Vehicle control method, device, equipment and unmanned vehicle

Abstract

This disclosure provides a vehicle control method, device, equipment, and unmanned vehicle, relating to the field of autonomous driving technology. The specific implementation scheme is as follows: Based on the vehicle's driving data and obstacle driving data within the current unit time period across multiple unit time periods, the acceleration decision value for the vehicle in the next unit time period is determined; based on the acceleration decision value and the vehicle's driving data, the reference speed and reference position of the vehicle at the end of the next unit time period are determined; based on the vehicle's reference speed and reference position at each moment, a reference speed sequence and a reference position sequence for the vehicle are obtained; the reference speed sequence and reference position sequence are optimized to obtain a planned speed sequence and a planned position sequence for the vehicle; the vehicle's driving is controlled based on the planned speed sequence and planned position sequence. This scheme fully considers the impact of obstacles on the vehicle within each time unit, improving the vehicle's driving stability and car-following performance.

Description

Technical Field

[0001] This disclosure relates to the field of artificial intelligence technology, specifically to the field of autonomous driving technology, and in particular to vehicle control methods, devices, equipment, and unmanned vehicles. Background Technology

[0002] Speed ​​planning for autonomous vehicles is one of the key factors in ensuring their safety and efficiency. The goal of speed planning is to determine the appropriate speed for the vehicle on the road in order to safely reach its destination and optimize driving efficiency while complying with traffic regulations.

[0003] When a vehicle enters a congested traffic area, or encounters unrecognizable or unpredictable road conditions (such as sudden obstacles or unknown road conditions), it needs to travel at extremely low speeds to ensure safety. However, existing solutions, which plan vehicle speed using ST graph search, rely too heavily on the stability of obstacle states. If obstacles move intermittently and repeatedly start and stop at low speeds, it can cause severe fluctuations in the vehicle's planned speed, leading to repeated emergency braking, significantly impacting vehicle performance and reducing passenger experience. Summary of the Invention

[0004] This disclosure provides a vehicle control method, apparatus, device, and unmanned vehicle.

[0005] According to a first aspect of this disclosure, a vehicle control method is provided, the method comprising:

[0006] Based on the vehicle's driving data and obstacle driving data in the current unit time period among multiple unit time periods, determine the vehicle's acceleration decision value in the next unit time period;

[0007] Based on the acceleration decision value and the vehicle's driving data, determine the reference speed and reference position of the vehicle at the end of the next unit time period;

[0008] Based on the reference speed and reference position of the vehicle at the end of each unit time period, the reference speed sequence and reference position sequence of the vehicle are obtained.

[0009] The reference speed sequence and reference position sequence are optimized to obtain the planned speed sequence and planned position sequence of the vehicle.

[0010] The vehicle's movement is controlled based on the planned speed sequence and the planned position sequence.

[0011] According to a second aspect of this disclosure, a vehicle control device is provided, the device comprising:

[0012] The first determining module is configured to determine the acceleration decision value of the vehicle in the next unit time period based on the vehicle's driving data and obstacle driving data in the current unit time period among multiple unit time periods;

[0013] The second determining module is configured to determine the reference speed and reference position of the vehicle at the end of the next unit time period based on the acceleration decision value and the vehicle's driving data.

[0014] The third determining module is configured to obtain the reference speed sequence and reference position sequence of the vehicle based on the reference speed and reference position of the vehicle at the end of each unit time period.

[0015] The optimization module is configured to optimize the reference speed sequence and reference position sequence to obtain the planned speed sequence and planned position sequence of the vehicle.

[0016] The control module is configured to control the vehicle's movement based on a planned speed sequence and a planned position sequence.

[0017] According to a third aspect of this disclosure, an electronic device is provided, comprising:

[0018] At least one processor; and

[0019] A memory that is communicatively connected to at least one processor; wherein,

[0020] The memory stores instructions that can be executed by at least one processor, such that the instructions are executed by at least one processor to enable the at least one processor to perform the method provided in the first aspect.

[0021] According to a fourth aspect of this disclosure, an unmanned vehicle is provided, the unmanned vehicle comprising:

[0022] A controller configured to execute the methods provided in the first aspect.

[0023] According to a fifth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions for causing a computer to perform the method provided in the first aspect.

[0024] The vehicle control method disclosed herein utilizes the vehicle's driving data and obstacle driving data within each unit of time to determine the vehicle's acceleration decision value for the next unit of time, thereby determining the vehicle's reference speed and reference position at the next moment. Based on this, a reference speed sequence and a reference position sequence are generated. These sequences are then optimized to obtain the vehicle's planned speed sequence and planned position sequence, which are used to control the vehicle's movement. Before determining each reference speed and reference position, the corresponding acceleration decision value in this disclosed scheme relies on both vehicle driving data and obstacle driving data, fully considering the impact of obstacles on the vehicle, improving the accuracy of the planned speed sequence and planned position sequence, effectively avoiding sudden braking or collisions, reducing vehicle stalling during driving, and improving the stability of vehicle driving and performance.

[0025] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0026] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0027] Figure 1 This is a flowchart of one embodiment of the vehicle control method according to the present disclosure;

[0028] Figure 2 This is a schematic diagram of one implementation process of the vehicle control method according to this disclosure;

[0029] Figure 3 This is a block diagram of one embodiment of a vehicle control device according to the present disclosure;

[0030] Figure 4 This is a block diagram of an electronic device used to implement the vehicle control method of this disclosure. Detailed Implementation

[0031] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0032] An exemplary system architecture for implementing the vehicle control method provided in this disclosure may include a terminal device, a network, and a server. The network provides a communication link between the terminal device and the server and may include various connection types, such as wired communication links, wireless communication links, or fiber optic cables, etc.

[0033] Users can use terminal devices to interact with servers over a network to receive or send information. Various client applications can be installed on these devices, such as map, entertainment, and navigation applications.

[0034] Terminal devices can be either hardware or software. When a terminal device is hardware, it can be various electronic devices, including but not limited to in-vehicle terminals, smartphones, tablets, laptops, and desktop computers; it can also be intelligent devices such as vehicles and intelligent robots, for example, self-driving cars and delivery robots. When a terminal device is software, it can be installed in the aforementioned electronic devices. It can be implemented as multiple software programs or software modules, or as a single software program or software module. No specific limitations are made here.

[0035] A server can be either hardware or software. When it's hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or as a single server. When it's software, it can be implemented as multiple software programs or software modules (e.g., used to provide distributed services), or as a single software program or software module. No specific limitations are made here.

[0036] It should be noted that the vehicle control method provided in this disclosure can be executed by the server in the above system architecture, or it can be implemented through the above terminal device (such as an in-vehicle terminal).

[0037] Figure 1 A schematic diagram of a flow 100 of an embodiment of the vehicle control method provided in this disclosure is shown, with reference to... Figure 1 As shown, the control method includes the following steps:

[0038] Step S101: Based on the vehicle's driving data and obstacle driving data within the current unit time period from multiple unit time periods, determine the vehicle's acceleration decision value for the next unit time period.

[0039] In this embodiment of the disclosure, the vehicle control method execution subject, such as a server or terminal device (e.g., an in-vehicle terminal), determines the vehicle's acceleration decision value in the next unit time based on the vehicle's driving data and obstacle driving data in the current unit time period in adjacent unit units.

[0040] It should be noted that the obstacles mentioned in the embodiments of this disclosure refer to obstacles in front of the vehicle and adjacent to the vehicle, that is, obstacles that follow the vehicle.

[0041] In this embodiment of the disclosure, for the driving data of an obstacle, the executing entity assumes that the obstacle is moving at a constant speed at its current speed, and thus determines the impact of the obstacle on the vehicle based on the driving data of the obstacle.

[0042] During the autonomous driving process, when the executing entity confirms that there is a moving obstacle (such as a vehicle) in a preset range ahead of the vehicle's driving path and the current road conditions are not conducive to overtaking (such as a congested road section), the executing entity confirms the moving obstacle as a following obstacle for the vehicle.

[0043] The aforementioned executing entity can determine the speed of the obstacle after identifying the following obstacle in front of the vehicle.

[0044] In some alternative implementations, the executing entity first determines the actual speed of the obstacle in its direction of travel, and then determines the projection of the actual speed onto the vehicle's speed, which is used as the obstacle's speed that affects the vehicle.

[0045] For example, the executing entity uses the projection component of the actual driving speed in the direction of the vehicle's current speed as the obstacle driving speed that affects the vehicle's driving data.

[0046] In some optional implementations, after the executing entity determines the speed of the obstacle, it determines that the acceleration of the obstacle at the end of the current unit time period is 0. That is, at the end of each current unit time period in the process of predicting the vehicle speed or acceleration, the obstacle is considered to be traveling at a constant speed at the end of the current unit time period.

[0047] In some optional implementations, the vehicle's driving data includes its position, speed, and acceleration, while the obstacle's driving data includes its position, speed, and acceleration (default is 0).

[0048] The aforementioned executing entity can determine the relative distance between the vehicle and the obstacle at the end of the current unit time period, i.e., the current vehicle distance, based on the location of the obstacle and the vehicle's travel position.

[0049] For example, after determining the current distance between the vehicle and the obstacle, the executing entity can combine driving data such as the vehicle speed and the obstacle speed to determine the acceleration decision value of the vehicle in the next unit time period.

[0050] To ensure smooth vehicle operation and avoid excessive acceleration or deceleration that could affect passenger comfort and vehicle performance, an appropriate acceleration threshold can be set for the vehicle based on its performance characteristics.

[0051] In some alternative implementations, the executing entity compares the calculated theoretical acceleration value with the acceleration threshold and determines the smaller of the two values ​​as the acceleration decision value.

[0052] In some alternative implementations, the executing entity can use a hybrid IDM model to calculate the vehicle's acceleration decision value in each unit time period, thereby obtaining the vehicle's acceleration decision sequence.

[0053] Step S102: Based on the acceleration decision value and the vehicle's driving data, determine the reference speed and reference position of the vehicle at the end of the next unit time period.

[0054] In this embodiment of the disclosure, the vehicle control method execution subject, such as a server or terminal device (e.g., an in-vehicle terminal), determines the reference speed and reference position of the vehicle at the end of the next unit time period based on the acceleration decision value determined in step S101 and the vehicle driving data within the current unit time period.

[0055] For each time unit after the current moment, the aforementioned executing entity determines the reference speed and reference position of the vehicle at the end of that time unit based on the acceleration decision value within that time unit and the vehicle's driving data within the adjacent previous time unit.

[0056] In some alternative implementations, the executing entity can determine the reference speed of the vehicle at the end of the next time unit based on the vehicle's speed at the end of the current time unit, the duration of the next time unit, and the acceleration decision value within the next time unit.

[0057] In some alternative implementations, the executing entity can determine the reference position of the vehicle at the end of the next time unit based on the vehicle's speed and position at the end of the current time unit, the duration of the next time unit, and the acceleration decision value within the next time unit.

[0058] Step S103: Based on the reference speed and reference position of the vehicle at the end of each unit time period, obtain the reference speed sequence and reference position sequence of the vehicle.

[0059] In this embodiment of the disclosure, the vehicle control method execution subject, such as a server or terminal device (e.g., an in-vehicle terminal), arranges the reference speed sequence and reference position sequence of the vehicle in chronological order according to the determined reference speed and reference position of the vehicle at the end of each unit time period.

[0060] For example, taking the current moment as the end of the current time period and using 0.5s as a time period unit, the system predicts the reference speed sequence and reference position sequence of the vehicle within the next 8s. The executing entity determines the vehicle's acceleration decision value within 0 to 0.5s based on the vehicle's driving data and obstacle driving data within the current time period unit (-0.5s to 0s), and then determines the vehicle's reference position and reference speed at 0.5s based on this acceleration decision value. This process is repeated recursively to determine the vehicle's reference speed and reference position at 0.5s, 1.0s, 1.5s...7.5s, and 8s. The executing entity arranges the determined reference speeds at each moment in chronological order to obtain the vehicle's reference speed sequence within 0 to 8s; similarly, arranging the determined reference position sequences at each moment in chronological order yields the vehicle's reference position sequence within 0 to 8s.

[0061] Step S104: Optimize the reference speed sequence and reference position sequence to obtain the planned speed sequence and planned position sequence of the vehicle.

[0062] In this embodiment of the disclosure, the vehicle control method is executed by a server or terminal device (e.g., an in-vehicle terminal), which optimizes the reference speed sequence and reference position sequence to obtain the planned speed sequence and planned position sequence of the vehicle.

[0063] As mentioned above, both the reference speed sequence and the reference position sequence are discrete data at the end of multiple unit time periods, which is not conducive to stable vehicle operation. Therefore, it is necessary to optimize the reference speed sequence and the reference position sequence.

[0064] In some optional implementations, the executing entity can preset corresponding optimization rules or pre-train corresponding optimization models to optimize the reference speed sequence and reference position sequence respectively, in order to obtain a planned speed sequence and a planned position sequence that can control the smooth driving of the vehicle.

[0065] In some optional implementations of the embodiments of this disclosure, optimizing the reference speed sequence and the reference position sequence to obtain the planned speed sequence and the planned position sequence of the vehicle includes: optimizing the reference speed sequence based on the sum of squared deviations between the planned speed and the reference speed to obtain the planned speed sequence; optimizing the reference position sequence based on the sum of squared deviations between the planned position and the reference position to obtain the planned position sequence, wherein the value of the planned position is less than or equal to the value of the reference position.

[0066] In this implementation, the executing entity optimizes the reference speed and reference position at the end of each unit time period by using the sum of squared deviations between its optimization results and the reference speed or reference position as the optimization function during the optimization process, thereby obtaining the optimized planned position sequence and planned speed sequence.

[0067] For example, an ST graph of the vehicle's position (S)-time (T) relationship can be constructed based on the reference position sequence and the reference speed sequence, and then the reference speed sequence and the reference position sequence can be optimized by searching the ST graph.

[0068] For example, when using an optimization model, the reference velocity and reference position at each time step can be used as the input to the optimization model, the corresponding optimized velocity and optimized position can be used as the expected output, and the sum of squared deviations between the input and output can be used as the loss function of the optimization model. In this way, the reference velocity sequence and reference position sequence can be optimized to obtain the corresponding optimization result.

[0069] For example, the following optimization model can be adopted:

[0070]

[0071] In the formula, s represents the planned location, v represents the planned velocity, a represents the acceleration decision value, i represents the i-th unit time interval, t represents the duration of each unit time interval, and j represents the impact intensity. The impact intensity is the reciprocal of the acceleration.

[0072] In some alternative implementations, if the executing entity uses a hybrid IDM model to calculate the vehicle's acceleration decision value within each unit time period, it is difficult to guarantee the vehicle's emergency braking under emergency conditions. Therefore, when optimizing the reference speed sequence and reference position sequence, the reference position at the end of each unit time period is used as a hard constraint for optimization, ensuring that the optimized reference position does not exceed the calculated reference position, thus preventing a collision between the vehicle and the obstacle.

[0073] In this embodiment of the disclosure, the optimized sequence is represented in the ST diagram as a smooth and continuous change in both the position curve and the velocity curve. Therefore, based on the optimized planned velocity sequence and planned position sequence, the vehicle can be controlled to drive smoothly, ensuring that the vehicle's speed changes smoothly during driving, avoiding emergency braking or sudden acceleration, and also avoiding collisions with obstacles in front, thus ensuring vehicle performance and passenger riding experience.

[0074] Step S105: Control the vehicle's movement according to the planned speed sequence and planned position sequence.

[0075] In this embodiment of the disclosure, the vehicle control method execution subject, such as a server or terminal device (e.g., an in-vehicle terminal), can control the autonomous driving of the vehicle according to the optimized planned speed sequence and planned position sequence.

[0076] In this process, the obstacle driving data and vehicle driving data within the current unit time period are fully combined to predict the acceleration decision value of the vehicle in the next unit time period during the prediction of each reference position sequence and reference speed sequence. That is, the impact of obstacles on the vehicle in each time period is fully considered. Therefore, the optimized planned speed sequence and planned position sequence have also fully taken into account the impact of obstacles in each unit time period. Controlling the vehicle's driving based on this can effectively improve the stability of the vehicle's driving process and effectively reduce emergency braking, frequent start-stop situations, etc.

[0077] According to the vehicle control method provided in this disclosure, the executing entity uses the vehicle's driving data and obstacle driving data within each unit time period to determine the vehicle's acceleration decision value for the next unit time period. This, in turn, determines the vehicle's reference speed and reference position at the end of the next unit time period, generating a reference speed sequence and a reference position sequence. The reference speed sequence and reference position sequence are then optimized to obtain the vehicle's planned speed sequence and planned position sequence. Before determining each reference speed and reference position, the corresponding acceleration decision value relies on both the vehicle's driving data and obstacle driving data. This fully considers the impact of obstacles on the vehicle, improving the accuracy of the planned speed sequence and planned position sequence, effectively avoiding sudden braking or collisions, reducing vehicle stalling during driving, and improving the stability of vehicle driving and performance.

[0078] It should be noted that in the vehicle control method disclosed herein, the scenarios of autonomous driving where other obstacles cross the vehicle's current path, intersections, or unprotected left turns are not considered. That is, it is assumed that the vehicle can also drive if obstacles are accessible.

[0079] In some implementations, the vehicle control method of this disclosure can be used in slow-moving road conditions where the obstacle speed is less than 10 km / h. The executing entity can use the vehicle control method of this disclosure to predict the planned speed sequence and planned position sequence of the vehicle within 15m ahead along the current path direction, so as to effectively control the vehicle to drive smoothly and avoid the occurrence of emergency braking, collisions, rapid starts, etc., while avoiding frequent starts and stops of the vehicle.

[0080] The acquisition, storage, and application of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0081] Figure 2A schematic diagram of flow 200 of an embodiment of the vehicle control method provided in this disclosure is shown, with reference to... Figure 2 As shown, the control method includes the following steps:

[0082] Step S201: Based on the vehicle's driving data and obstacle's driving data within the current unit time period, determine the vehicle's expected distance and the current distance between the vehicle and the obstacle.

[0083] In this embodiment of the disclosure, the vehicle control method execution subject, such as a server or terminal device (e.g., an in-vehicle terminal), determines the expected distance between the vehicle and the obstacle based on the vehicle's driving data and the obstacle's driving data within the current unit time period.

[0084] The expected distance between the vehicle and the obstacle is the expected distance between the vehicle and the obstacle at the end of multiple unit time periods, that is, the expected distance between the vehicle and the obstacle at the end of the predicted time range.

[0085] The current distance is the distance between the vehicle and the obstacle at the end of the current time period.

[0086] In this embodiment of the disclosure, the aforementioned execution entity determines the current distance between the vehicle and the obstacle based on the vehicle's driving data and the obstacle's driving data within the current unit time period, and determines the expected distance of the vehicle at the end of the total time period by combining the total duration of multiple unit time periods.

[0087] In some optional implementations of the embodiments of this disclosure, the vehicle driving data includes the vehicle position, vehicle speed, and stopping distance, and the obstacle driving data includes the obstacle position and obstacle speed; and determining the expected vehicle distance and the current vehicle distance between the vehicle and the obstacle based on the vehicle driving data and obstacle driving data within the current unit time period includes: determining the expected vehicle distance based on the vehicle speed, obstacle speed, and stopping distance; and determining the current vehicle distance between the vehicle and the obstacle based on the vehicle position and obstacle position.

[0088] In this embodiment of the disclosure, the vehicle's driving data within the current unit time period includes the vehicle's position and speed at the end of the current unit time period, and may also include the parking distance required for the vehicle to safely stop; the obstacle's driving speed within the current unit time period includes the obstacle's speed and position at the end of the current unit time period.

[0089] Among them, obstacle driving data is obtained by the autonomous driving system of the vehicle from analyzing the driving information of the detected obstacles. The detection of obstacles is based on the shape and position of the obstacles. Therefore, the obstacle position is the position of the obstacle relative to the edge of the nearest outer contour of the vehicle (such as the outer contour of the rear of the vehicle or the back contour of a pedestrian).

[0090] For example, the executing entity can calculate the difference between the obstacle position and the vehicle position at the end of the current unit time period to obtain the current vehicle distance at the end of the current unit time period.

[0091] In some alternative implementations, the vehicle's position is determined by the executing entity based on the vehicle's center position, while the vehicle's speed and position planning is based on the position of the vehicle's front bumper. In this case, at the end of any given time period, the current distance between the vehicle and the obstacle is calculated as follows: first, the difference between the obstacle's position and the vehicle's position is calculated; then, the distance between the front bumper and the vehicle's center is subtracted from this difference.

[0092] For example, if the distance between the front bumper and the center of the vehicle is L, and the vehicle's position at the end of the i-th time unit is s... i The obstacle's position is s′ i Then the current distance S between the vehicle and the obstacle i For: S i =s′ i -s i -L.

[0093] In some alternative implementations, the vehicle's position is determined by the position of its front bumper, while the vehicle's speed and position planning is based on its center position. In this case, at the end of any given time period, the current distance between the vehicle and the obstacle is calculated by first determining the difference between the obstacle's position and the vehicle's position, and then adding the distance between the front bumper and the vehicle's center to that difference. For example, S... i =s′ i -s i +L.

[0094] For example, the executing entity determines the expected distance of the vehicle at the end of the current unit time period based on the vehicle speed, obstacle speed, and parking distance at the end of the current unit time period, combined with the total duration of multiple unit time periods.

[0095] In some optional implementations of the embodiments of this disclosure, determining the desired distance of the vehicle based on the vehicle speed, the obstacle speed, and the stopping distance includes: determining the vehicle's headway based on the obstacle speed; and determining the desired distance based on the headway, the stopping distance, the vehicle speed, and the obstacle speed.

[0096] Among them, the headway is the time difference between when the vehicle and the obstacle pass the same position.

[0097] In this implementation, since the vehicle's speed and position are uncertain in the future, the vehicle's headway is determined based on obstacle travel data. For example, the vehicle's headway is determined based on the obstacle's speed.

[0098] In some optional implementations, the headway T of the vehicle can be defined. h =t0+v′ i / T, where t0 is the minimum headway between the vehicle and the obstacle (the headway when the distance between the front of the vehicle and the rear of the obstacle is 0), v′ i Let T be the speed of the obstacle at the end of the i-th time interval, and let T be the total duration of the multiple time intervals.

[0099] After determining the headway of its own vehicle, the executing entity can calculate the expected distance of its own vehicle based on the headway, the parking distance, the vehicle's speed at the end of the current time period, and the speed of the obstacle.

[0100] In some alternative implementations, the executing entity can calculate the desired vehicle distance S according to the following formula. * :

[0101] S * =S s +T h ×v i +v i ×(v i -v′ i ) / 4

[0102] Among them, S s The preset parking distance for the vehicle, T h v is the headway of the vehicle. i Let v′ be the vehicle speed at the end of the i-th time unit. i Let be the obstacle velocity at the end of the i-th unit time interval.

[0103] To ensure safe braking and avoid collisions, the vehicle's stopping distance S... sThe stopping distance needs to be greater than the vehicle's minimum braking distance. For example, if the vehicle's minimum braking distance is 1.5m, its stopping distance can be set to 2.5m or 3m, etc.

[0104] Step S202: Determine the desired acceleration of the vehicle based on the desired distance to the vehicle and the current distance to the vehicle.

[0105] In this embodiment of the disclosure, the vehicle control method execution subject, such as a server or terminal device (e.g., an in-vehicle terminal), determines the vehicle's desired acceleration based on the vehicle's desired distance to other vehicles and the current distance to other vehicles.

[0106] In this embodiment of the disclosure, the desired acceleration of the vehicle is the theoretical acceleration value adopted by the vehicle to achieve the desired distance without considering factors such as vehicle performance and driving stability.

[0107] In some optional implementations of the embodiments of this disclosure, the vehicle driving data further includes an acceleration threshold, and determining the expected acceleration of the vehicle based on the expected distance and the current distance, including: determining an acceleration coefficient for the vehicle based on the expected distance, the current distance, the vehicle speed, and the obstacle speed; and determining the expected acceleration of the vehicle based on the acceleration coefficient and the acceleration threshold.

[0108] Among them, the vehicle's acceleration coefficient represents the ideal coefficient that the vehicle can use when accelerating under the influence of obstacles on the current path.

[0109] The executing entity determines the vehicle's acceleration coefficient based on the expected distance between vehicles at the end of the current time period, the current distance between vehicles, the vehicle's speed, and the obstacle's speed. Then, it determines the vehicle's expected acceleration by multiplying this acceleration coefficient by the vehicle's acceleration threshold.

[0110] In some alternative implementations, if the vehicle's acceleration coefficient is defined as k, it can be calculated using the following formula:

[0111] k = 1 + (v i -v′ i ) / (v′ i +1.5)-(S * / S i ) 2

[0112] Furthermore, the implementing entity can use this information to determine the vehicle's expected acceleration a. * for:

[0113] a * =a m ×k

[0114] Right now:

[0115] a* =a m ×(1+(v i -v′ i ) / (v′ i +1.5)-(S * / S i ) 2 )

[0116] Step S203: Based on the desired acceleration and the vehicle's driving data, determine the vehicle's acceleration decision value for the next unit time period.

[0117] In this embodiment of the disclosure, the vehicle control method execution subject, such as a server or terminal device (e.g., an in-vehicle terminal), determines the acceleration decision value of the vehicle in the next unit time period based on the desired acceleration and the vehicle's driving data.

[0118] For example, the aforementioned executing entity can determine the maximum impact coefficient that the vehicle can withstand, or the steady impact coefficient under stable driving conditions, based on the vehicle's performance. Then, based on the maximum impact coefficient or steady impact coefficient, and in conjunction with the vehicle's desired acceleration, the vehicle's acceleration decision value is determined.

[0119] For example, considering that acceleration directly affects the vehicle speed, the vehicle speed, vehicle acceleration, and vehicle impact coefficient at the end of the current unit time period can be combined to determine the vehicle acceleration decision value.

[0120] In some optional implementations of the embodiments of this disclosure, the vehicle driving data includes the current acceleration and the impact coefficient; and determining the acceleration decision value of the vehicle in the next unit time period based on the expected acceleration and the vehicle driving data includes: determining the acceleration decision value of the vehicle in the next unit time period based on the expected acceleration, the current acceleration and the impact coefficient.

[0121] For example, the impact coefficient of the vehicle can be defined as P, for instance, P can be set to a value of 0.3 based on the vehicle's performance. The acceleration decision value of the vehicle in the next unit time period can be determined by the following formula:

[0122] a i+1 =a i +P×(a * -a i )

[0123] Step S204: Based on the acceleration decision value and the vehicle's driving data, determine the reference speed and reference position of the vehicle at the end of the next unit time period.

[0124] In this embodiment of the disclosure, the vehicle control method execution subject, such as a server or terminal device (e.g., an in-vehicle terminal), determines the reference speed and reference position of the vehicle at the end of the next unit time period based on the acceleration decision value and the vehicle's driving data.

[0125] Step S204 and Figure 1 The steps S102 in the illustrated embodiment are basically the same. For the specific implementation method, please refer to the foregoing description of step S102. It will not be repeated here.

[0126] Step S205: Based on the reference speed and reference position of the vehicle at the end of each unit time period, obtain the reference speed sequence and reference position sequence of the vehicle.

[0127] In this embodiment of the disclosure, the vehicle control method execution subject, such as a server or terminal device (e.g., an in-vehicle terminal), obtains the reference speed sequence and reference position sequence of the vehicle based on the reference speed and reference position of the vehicle at the end of each unit time period.

[0128] Step S205 and Figure 1 The steps S103 in the illustrated embodiment are basically the same. For the specific implementation method, please refer to the above description of step S103, which will not be repeated here.

[0129] Step S206: Optimize the reference speed sequence and reference position sequence to obtain the planned speed sequence and planned position sequence of the vehicle.

[0130] In this embodiment of the disclosure, the vehicle control method execution subject, such as a server or terminal device (e.g., an in-vehicle terminal), optimizes the obtained reference speed sequence and reference position sequence to obtain the vehicle's planned speed sequence and planned position sequence.

[0131] Step S206 and Figure 1 The steps S104 in the illustrated embodiment are basically the same. For the specific implementation method, please refer to the foregoing description of step S104. It will not be repeated here.

[0132] Step S207: Control the vehicle's movement according to the planned speed sequence and the planned position sequence.

[0133] In this embodiment of the disclosure, the vehicle control method is executed by a server or terminal device (e.g., an in-vehicle terminal), which controls the vehicle to perform autonomous driving based on the obtained planned speed sequence and planned position sequence.

[0134] Step S207 and Figure 1 The steps S105 in the illustrated embodiment are basically the same. For the specific implementation method, please refer to the foregoing description of step S105. It will not be repeated here.

[0135] The vehicle control method provided in this disclosure involves an execution entity determining the current distance between the vehicle and the obstacle, as well as the desired distance, based on the vehicle's driving data and obstacle driving data within the current unit time period. Based on this, the desired acceleration of the vehicle is determined. Then, based on this desired acceleration and the vehicle's driving data (e.g., impact coefficient, vehicle speed, current acceleration decision value, etc.), the acceleration decision value for the vehicle in the next unit time period is determined. The reference position and reference speed of the vehicle are then determined, and optimization is performed to obtain the planned speed and planned position. By combining obstacle driving data to determine the desired distance, the influence of obstacles on the vehicle is fully considered, improving the reliability of the vehicle's acceleration decision, thereby improving the reliability of the vehicle's speed and position planning and ensuring the smoothness and safety of the vehicle's driving.

[0136] As an implementation of the methods shown in the above figures, Figure 3 An embodiment of a vehicle control device according to the present disclosure is shown. The vehicle control device 300 is... Figure 1 Corresponding to the method embodiments shown, this device can be applied to various electronic devices.

[0137] Reference Figure 3 As shown, the vehicle control device 300 provided in this embodiment includes: a first determining module 301, a second determining module 302, a third determining module 303, an optimization module 304, and a control module 305. The first determining module 301 is configured to determine the acceleration decision value of the vehicle in the next unit time period based on the vehicle's driving data and obstacle driving data within the current unit time period. The second determining module 302 is configured to determine the reference speed and reference position of the vehicle at the end of the next unit time period based on the acceleration decision value and the vehicle's driving data. The third determining module 303 is configured to obtain a reference speed sequence and a reference position sequence of the vehicle based on the reference speed and reference position of the vehicle at the end of each unit time period. The optimization module 304 is configured to optimize the reference speed sequence and reference position sequence to obtain a planned speed sequence and a planned position sequence of the vehicle. The control module 305 is configured to control the vehicle's movement based on the planned speed sequence and planned position sequence.

[0138] In the vehicle control device 300 of this embodiment, the specific processing of the first determining module 301, the second determining module 302, the third determining module 303, the optimization module 304, and the control module 305, and the resulting technical effects, can be found in the following references: Figure 1 The relevant descriptions of steps S101-S105 in the corresponding embodiments will not be repeated here.

[0139] In some optional implementations of the embodiments of this disclosure, the first determining module includes a first determining unit, a second determining unit, and a third determining unit. The first determining unit is configured to determine the expected distance of the vehicle and the current distance between the vehicle and the obstacle based on the vehicle's driving data and obstacle driving data within the current unit time period. The second determining unit is configured to determine the expected acceleration of the vehicle based on the expected distance and the current distance. The third determining unit is configured to determine the acceleration decision value of the vehicle in the next unit time period based on the expected acceleration and the vehicle's driving data.

[0140] In some optional implementations of the embodiments of this disclosure, the vehicle driving data includes the vehicle position, vehicle speed, and stopping distance; the obstacle driving data includes the obstacle position and obstacle speed; and the first determining unit includes a first determining subunit and a second determining subunit, wherein the first determining subunit is configured to determine the desired distance between the vehicle and the obstacle based on the vehicle speed, obstacle speed, and stopping distance; and the second determining subunit is configured to determine the current distance between the vehicle and the obstacle based on the vehicle position and obstacle position.

[0141] In some optional implementations of the embodiments of this disclosure, the first determining subunit is configured to: determine the vehicle's headway based on the obstacle speed; and determine the desired vehicle distance based on the headway, stopping distance, vehicle speed, and obstacle speed.

[0142] In some optional implementations of the embodiments of this disclosure, the vehicle driving data further includes an acceleration threshold, and the second determining unit is configured to determine an acceleration coefficient for the vehicle based on the desired vehicle distance, the current vehicle distance, the vehicle speed, and the obstacle speed; and to determine the desired acceleration of the vehicle based on the acceleration coefficient and the acceleration threshold.

[0143] In some optional implementations of the embodiments of this disclosure, the vehicle driving data includes the current acceleration and the impact coefficient; and the third determining unit is configured to: determine the acceleration decision value of the vehicle in the next unit time period based on the expected acceleration, the current acceleration and the impact coefficient.

[0144] In some optional implementations of the embodiments of this disclosure, the optimization module is configured to: optimize the reference speed sequence based on the sum of squared deviations between the planned speed and the reference speed to obtain a planned speed sequence; optimize the reference position sequence based on the sum of squared deviations between the planned position and the reference position to obtain a planned position sequence, wherein the value of the planned position is less than or equal to the value of the reference position.

[0145] The vehicle control method and vehicle control device provided in this disclosure can be applied to the field of autonomous driving technology. For example, they can be used for autonomous driving of vehicles in congested road sections. Accordingly, this disclosure also provides an autonomous driving vehicle for executing the above-described vehicle control method.

[0146] The unmanned vehicle provided in this embodiment includes: a vehicle body, at least one imaging device mounted on the vehicle body, a positioning radar, and a controller. The imaging device is configured to acquire images of the environment surrounding the vehicle body, including the movement of obstacles around the vehicle; the positioning radar is configured to locate the vehicle body; and the controller is configured to execute the vehicle control method described in the above embodiments.

[0147] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0148] Figure 4 A schematic block diagram of an example electronic device 400 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0149] like Figure 4 As shown, device 400 includes a computing unit 401, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 402 or a computer program loaded from storage unit 408 into random access memory (RAM) 403. RAM 403 may also store various programs and data required for the operation of device 400. The computing unit 401, ROM 402, and RAM 403 are interconnected via bus 404. Input / output (I / O) interface 405 is also connected to bus 404.

[0150] Multiple components in device 400 are connected to I / O interface 405, including: input unit 406, such as keyboard, mouse, etc.; output unit 407, such as various types of monitors, speakers, etc.; storage unit 408, such as disk, optical disk, etc.; and communication unit 409, such as network card, modem, wireless transceiver, etc. Communication unit 409 allows device 400 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0151] The computing unit 401 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 401 performs the various methods and processes described above, such as vehicle control methods. For example, in some embodiments, the vehicle control method may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on device 400 via ROM 402 and / or communication unit 409. When the computer program is loaded into RAM 403 and executed by the computing unit 401, one or more steps of the vehicle control method described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to perform the vehicle control method by any other suitable means (e.g., by means of firmware).

[0152] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0153] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0154] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, 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 devices, magnetic storage devices, or any suitable combination of the foregoing.

[0155] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0156] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0157] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0158] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0159] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A vehicle control method, characterized in that, The method includes: Based on the vehicle's driving data and obstacle's driving data within the current unit time period, determine the vehicle's expected distance and the current distance between the vehicle and the obstacle. The vehicle's driving data includes the current acceleration and impact coefficient. The desired acceleration of the vehicle is determined based on the desired distance and the current distance. Based on the expected acceleration and the vehicle's driving data, determine the vehicle's acceleration decision value for the next unit time period; and based on the expected acceleration, the current acceleration, and the impact coefficient, determine the vehicle's acceleration decision value for the next unit time period. Based on the acceleration decision value and the vehicle's driving data, determine the reference speed and reference position of the vehicle at the end of the next unit time period; Based on the reference speed and reference position of the vehicle at the end of each unit time period, the reference speed sequence and reference position sequence of the vehicle are obtained; The reference speed sequence and the reference position sequence are optimized to obtain the planned speed sequence and planned position sequence of the vehicle. The vehicle's movement is controlled based on the planned speed sequence and the planned position sequence.

2. The method according to claim 1, characterized in that, The vehicle driving data includes the vehicle's position, vehicle speed, and stopping distance; the obstacle driving data includes the obstacle's position and obstacle speed. as well as The step of determining the expected distance of the vehicle and the current distance between the vehicle and the obstacle based on the vehicle's driving data and the obstacle's driving data includes: The desired distance between the vehicle and the obstacle is determined based on the vehicle speed, the obstacle speed, and the parking distance. Based on the vehicle's position and the obstacle's position, determine the current distance between the vehicle and the obstacle.

3. The method according to claim 2, characterized in that, Determining the desired distance of the vehicle based on the vehicle speed, the obstacle speed, and the parking distance includes: The vehicle's headway is determined based on the speed of the obstacle; The desired vehicle distance is determined based on the headway, the parking distance, the vehicle speed, and the obstacle speed.

4. The method according to claim 2, characterized in that, The vehicle driving data also includes acceleration thresholds, and Determining the desired acceleration of the vehicle based on the desired distance and the current distance includes: An acceleration coefficient for the vehicle is determined based on the desired vehicle distance, the current vehicle distance, the vehicle speed, and the obstacle speed. The desired acceleration of the vehicle is determined based on the acceleration coefficient and the acceleration threshold.

5. The method according to claim 1, characterized in that, The optimization of the reference velocity sequence and the reference position sequence to obtain the planned velocity sequence and the planned position sequence includes: The reference speed sequence is optimized based on the sum of squared deviations between the planned speed and the reference speed to obtain the planned speed sequence; The reference position sequence is optimized based on the sum of squared deviations between the planned position and the reference position to obtain the planned position sequence, wherein the value of the planned position is less than or equal to the value of the reference position.

6. A vehicle control device, characterized in that, The device includes: The first determining module is configured to determine the expected distance of the vehicle and the current distance between the vehicle and the obstacle based on the vehicle's driving data and obstacle driving data within the current unit time period, wherein the vehicle driving data includes the current acceleration and the impact coefficient; determine the expected acceleration of the vehicle based on the expected distance and the current distance; determine the acceleration decision value of the vehicle in the next unit time period based on the expected acceleration and the vehicle driving data; and determine the acceleration decision value of the vehicle in the next unit time period based on the expected acceleration, the current acceleration, and the impact coefficient. The second determining module is configured to determine the reference speed and reference position of the vehicle at the end of the next unit time period based on the acceleration decision value and the vehicle driving data. The third determining module is configured to obtain a reference speed sequence and a reference position sequence of the vehicle based on the reference speed and the reference position of the vehicle at the end of each unit time period. The optimization module is configured to optimize the reference speed sequence and the reference position sequence to obtain the planned speed sequence and planned position sequence of the vehicle. The control module is configured to control the movement of the vehicle according to the planned speed sequence and the planned position sequence.

7. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.

8. An unmanned vehicle, characterized in that, The unmanned vehicles include: A controller configured to perform the method of any one of claims 1-5.

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