autonomous driving.

[0034] This application provides a control method for autonomous vehicles, which involves acquiring the current vehicle's state parameters and the road condition parameters of the current driving road; calculating the vehicle adhesion coefficient of the current driving road based on the current vehicle's state parameters and the road condition parameters; acquiring the vehicle's proximity environment parameters, including the operating parameters of other vehicles on the current driving road and the distance between other vehicles and the current vehicle; and determining the current vehicle's driving strategy based on the proximity environment parameters, the current vehicle's operating parameters, and the vehicle adhesion coefficient of the current driving road. Specifically, the method calculates the road adhesion coefficient based on the current vehicle's own state parameters and the road condition parameters of the current driving road, and combines this with information such as the operating parameters of other vehicles on the current driving road and the distance between the current vehicle and other vehicles to determine the current vehicle's driving strategy. By using the road adhesion coefficient as an autonomous driving decision control variable, the method makes autonomous driving control more precise and safer, improves the safety of autonomous driving following other vehicles, and increases the operating efficiency of autonomous vehicles while ensuring safety.

[0035] In one embodiment, the current vehicle state parameters include the current vehicle tire lateral stiffness, and the current road condition parameters include the road surface image of the current road. Specifically, step 120 can be implemented by inputting the current vehicle tire lateral stiffness and the road surface image of the current road into the vehicle adhesion coefficient calculation model to obtain the vehicle adhesion coefficient of the current road.

[0036] Specifically, this application constructs a vehicle adhesion coefficient calculation model and actually collects photos of different road surfaces such as dry, snowy, and wet surfaces. The model is trained by combining the known tire lateral stiffness of the vehicle with the input of the vehicle adhesion coefficient calculation model. After training, the tire lateral stiffness of the current vehicle and the road surface image of the current driving road are acquired in real time using the sensors and cameras on the current vehicle. The tire lateral stiffness of the current vehicle and the road surface image of the current driving road are then input into the vehicle adhesion coefficient calculation model to generate the vehicle adhesion coefficient of the current driving road, providing basic parameters for subsequent adhesion force calculation.

[0037] In one embodiment, step 140 can be implemented as follows: based on the current vehicle's operating parameters and the vehicle adhesion coefficient of the current road, calculate the braking distance of the current vehicle; based on the distance between the current vehicle and the vehicle in front of the current vehicle and the current vehicle's operating speed, calculate the vehicle's inter-vehicle time distance; if the distance between the current vehicle and the vehicle in front of the current vehicle is less than the braking distance of the current vehicle, or the vehicle's inter-vehicle time distance is less than a preset time threshold, then reduce the current vehicle's operating speed to increase the distance between the current vehicle and the vehicle in front of the current vehicle.

[0038] This application obtains multiple variables affecting the longitudinal braking distance and lateral lane change displacement of an autonomous vehicle through experiments, along with their corresponding longitudinal braking distances or lateral lane change displacements. Based on these variables and their corresponding longitudinal braking distances or lateral lane change displacements, the application calculates the correspondence between the longitudinal braking distance / lateral lane change displacement and each variable. This allows the autonomous vehicle to determine its longitudinal braking distance and lateral lane change displacement during autonomous driving based on this correspondence. Specifically, the variables may include vehicle speed, tire wear, braking performance, vehicle adhesion coefficient, road surface condition, wind speed, and gradient. This application uses a weighted average to calculate the vehicle's longitudinal braking distance and lateral lane change displacement based on these variables, as shown in the following formula:

[0039] F(X) = a1X1 + a2X2 + a3X3 + ... + a n X n +b;

[0040] Where F(X) is the longitudinal braking distance or lateral lane change displacement, X1~X n Each represents a variable factor, a1 to a2. n They are X1 to X n The weighting coefficients, where b is a constant value. This application calculates the aforementioned coefficients a1 to a2 based on the values ​​of multiple sets of actual collected variables and their corresponding longitudinal braking distances or lateral lane change displacements. n And b. Preferably, this application can also utilize cloud-based big data to correct the above coefficients in real time, thereby improving the prediction accuracy of the vehicle's longitudinal braking distance and lateral lane change displacement. This application calculates the current vehicle's braking distance in real time according to this formula during actual driving.

[0041] like Figure 2As shown, this application senses the operating parameters and environmental information (road condition parameters and adjacent environment parameters) of the vehicle through sensors, and adjusts the distance between the current vehicle and the vehicle ahead according to the road surface condition. Specifically, this application calculates the time headway of the current vehicle based on the distance between the vehicle ahead of the current vehicle and the current vehicle and the operating speed of the current vehicle. Specifically, the distance A between the vehicle ahead and the current vehicle = |x 当 -x 前 |, where x 当 represents the position of the current vehicle, and x 前 represents the position of the vehicle ahead; the time headway B of the current vehicle = A / v, where v represents the operating speed of the current vehicle.

[0042] If the distance between the vehicle ahead of the current vehicle and the current vehicle is less than the braking distance of the current vehicle, or the time headway of the current vehicle is less than the preset time threshold, for example, if A < F(X), or B < 2 seconds, it means that the current driving road is a slippery road surface and the distance between the vehicle ahead of the current vehicle and the current vehicle is short. To avoid safety accidents such as collisions, this application reduces the operating speed of the current vehicle to increase the distance between the current vehicle and the vehicle ahead, thereby improving safety.

[0043] If the distance between the vehicle ahead of the current vehicle and the current vehicle is greater than or equal to the braking distance of the current vehicle and the time headway of the current vehicle is greater than or equal to the preset time threshold, it means that the current driving road is a dry road surface and the distance between the vehicle ahead of the current vehicle and the current vehicle is long. At this time, this application reduces the distance between the current vehicle and the vehicle ahead to improve the operating efficiency of the current vehicle.

[0044] Preferably, this application can set a redundancy range value a (for example, 5m - 20 meters). If A ≥ F(X) + a, or B ≥ 2 seconds, the operating speed of the current vehicle is increased to avoid frequent acceleration or deceleration of the current vehicle during driving caused by single-point limits, thereby improving the driving experience.

[0045] During the normal following driving process of the current vehicle in this application, the operating parameters during driving are collected in real time through sensors such as cameras and radars on the current vehicle. The distance between the vehicle ahead and the current vehicle and the operating speed of the vehicle ahead are obtained through the radar, and the driving road surface environment is identified through the camera. And the information obtained is transmitted to the processor in real time for processing and then transmitted to the controller. This is used as the decision-making information of the controller. After receiving the processed perception information, the controller dynamically adjusts the following distance between the two vehicles during the following process according to the distance and time headway between the vehicle ahead and the current vehicle, and dynamically adjusts the following distance between the two vehicles during the following process to achieve the purpose of safe following.

[0046] In one embodiment, step 140 can be implemented as follows: if there is an obstacle in front of the current vehicle, the braking distance of the current vehicle is calculated based on the current vehicle's operating parameters and the vehicle adhesion coefficient of the current road; if the braking distance of the current vehicle is less than the distance between the current vehicle and the obstacle, the current vehicle is controlled to brake to avoid colliding with the obstacle.

[0047] Specifically, such as Figure 3 As shown, this application uses vehicle sensors to perceive the vehicle's operating parameters and road conditions in real time. When an obstacle is detected in front of the vehicle (which can be a stationary obstacle or a vehicle braking suddenly), the control system actively intervenes to avoid a rear-end collision. First, the braking distance of the vehicle is calculated, and based on this distance and the distance between the vehicle and the obstacle, it is determined whether a rear-end collision will occur when the vehicle applies maximum braking force (i.e., whether the braking distance is less than the distance between the vehicle and the obstacle). If the braking distance is less than the distance (i.e., no rear-end collision will occur when the vehicle applies maximum braking force), the vehicle is controlled to brake longitudinally until there is no risk of a rear-end collision or the vehicle comes to a stop.

[0048] In one embodiment, step 140 can be implemented as follows: based on the current vehicle's operating parameters and the road condition parameters of the current driving road, calculate the lateral force generated by the current vehicle changing lanes; based on the current vehicle's state parameters and the vehicle adhesion coefficient of the current driving road, calculate the current vehicle's road surface adhesion force; if the current vehicle's braking distance is greater than or equal to the distance between the current vehicle and the obstacle, and the lateral force generated by the current vehicle changing lanes is less than the current vehicle's road surface adhesion force, then control the current vehicle to change lanes to avoid the obstacle.

[0049] Specifically, such as Figure 3 As shown, this application calculates the lateral force generated by the current vehicle changing lanes based on the current vehicle's operating parameters and the road condition parameters of the current driving road. Based on the current vehicle's state parameters and the vehicle adhesion coefficient of the current driving road, it calculates the current vehicle's road adhesion force. By determining whether the current vehicle will skid when changing lanes (i.e., whether the lateral force generated by the current vehicle changing lanes is less than the current vehicle's road adhesion force), if the lateral force generated by the current vehicle changing lanes is less than the current vehicle's road adhesion force (i.e., the current vehicle will not skid when changing lanes), the current vehicle is controlled to change lanes to avoid the obstacle, thereby avoiding a rear-end collision and ensuring the normal driving of the current vehicle. If the lateral force generated by the current vehicle changing lanes is greater than or equal to the current vehicle's road adhesion force (i.e., the current vehicle will skid when changing lanes), the current vehicle is controlled to maintain its lane (current lane) and decelerate and brake before determining whether the current vehicle will skid when changing lanes again.

[0050] When the vehicle in this application senses an obstacle ahead, it estimates the braking distance based on the vehicle's operating parameters and the road conditions of the current road. If the braking distance is less than the distance between the vehicle and the obstacle, it selects longitudinal active braking until there is no risk of a rear-end collision. If the braking distance is less than the distance between the vehicle and the obstacle, it adopts an alternative avoidance strategy—lane change avoidance—provided that the distance between vehicles in adjacent lanes meets the requirements. Before executing the lane change avoidance, it calculates whether the lateral force generated by the current lane change is lower than the adhesion provided by the road. If there is a risk of skidding, it first decelerates to a safe speed before performing the lane change avoidance; if there is no risk of skidding at the current speed, it selects to directly change lanes to avoid the obstacle.

[0051] In one embodiment, step 140 can be implemented as follows: based on the operating parameters of the vehicles behind the current vehicle and the operating parameters of the current vehicle, calculate the dangerous braking distance of the vehicles behind; wherein, the dangerous braking distance represents the shortest braking distance that the vehicles behind can use when applying maximum braking force to avoid colliding with the current vehicle; if the distance between the vehicles behind and the current vehicle is less than the dangerous braking distance, then obtain the distance between the vehicles in front of the current vehicle and the current vehicle; if the distance between the vehicles in front of the current vehicle and the current vehicle is greater than a preset distance threshold, then increase the operating speed of the current vehicle to increase the distance between the current vehicle and the vehicles behind.

[0052] Specifically, such as Figure 4 As shown, this application uses vehicle sensors to perceive the current vehicle's operating parameters and the road condition parameters of the current road in real time. Based on the operating parameters of the vehicles behind the current vehicle and the current vehicle's operating parameters, it calculates the dangerous braking distance of the vehicles behind and determines whether the distance between the vehicles behind and the current vehicle is less than the dangerous braking distance (i.e., whether there is a risk of rear-end collision). If the distance between the vehicles behind and the current vehicle is less than the dangerous braking distance (i.e., there is a risk of rear-end collision), it obtains the distance between the vehicles in front of the current vehicle and determines whether the distance between the vehicles in front of the current vehicle and the current vehicle is sufficient (i.e., whether it is greater than a preset distance threshold). If the distance between the vehicles in front of the current vehicle and the current vehicle is greater than the preset distance threshold (e.g., 100 meters), it increases the current vehicle's operating speed to increase the distance between the current vehicle and the vehicles behind.

[0053] The formula for calculating the dangerous braking distance d is as follows:

[0054]

[0055] Where v1 is the speed of the vehicle behind, t s Let f(u) be the system response time of the vehicle behind, f(u) be the braking system performance parameter of the vehicle behind, and v be the system response time of the vehicle behind. a Let a be the speed difference between the vehicle behind and the vehicle in front. max This is the maximum braking deceleration of the vehicle behind.

[0056] Preferably, when the current vehicle accelerates to increase the distance between itself and the vehicle behind it, the collision time must be greater than a preset time (e.g., 2.6 seconds). Specifically, the collision time TTS is calculated as follows:

[0057]

[0058] Where s is the distance between the vehicle behind and the current vehicle, and v2 is the current vehicle's speed.

[0059] In one embodiment, step 140 can be implemented as follows: if the distance between the vehicle in front of the current vehicle and the current vehicle is less than or equal to a distance threshold, then the current vehicle is controlled to change lanes to avoid the vehicle behind.

[0060] Specifically, such as Figure 4 As shown, if the distance between the vehicle in front of the current vehicle and the current vehicle is less than or equal to the distance threshold (i.e., there is a risk of the current vehicle rear-ending the vehicle in front when accelerating), the lateral force generated by the current vehicle changing lanes is calculated based on the current vehicle's operating parameters and the road condition parameters of the current road. The road adhesion force of the current vehicle is calculated based on the current vehicle's state parameters and the vehicle adhesion coefficient of the current road. If the lateral force generated by the current vehicle changing lanes is less than the road adhesion force of the current vehicle, the current vehicle is controlled to change lanes to avoid the vehicle behind. If the lateral force generated by the current vehicle changing lanes is greater than or equal to the road adhesion force of the current vehicle (i.e., the current vehicle will skid when changing lanes), the current vehicle is controlled to maintain its lane and decelerate and brake before reassessing whether the current vehicle will skid when changing lanes.

[0061] When a vehicle is performing a lane change operation (such as avoiding obstacles, avoiding vehicles in front, or avoiding vehicles behind), the vehicle uses sensors to detect whether the adjacent lanes meet the lane change conditions (whether the lateral force generated by the current vehicle's lane change is less than the current vehicle's road surface adhesion, i.e., whether skidding will occur). If the adjacent lanes do not meet the lane change conditions, the vehicle cancels the lane change request, reduces its speed, and continues to determine whether the lane change conditions are met. When the adjacent lanes meet the lane change conditions, the vehicle performs the lane change operation.

[0062] Figure 5 This is a schematic diagram of the structure of a control device for an autonomous vehicle provided in an exemplary embodiment of this application. Figure 5As shown, the control device 50 of the autonomous vehicle includes: a vehicle parameter acquisition module 51, used to acquire the current vehicle's state parameters and the road condition parameters of the current driving road where the vehicle is currently traveling; an adhesion coefficient calculation module 52, used to calculate the vehicle adhesion coefficient of the current driving road based on the current vehicle's state parameters and the road condition parameters of the current driving road; an environmental parameter acquisition module 53, used to acquire the current vehicle's adjacent environmental parameters; wherein, the adjacent environmental parameters include the operating parameters of other vehicles on the current driving road and the distance between other vehicles and the current vehicle; and a driving strategy determination module 54, used to determine the current vehicle's driving strategy based on the current vehicle's adjacent environmental parameters, the current vehicle's operating parameters, and the vehicle adhesion coefficient of the current driving road.

[0063] This application provides a control device for an autonomous vehicle. A vehicle parameter acquisition module 51 acquires the current vehicle's state parameters and the road condition parameters of the current driving road. An adhesion coefficient calculation module 52 calculates the vehicle adhesion coefficient of the current driving road based on the current vehicle's state parameters and the road condition parameters. An environmental parameter acquisition module 53 acquires the vehicle's proximity environmental parameters, including the operating parameters of other vehicles on the current driving road and the distance between the current vehicle and other vehicles. A driving strategy determination module 54 determines the current vehicle's driving strategy based on the proximity environmental parameters, the current vehicle's operating parameters, and the vehicle adhesion coefficient of the current driving road. Specifically, it calculates the road adhesion coefficient based on the vehicle's own state parameters and the road condition parameters of the current driving road, and combines this with information such as the operating parameters of other vehicles on the current driving road and the distance between the current vehicle and other vehicles to determine the current vehicle's driving strategy. By using the road adhesion coefficient as an autonomous driving decision control variable, the autonomous driving control becomes more precise and safer, improving the safety of following other vehicles and increasing the operating efficiency of the autonomous vehicle while ensuring safety.

[0064] In one embodiment, the current vehicle state parameters include the current vehicle tire lateral stiffness, and the current road condition parameters include the road surface image of the current road. The aforementioned adhesion coefficient calculation module 52 can be further configured to input the current vehicle tire lateral stiffness and the road surface image of the current road into the vehicle adhesion coefficient calculation model to obtain the vehicle adhesion coefficient of the current road.

[0065] In one embodiment, the driving strategy determination module 54 can be further configured to: calculate the braking distance of the current vehicle based on the current vehicle's operating parameters and the vehicle adhesion coefficient of the current driving road; calculate the inter-vehicle time distance of the current vehicle based on the distance between the current vehicle and the vehicle in front of the current vehicle and the current vehicle's operating speed; if the distance between the current vehicle and the vehicle in front of the current vehicle is less than the braking distance of the current vehicle, or the inter-vehicle time distance of the current vehicle is less than a preset time threshold, then reduce the operating speed of the current vehicle to increase the distance between the current vehicle and the vehicle in front of the current vehicle.

[0066] In one embodiment, the driving strategy determination module 54 can be further configured to: if there is an obstacle in front of the current vehicle, calculate the braking distance of the current vehicle based on the current vehicle's operating parameters and the vehicle adhesion coefficient of the current driving road; if the braking distance of the current vehicle is less than the distance between the current vehicle and the obstacle, control the current vehicle to brake to avoid colliding with the obstacle.

[0067] In one embodiment, the driving strategy determination module 54 can be further configured to: calculate the lateral force generated by the current vehicle changing lanes based on the current vehicle's operating parameters and the road condition parameters of the current driving road; calculate the road surface adhesion force of the current vehicle based on the current vehicle's state parameters and the vehicle adhesion coefficient of the current driving road; and if the braking distance of the current vehicle is greater than or equal to the distance between the current vehicle and the obstacle, and the lateral force generated by the current vehicle changing lanes is less than the road surface adhesion force of the current vehicle, then control the current vehicle to change lanes to avoid the obstacle.

[0068] In one embodiment, the driving strategy determination module 54 can be further configured to: calculate the dangerous braking distance of the rear vehicle based on the operating parameters of the vehicle behind the current vehicle and the operating parameters of the current vehicle; wherein, the dangerous braking distance represents the shortest braking distance that the rear vehicle can avoid colliding with the current vehicle when it uses maximum braking force; if the distance between the rear vehicle and the current vehicle is less than the dangerous braking distance, then the distance between the vehicle in front of the current vehicle and the current vehicle is obtained; if the distance between the vehicle in front of the current vehicle and the current vehicle is greater than a preset distance threshold, then the operating speed of the current vehicle is increased to increase the distance between the current vehicle and the vehicle behind.

[0069] In one embodiment, the driving strategy determination module 54 can be further configured to: if the distance between the current vehicle and the vehicle in front of the current vehicle is less than or equal to a distance threshold, then control the current vehicle to change lanes to avoid the vehicle behind.

[0070] Below, for reference Figure 6This application describes an electronic device according to embodiments thereof. The electronic device may be either or both of a first device and a second device, or a standalone device independent of them, which may communicate with the first device and the second device to receive acquired input signals from them.

[0071] Figure 6 A block diagram of an electronic device according to an embodiment of this application is illustrated.

[0072] like Figure 6 As shown, the electronic device 10 includes one or more processors 11 and memory 12.

[0073] The processor 11 may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 10 to perform desired functions.

[0074] The memory 12 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 11 may execute the program instructions to implement the methods of the various embodiments of this application described above and / or other desired functions. Various contents such as input signals, signal components, and noise components may also be stored in the computer-readable storage medium.

[0075] In one example, the electronic device 10 may also include an input device 13 and an output device 14, which are interconnected via a bus system and / or other forms of connection mechanism (not shown).

[0076] When the electronic device is a standalone device, the input device 13 can be a communication network connector for receiving the collected input signals from the first device and the second device.

[0077] In addition, the input device 13 may also include, for example, a keyboard, a mouse, etc.

[0078] The output device 14 can output various information to the outside, including determined distance information, direction information, etc. The output device 14 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0079] Of course, for the sake of simplicity, Figure 6Only some of the components of the electronic device 10 relevant to this application are shown in this illustration; components such as buses, input / output interfaces, etc., are omitted. In addition, the electronic device 10 may include any other suitable components depending on the specific application.

[0080] In addition to the methods and apparatus described above, embodiments of this application may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the methods according to various embodiments of this application described in the "Exemplary Methods" section above.

[0081] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this application. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0082] Furthermore, embodiments of this application may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps in the methods according to various embodiments of this application described in the "Exemplary Methods" section above.

[0083] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may, for example, include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0084] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.

[0085] The block diagrams of devices, apparatuses, devices, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0086] It should also be noted that in the apparatus, equipment, and methods of this application, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of this application.

[0087] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0088] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this application to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A control method for an autonomous vehicle, characterized in that, include: Obtain the current vehicle's status parameters and the road condition parameters of the current road where the vehicle is currently traveling; Based on the current vehicle state parameters and the current road condition parameters, the vehicle adhesion coefficient of the current road is calculated. Obtain the nearby environmental parameters of the current vehicle; wherein, the nearby environmental parameters include the operating parameters of other vehicles on the current driving road and the distance between the other vehicles and the current vehicle; The driving strategy of the current vehicle is determined based on the current vehicle's nearby environmental parameters, the current vehicle's operating parameters, and the vehicle adhesion coefficient of the current driving road. The process of determining the driving strategy of the current vehicle based on the nearby environmental parameters, the operating parameters of the current vehicle, and the vehicle adhesion coefficient of the current driving road includes: Based on the operating parameters of the vehicles behind the current vehicle and the operating parameters of the current vehicle, the dangerous braking distance of the vehicles behind is calculated; wherein, the dangerous braking distance represents the shortest braking distance that the vehicles behind can use to avoid colliding with the current vehicle when applying maximum braking force; the dangerous braking distance d The calculation formula is as follows: ； in, The speed of the vehicles behind. For the system response time of vehicles behind, For the braking system performance parameters of the vehicles behind, The speed difference between the vehicle behind and the vehicle in front. The maximum braking deceleration of the vehicle behind; If the distance between the vehicle behind and the current vehicle is less than the dangerous braking distance, then the distance between the vehicle in front of the current vehicle and the current vehicle is obtained. If the distance between the vehicle in front of the current vehicle and the current vehicle is greater than a preset distance threshold, then the operating speed of the current vehicle is increased to increase the distance between the current vehicle and the vehicle behind it.

2. The control method for an autonomous vehicle according to claim 1, characterized in that, The current vehicle's state parameters include the tire lateral stiffness of the current vehicle, and the road condition parameters of the current driving road include a road surface image of the current driving road; wherein, the calculation of the vehicle adhesion coefficient of the current driving road based on the current vehicle's state parameters and the road condition parameters of the current driving road includes: The tire lateral stiffness of the current vehicle and the road surface image of the current driving road are input into the vehicle adhesion coefficient calculation model to obtain the vehicle adhesion coefficient of the current driving road.

3. The control method for an autonomous vehicle according to claim 1 or 2, characterized in that, The process of determining the driving strategy of the current vehicle based on the nearby environmental parameters, the operating parameters of the current vehicle, and the vehicle adhesion coefficient of the current driving road includes: The braking distance of the current vehicle is calculated based on the current vehicle's operating parameters and the vehicle adhesion coefficient of the current road. The inter-vehicle time distance of the current vehicle is calculated based on the distance between the vehicle in front of the current vehicle and the current vehicle's operating speed. If the distance between the current vehicle and the vehicle in front of the current vehicle is less than the braking distance of the current vehicle, or the time difference between the current vehicle and the vehicle in front is less than a preset time threshold, then the operating speed of the current vehicle is reduced to increase the distance between the current vehicle and the vehicle in front.

4. The control method for an autonomous vehicle according to claim 1 or 2, characterized in that, The process of determining the driving strategy of the current vehicle based on the nearby environmental parameters, the operating parameters of the current vehicle, and the vehicle adhesion coefficient of the current driving road includes: If there is an obstacle in front of the current vehicle, the braking distance of the current vehicle is calculated based on the operating parameters of the current vehicle and the vehicle adhesion coefficient of the current driving road. If the braking distance of the current vehicle is less than the distance between the current vehicle and the obstacle, then the current vehicle is controlled to brake in order to avoid colliding with the obstacle.

5. The control method for an autonomous vehicle according to claim 4, characterized in that, The process of determining the driving strategy of the current vehicle based on the nearby environmental parameters, the operating parameters of the current vehicle, and the vehicle adhesion coefficient of the current driving road includes: Based on the current vehicle's operating parameters and the road condition parameters of the current road, calculate the lateral force generated by the current vehicle's lane change; Based on the current vehicle's state parameters and the vehicle adhesion coefficient of the current driving road, calculate the current vehicle's road adhesion force. If the braking distance of the current vehicle is greater than or equal to the distance between the current vehicle and the obstacle, and the lateral force generated by the current vehicle changing lanes is less than the road surface adhesion of the current vehicle, then the current vehicle is controlled to change lanes to avoid the obstacle.

6. The control method for an autonomous vehicle according to claim 5, characterized in that, The process of determining the driving strategy of the current vehicle based on the nearby environmental parameters, the operating parameters of the current vehicle, and the vehicle adhesion coefficient of the current driving road includes: If the braking distance of the current vehicle is greater than or equal to the distance between the current vehicle and the obstacle, and the lateral force generated by the current vehicle changing lanes is greater than or equal to the road surface adhesion force of the current vehicle, then the current vehicle is controlled to maintain its lane and the operating speed of the current vehicle is reduced.

7. The control method for an autonomous vehicle according to claim 1, characterized in that, The process of determining the driving strategy of the current vehicle based on the nearby environmental parameters, the operating parameters of the current vehicle, and the vehicle adhesion coefficient of the current driving road includes: If the distance between the vehicle in front of the current vehicle and the current vehicle is less than or equal to the distance threshold, then the lateral force generated by the current vehicle changing lanes is calculated based on the current vehicle's operating parameters and the road condition parameters of the current road. Based on the current vehicle's state parameters and the vehicle adhesion coefficient of the current driving road, calculate the current vehicle's road adhesion force. If the lateral force generated by the current vehicle changing lanes is less than the road surface adhesion of the current vehicle, then the current vehicle is controlled to change lanes to avoid the vehicle behind.

8. A control device for an autonomous vehicle, characterized in that, include: The vehicle parameter acquisition module is used to acquire the current vehicle's status parameters and the road condition parameters of the current road where the vehicle is currently traveling. The adhesion coefficient calculation module is used to calculate the vehicle adhesion coefficient of the current driving road based on the current vehicle state parameters and the road condition parameters of the current driving road. An environmental parameter acquisition module is used to acquire the nearby environmental parameters of the current vehicle; wherein, the nearby environmental parameters include the operating parameters of other vehicles on the current driving road and the distance between the other vehicles and the current vehicle; The driving strategy determination module is used to determine the driving strategy of the current vehicle based on the nearby environmental parameters of the current vehicle, the operating parameters of the current vehicle, and the vehicle adhesion coefficient of the current driving road. The driving strategy determination module is configured as follows: Based on the operating parameters of the vehicles behind the current vehicle and the operating parameters of the current vehicle, the dangerous braking distance of the vehicles behind is calculated; wherein, the dangerous braking distance represents the shortest braking distance that the vehicles behind can use to avoid colliding with the current vehicle when applying maximum braking force; the dangerous braking distance d The calculation formula is as follows: ； in, The speed of the vehicles behind. For the system response time of vehicles behind, For the braking system performance parameters of the vehicles behind, The speed difference between the vehicle behind and the vehicle in front. The maximum braking deceleration of the vehicle behind; If the distance between the vehicle behind and the current vehicle is less than the dangerous braking distance, then the distance between the vehicle in front of the current vehicle and the current vehicle is obtained. If the distance between the vehicle in front of the current vehicle and the current vehicle is greater than a preset distance threshold, then the operating speed of the current vehicle is increased to increase the distance between the current vehicle and the vehicle behind it.

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

10. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is used to execute the control method for the autonomous vehicle according to any one of claims 1-7.

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