Vehicle control method, device, computer equipment, storage medium and program product

Abstract

The present invention relates to the field of vehicle control technology, and discloses a vehicle control method, device, computer equipment, storage medium and program product. The method comprises: obtaining driving information of a vehicle at a target time; wherein the driving information comprises a driving position and a driving direction; according to the driving information, determining a target position and a target direction corresponding to the target time from a path planning route; determining a position deviation value according to the driving position and the target position, and determining a direction deviation value according to the driving direction and the target direction; and controlling the vehicle to adjust according to the position deviation value and the direction deviation value.

Description

Technical Field

[0001] The present invention relates to the field of vehicle control technology, and in particular to a vehicle control method, device, computer equipment, storage medium and program product. Background Art

[0002] In existing autonomous driving control systems, path planning is a crucial link. Path planning refers to the use of algorithms to determine the best driving path for a vehicle on the road to achieve safe and efficient driving. Specifically, the autonomous driving system will generate the optimal driving route from the starting point to the end point based on real-time road conditions and traffic rules to ensure that the vehicle can reach its destination safely and efficiently.

[0003] However, when the vehicle is driving along the planned route, the execution device in the vehicle may receive wrong information, causing the vehicle's driving route to deviate from the planned route. Such deviation may cause serious traffic accidents and cause immeasurable consequences. Summary of the invention

[0004] In view of this, the present invention provides a vehicle control method, apparatus, computer equipment, storage medium and program product.

[0005] In a first aspect, the present invention provides a vehicle control method, the method comprising: obtaining driving information of a vehicle at a target time; wherein the driving information comprises a driving position and a driving direction; determining a target position and a target direction corresponding to the target time from a path planning route according to the driving information; determining a position deviation value according to the driving position and the target position, and determining a direction deviation value according to the driving direction and the target direction; and controlling the vehicle to adjust according to the position deviation value and the direction deviation value.

[0006] The vehicle control method provided in this embodiment obtains the driving information of the vehicle at the target time, including the driving position and driving direction, and determines the target position and target direction corresponding to the target time from the path planning route according to the driving information obtained in real time. Then, the system calculates the position deviation value between the current driving position and the target position, and the direction deviation value between the current driving direction and the target direction. These two deviation values ​​reflect the difference between the current state of the vehicle and the planned route. According to these deviation values, the vehicle is controlled to make corresponding adjustments so that it can return to the planned route, so that the deviation of the vehicle during driving can be monitored and corrected in real time, thereby effectively solving the problem that the vehicle may deviate from the planned route due to erroneous information.

[0007] In one possible implementation, the process of determining a path planning route includes: obtaining the number of obstacles in the lane where the vehicle is located; if the number of obstacles is not less than a preset number, obtaining a first target distance between two adjacent obstacles in front of the vehicle; if the first target distance is less than the preset distance, obtaining the outer contour points of the two adjacent obstacles and the vehicle's passage space; based on the outer contour points and the vehicle's passage space, generating a path planning route in the lane where the vehicle is located.

[0008] The vehicle control method provided in this embodiment can more comprehensively understand the current road environment by obtaining the number of obstacles in the lane where the vehicle is located, and provide more accurate input information for subsequent path planning. At the same time, if the number of obstacles is large, the first target distance between two adjacent obstacles is further determined to identify narrow or crowded sections of the road, so as to make more reasonable path planning. In addition, by obtaining the outer contour points of two adjacent obstacles and the passage space of the vehicle, the passable path can be calculated more accurately to avoid collision with obstacles.

[0009] In one possible implementation, a path planning route is generated within the lane where the ego vehicle is located based on external contour points and the ego vehicle's passable space, including: generating the ego vehicle's driving route based on the external contour points; determining multiple driving spaces of the ego vehicle based on the driving route and the boundary of the lane where the ego vehicle is located; detecting whether the multiple driving spaces are smaller than the ego vehicle's passable space; if the multiple driving spaces are not smaller than the ego vehicle's passable space, generating a path planning route within the lane where the ego vehicle is located based on the driving route.

[0010] The vehicle control method provided in this embodiment can ensure that the path planning closely follows the obstacle contour while maintaining a certain safety distance to avoid collision by generating a driving route based on external contour points, and can ensure that the planned path will not cause safety problems due to insufficient space during actual driving by detecting whether multiple driving spaces are smaller than the vehicle's own passage space.

[0011] In a possible implementation, the method also includes: obtaining the position of the obstacle closest to the front of the vehicle and the position of the obstacle closest to the rear of the vehicle; determining a second target distance based on the position of the vehicle and the position of the obstacle closest to the front of the vehicle; determining a third target distance based on the position of the vehicle and the position of the obstacle closest to the rear of the vehicle; detecting whether the second target distance is within a first dangerous driving range; if the second target distance is within the first dangerous driving range, generating an alarm prompt and displaying the alarm prompt to a user; if the second target distance is within the second dangerous driving range, detecting whether the third target distance is within a safe driving range; if the third target distance is within the safe driving range, controlling the vehicle to brake.

[0012] The vehicle control method provided in this embodiment can detect potential collision risks in a timely manner by real-time monitoring the distance between the vehicle and the nearest obstacles in front and behind. When the second target distance (i.e., the distance between the vehicle and the obstacle in front) is in the first dangerous driving range, an alarm prompt will be immediately generated and displayed to the user, thereby reminding the user to take emergency braking or other avoidance measures. When the second target distance is in the second dangerous driving range and the third target distance (i.e., the distance between the vehicle and the obstacle behind) is in the safe driving range, the system will control the vehicle to brake to avoid rear-end collisions.

[0013] In a possible implementation, the method further includes: if the third target distance is not within the safe driving range, detecting whether there is an obstacle in an adjacent lane; if there is no obstacle in the adjacent vehicle, controlling the vehicle to travel through the adjacent lane.

[0014] The vehicle control method provided in this embodiment provides a potential escape path for the vehicle by detecting whether the adjacent lane is safe when the distance between the vehicle and the rear obstacle is too close and there is a risk of rear-end collision. If there is no obstacle in the adjacent lane, the vehicle can change lanes safely, so that the vehicle can quickly identify and use the space in the adjacent lane in an emergency, thereby improving the emergency response and risk avoidance capabilities of the vehicle.

[0015] In a possible implementation, the method also includes: obtaining status information of the vehicle; detecting whether the status information of the vehicle is in preset status information; if the status information of the vehicle is in preset status information, obtaining vehicle speed information, radar ranging information, and light sensor information; and determining to adjust the light source status of the vehicle based on the vehicle speed information, radar ranging information, and light sensor information.

[0016] The vehicle control method provided in this embodiment can quickly respond to road and weather changes, adjust the light source status in time, and ensure driving safety by monitoring the status information of the vehicle in real time and immediately obtaining the vehicle speed, radar ranging and light sensing information when the status information is in a preset condition. In addition, according to the vehicle speed, surrounding environment and light conditions, the system can intelligently adjust the brightness, illumination angle and mode of the lights to provide a more comfortable driving environment for the driver.

[0017] In a second aspect, the present invention provides a vehicle control device, which includes: an acquisition module for acquiring driving information of the vehicle at a target time; wherein the driving information includes a driving position and a driving direction; a first determination module for determining a target position and a target direction corresponding to the target time from a path planning route according to the driving information; a second determination module for determining a position deviation value according to the driving position and the target position, and determining a direction deviation value according to the driving direction and the target direction; and an adjustment module for controlling the vehicle to adjust according to the position deviation value and the direction deviation value.

[0018] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor are communicatively connected to each other, computer instructions are stored in the memory, and the processor executes the vehicle control method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0019] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the vehicle control method of the first aspect or any corresponding embodiment thereof.

[0020] In a fifth aspect, the present invention provides a computer program product, comprising computer instructions for causing a computer to execute the vehicle control method of the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 is a schematic diagram of a vehicle control system according to an embodiment of the present invention;

[0023] Figure 2 is a flow chart of a vehicle control method according to an embodiment of the present invention;

[0024] Figure 3 is a structural block diagram of a vehicle control device according to an embodiment of the present invention;

[0025] Figure 4 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0027] Based on relevant technologies, autonomous driving technology, as a product of the integration of artificial intelligence and the automotive industry, has made significant progress in recent years and has gradually moved from the laboratory to real life, leading the transformation of future transportation. The basic principle of autonomous driving technology is to enable vehicles to drive safely and efficiently without human driver intervention by integrating multiple sensors (such as lidar, cameras, radar and ultrasonic sensors), high-performance computing platforms and advanced artificial intelligence algorithms. The data collected by these sensors is processed through fusion algorithms to generate an accurate 3D world model, providing the vehicle with all-round perception capabilities. On this basis, the autonomous driving system can plan the optimal driving path in real time and make accurate driving decisions, such as acceleration, deceleration, steering, lane change, etc.

[0028] In existing autonomous driving control systems, path planning is a crucial link. Path planning refers to the use of algorithms to determine the best driving path for a vehicle on the road to achieve safe and efficient driving. Specifically, the autonomous driving system will generate the optimal driving route from the starting point to the end point based on real-time road conditions and traffic rules to ensure that the vehicle can reach its destination safely and efficiently.

[0029] However, when the vehicle is driving along the planned route, the execution device in the vehicle may receive wrong information, causing the vehicle's driving route to deviate from the planned route. Such deviation may cause serious traffic accidents and cause immeasurable consequences.

[0030] Based on this, the present invention provides a vehicle control method, which obtains the driving information of the vehicle at the target time, including the driving position and driving direction, and determines the target position and target direction corresponding to the target time from the path planning route according to the driving information obtained in real time. Then, the system calculates the position deviation value between the current driving position and the target position, and the direction deviation value between the current driving direction and the target direction. These two deviation values ​​reflect the difference between the current state of the vehicle and the planned route. According to these deviation values, the vehicle is controlled to make corresponding adjustments so that it can return to the planned route, so that the deviation of the vehicle during driving can be monitored and corrected in real time, thereby effectively solving the problem that the vehicle may deviate from the planned route due to erroneous information.

[0031] Please refer to Figure 1 , Figure 1 is a schematic diagram of a vehicle control system provided according to an embodiment of the present invention.

[0032] The vehicle control system includes a driving assistance platform, a safety detection platform, a vehicle control platform, a power control platform and a signal transceiver platform.

[0033] The driving assistance platform assists in controlling the car's automatic driving through active control, early warning, and other auxiliary functions. The safety detection platform performs real-time safety detection on the car's automatic driving process; the vehicle control platform controls the car's automatic driving to drive safely along the planned route in real time; the power control platform controls the power installed in the car's automatic driving in real time; the signal transceiver platform receives the background driving planning information and sends the car's driving information to the background in real time.

[0034] The driving assistance platform includes an adaptive cruise control module, an automatic emergency braking module, an adaptive headlight module and an active adjustment headlight module. The adaptive cruise control module sets a following distance and then monitors the car in front in real time through a long-distance millimeter-wave radar to control the engine and brakes so that the vehicle and the front vehicle always maintain a set distance and drive smoothly. The automatic emergency braking module uses radar to detect the distance of the front vehicle and make an analysis, and issue an alarm according to different distances and speeds. If the driver does not respond after the alarm is displayed, when the safety distance is less than the specified range, AEB will be activated and automatically intervene in the car's braking system to reduce the probability of collision with the vehicle or person in front. The adaptive headlight module is aimed at the vehicle when following or meeting a vehicle, and the driver of the other vehicle is dazzled by the high beam. The collected signals include vehicle speed signals, radar ranging signals, light sensor signals, etc. The algorithm calculates the status of surrounding vehicles, including the vehicle light on status, vehicle speed, and vehicle driving direction. The headlight module actively adjusts the output control signal to the actuator to adjust the light type so that the light beam irradiating the vehicle in front is automatically closed.

[0035] The safety detection platform includes anti-lock braking module, electronic stability program module, brake assist module, traction control module, body stability control module, lane departure warning module, blind spot monitoring module and pedestrian detection and protection module. The anti-lock braking module prevents the wheels from locking and maintains the directional stability of the vehicle during emergency braking. The electronic stability program module helps the driver correct the vehicle's out-of-control state by controlling the engine torque and braking force. The brake assist module automatically increases the braking force and improves the braking effect during emergency braking. The traction control module limits the engine torque to prevent the vehicle from losing control when the drive wheel slips. The body stability control module maintains the vehicle's stable driving by controlling the engine torque, brakes and suspension systems. The lane departure warning module monitors whether the vehicle deviates from the lane and reminds the driver to pay attention. The blind spot monitoring module detects the blind spots on both sides of the vehicle and reminds the driver to pay attention to surrounding vehicles. The pedestrian detection and protection module automatically identifies pedestrians during driving and takes measures to avoid or reduce collision damage.

[0036] The vehicle control platform includes a travel control module, a sensor acquisition module, a driving control module, a peripheral acquisition module and a processing module. The travel control module controls the vehicle's travel speed and direction, the sensor acquisition module collects the vehicle's position information and vehicle environment information, the driving control module controls the vehicle's acceleration and deceleration, the peripheral acquisition module collects the peripheral road condition information of the vehicle's planned route, and the processing module receives and processes the background signals.

[0037] The travel control module is connected to the engine, transmission, energy source and wheels, the sensor acquisition module is connected to the positioning acquisition, inertial measurement, radar, laser rangefinder and camera, the driving control module is connected to the steering gear, throttle, brake steering, route controller, obstacle avoidance device and door controller, the peripheral acquisition module is connected to the optical communicator, on-board computer, microphone and speaker, and the processing module is connected to the command processor, memory and display.

[0038] The power control platform includes a power charging module, a power discharging module and a leakage detection module. The power charging module controls the charging of the battery in the car battery, the power discharging module controls the discharging of the battery in the car battery, and the leakage detection module detects whether the battery in the car battery is leaking.

[0039] According to an embodiment of the present invention, a vehicle control method embodiment is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0040] In this embodiment, a vehicle control method is provided, which can be used for the above-mentioned vehicle control system. Figure 2 is a flow chart of a vehicle control method according to an embodiment of the present invention, such as Figure 2 As shown, the process includes the following steps:

[0041] Step S201, obtaining the driving information of the vehicle at the target time; wherein the driving information includes the driving position and the driving direction.

[0042] The target time may represent the time at which the driving information of the vehicle is obtained. The target time may be any time during the driving process of the vehicle. The driving information refers to the driving position and driving direction of the vehicle at a certain time. The driving information may include the driving position and driving direction.

[0043] As an example, the driving position (latitude and longitude, altitude, etc.) and driving direction (angle of the vehicle's front direction) at the target time can be obtained through the vehicle's GPS positioning system, sensors, etc.

[0044] As an example, the driving direction can be calculated by using sensors inside the vehicle (such as a gyroscope, accelerometer) combined with an algorithm.

[0045] As an example, on-board cameras and image recognition technology can be used to identify road signs and lane lines to assist in determining the direction of travel.

[0046] Step S202: determining a target position and a target direction corresponding to a target time from a path planning route according to the driving information.

[0047] The path planning route can represent the optimal driving path of the vehicle from the starting point to the end point. Among them, the path planning route is a route that has been determined and is being executed. The actual driving information is compared with the path of the path planning route to find the ideal position (target position) and the ideal direction (target direction) that the vehicle should be facing at the target time.

[0048] As an example, based on the vehicle's speed and current position, the point closest to this time or distance is found in the path planning route as the target position. Finally, the direction between the target position and the adjacent path points is calculated, or the target direction is determined based on the tangent direction of the path curve.

[0049] As an example, curve fitting is performed on the path planning route to obtain the mathematical expression of the curve. Then, the time or driving distance required to reach the target time is calculated based on the vehicle's driving speed and current position. Next, the point corresponding to this time or distance is found on the curve as the target position. Finally, the tangent direction of the curve at the target position is obtained through differential calculation as the target direction.

[0050] Step S203, determining a position deviation value according to the driving position and the target position, and determining a direction deviation value according to the driving direction and the target direction.

[0051] The position deviation value refers to the physical distance or spatial offset between the current driving position of the vehicle and the target position. The position deviation value can be calculated by a variety of methods: the straight-line distance between the current position of the vehicle (such as GPS coordinates) and the target position can be calculated. Specifically, it may involve distance formulas in two-dimensional or three-dimensional space. If the path planning route is a curve, the current position of the vehicle can be projected onto the path curve, and then the distance between the projection point and the target position is calculated. In the path planning route, find the path point closest to the current position of the vehicle, and then calculate the distance between the path point and the target position. This method is applicable to the case of discrete path points, etc., and is not specifically limited here, and can be implemented by those skilled in the art.

[0052] The direction deviation value is the angular difference between the vehicle's current direction and the target direction. The direction deviation value can be calculated in a variety of ways: the vehicle's current direction and the target direction are represented as vectors, and then the angle between the two vectors is calculated. This usually involves the dot product and modulus of the vectors. If the path planning route is a curve, the tangent direction of the curve can be calculated at the target position and compared with the vehicle's current direction to obtain the direction deviation value. In the path planning route, find the path points that are closest to the vehicle's current position and the target position, respectively, and then calculate the angle between the direction of the line between the two path points and the vehicle's current direction.

[0053] As an example, the deviation distance is obtained by calculating the Euclidean distance between the current vehicle position and the corresponding point on the preset route: Where (x1, y1) is the position of the vehicle, (x2, y2) is the target position of the corresponding point on the preset route, and the deviation direction is obtained by calculating the angle difference between the current vehicle position and the corresponding point on the preset route. The calculation formula is: θ = arctan2(y2-y1, x2-x1), where (x1, y1) is the position of the vehicle, and (x2, y2) is the target position of the corresponding point on the preset route.

[0054] Step S204, controlling the vehicle to adjust according to the position deviation value and the direction deviation value.

[0055] In autonomous driving or vehicle navigation systems, position deviation values ​​and direction deviation values ​​are used as important indicators to evaluate the difference between the vehicle's current state and the target state. These deviation values ​​are obtained by comparing the vehicle's current driving position and driving direction with the target position and target direction. Once these deviation values ​​are calculated, the system needs to adjust the vehicle's driving trajectory based on these values ​​to make it closer to the target path.

[0056] As an example, the adjustment can be performed by controlling the vehicle based on PID. Specifically, the position deviation value and the direction deviation value are calculated. According to the PID control algorithm, the control instruction (such as steering angle, vehicle speed adjustment, etc.) is calculated. The control instruction is sent to the control system of the vehicle to perform the adjustment operation.

[0057] As an example, the adjustment can be made by controlling the vehicle based on a mathematical model. Specifically, a dynamic model and a road model of the vehicle are established. The future driving state is calculated based on the position deviation value and the direction deviation value, as well as the prediction model. The optimal control command is solved through the optimization algorithm. The control command is sent to the control system of the vehicle to perform the adjustment operation.

[0058] As an example, adjustments can be made by controlling the vehicle based on a neural network model. Specifically, the vehicle's historical driving data and current driving status are collected. Using machine learning algorithms, the data is analyzed and control instructions are optimized. The optimized control instructions are sent to the vehicle's control system to perform adjustment operations. Continuous monitoring and adjustment are performed to continuously optimize control performance.

[0059] As an example, the vehicle can be adjusted by controlling the vehicle based on multi-sensor fusion. Specifically, information from multiple sensors (such as position information, direction information, obstacle information, etc.) is collected. The sensor information is fused and processed to obtain more accurate vehicle status and surrounding environment information. Based on the fused information, control instructions are calculated. The control instructions are sent to the vehicle's control system to perform the adjustment operation.

[0060] The vehicle control method provided in this embodiment obtains the driving information of the vehicle at the target time, including the driving position and driving direction, and determines the target position and target direction corresponding to the target time from the path planning route according to the driving information obtained in real time. Then, the system calculates the position deviation value between the current driving position and the target position, and the direction deviation value between the current driving direction and the target direction. These two deviation values ​​reflect the difference between the current state of the vehicle and the planned route. According to these deviation values, the vehicle is controlled to make corresponding adjustments so that it can return to the planned route, so that the deviation of the vehicle during driving can be monitored and corrected in real time, thereby effectively solving the problem that the vehicle may deviate from the planned route due to erroneous information.

[0061] In a possible implementation, the process of determining a path planning route includes:

[0062] Step a1, obtaining the number of obstacles in the lane where the vehicle is located.

[0063] Obstacles refer to obstacles that affect the normal driving of vehicles. Among them, obstacles can be stationary obstacles, such as objects scattered on the road, fallen rocks, or cartons, etc.; they can also be non-stationary obstacles, such as vehicles traveling on the road, pedestrians crossing the road, etc., which are not specifically limited here. Specifically, sensors installed on the vehicle (such as radar, camera, lidar, etc.) are used to collect obstacle information in the lane. Obstacles are identified from sensor data through image processing or signal processing algorithms. The number of identified obstacles is counted and stored in the vehicle's control system.

[0064] Step a2: If the number of obstacles is not less than a preset number, a first target distance between two adjacent obstacles in front of the vehicle is obtained.

[0065] If the number of obstacles is not less than the preset number, the actual distance between two adjacent obstacles in front of the vehicle is calculated as the first target distance.

[0066] Step a3: If the first target distance is less than the preset distance, the outer contour points of two adjacent obstacles and the vehicle passage space of the vehicle are obtained.

[0067] If the first target distance is less than the preset distance, it indicates that the distance between the two obstacles cannot guarantee that the vehicle can return to the original lane after bypassing the obstacle. Then the sensor data can be used to obtain the external contour points of the two adjacent obstacles. Among them, the external contour points can represent the external contour points that can constitute the path planning route of the self-vehicle. According to the external contour points of the obstacle and the size of the self-vehicle, the passage space of the self-vehicle in the lane is calculated. The passage space of the self-vehicle can be a space to ensure the safe passage of the vehicle. Specifically, the passage space of the vehicle can be (vehicle width + safety distance × 2). Among them, the safety distance is the safe distance of the left side of the vehicle from the boundary of the lane and the safe distance of the right side of the vehicle from the obstacle. Specifically, it can be 40cm, 50cm, etc., and is not specifically limited here.

[0068] Step a4, based on the external contour points and the vehicle's passage space, a path planning route is generated within the lane where the vehicle is located.

[0069] Build a geometric model of the obstacle using the outer contour points. This may include polygons, circles, or other more complex shapes, depending on the complexity of the obstacle and the accuracy of the sensor. Determine the dynamic properties of the obstacle (such as velocity, acceleration, etc.). Define a search space within the ego vehicle's travel space to find possible paths. This space is usually a two-dimensional plane that contains all feasible areas within the lane. Choose an appropriate path planning algorithm, such as the A* algorithm, Dijkstra's algorithm, the fast random search tree (RRT) algorithm, or a modified version of it. Run the path planning algorithm using the search space, the starting point (the current position of the ego vehicle), and the target point (the position the ego vehicle wants to reach) as input. The algorithm will output an optimal path from the starting point to the target point that is within the search space and avoids all obstacles.

[0070] In a possible implementation, if the first target distance is greater than the preset distance, the vehicle may circumvent the obstacle closest to the vehicle, return to the center of the lane, and then circumvent the next obstacle.

[0071] The vehicle control method provided in this embodiment can more comprehensively understand the current road environment by obtaining the number of obstacles in the lane where the vehicle is located, and provide more accurate input information for subsequent path planning. At the same time, if the number of obstacles is large, the first target distance between two adjacent obstacles is further determined to identify narrow or crowded sections of the road, so as to make more reasonable path planning. In addition, by obtaining the outer contour points of two adjacent obstacles and the passage space of the vehicle, the passable path can be calculated more accurately to avoid collision with obstacles.

[0072] In a possible implementation, the above step a4 includes:

[0073] Step a41, generating the driving route of the vehicle based on the external contour points.

[0074] First, the external contour points are processed, which may include pre-processing steps such as denoising and smoothing to ensure the accuracy of the contour. A preliminary driving route is generated using a path planning algorithm (such as A*, Dijkstra, RRT, etc.) using these contour points as obstacle avoidance constraints. This route should avoid all obstacles as much as possible and stay within the lane. The preliminary generated driving route is optimized, which may include smoothing, reducing unnecessary turns, etc., to improve driving comfort and efficiency.

[0075] Step a42, determining multiple driving spaces of the vehicle based on the driving route and the boundary of the lane in which the vehicle is located.

[0076] Using lane detection algorithms (such as Hough transform, convolutional neural network, etc.), lane boundaries are identified. Based on the driving route and lane boundaries, the lane is divided into multiple possible driving spaces. These spaces should take into account factors such as the size of the vehicle, driving speed, turning radius, etc. to ensure that the vehicle can drive safely in each space. Each driving space is evaluated, including space size, obstacle density, traffic conditions, etc., to determine which spaces are feasible.

[0077] Step a43, detecting whether the plurality of driving spaces are smaller than the own vehicle's own vehicle passing space.

[0078] Based on the size of the ego vehicle, performance parameters (such as maximum turning radius, maximum acceleration, etc.) and road conditions, define a minimum space in which the ego vehicle can safely drive, namely the ego vehicle passage space. Compare each driving space with the ego vehicle passage space to determine whether it meets the ego vehicle's driving needs.

[0079] Step a44, if the multiple driving spaces are not smaller than the own vehicle's own vehicle passage space, a path planning route is generated within the lane where the own vehicle is located based on the driving route.

[0080] From multiple driving spaces that meet the conditions, an optimal space is selected based on certain evaluation criteria (such as spatial spaciousness, traffic flow, driving efficiency, etc.). The evaluation criteria can be defined and adjusted according to the actual situation to ensure that the selected space is both safe and efficient. Within the selected optimal driving space, the preliminary driving route is refined. The refinement process may include adjusting the curvature of the path, optimizing turning points, reducing unnecessary stops and starts, etc. to ensure the smoothness and continuity of the path. Advanced path planning algorithms (such as sampling-based algorithms, optimization algorithms, etc.) can be used to further optimize the path. After completing the path refinement and considering dynamic factors, the final path planning route is generated. This route should ensure that the vehicle can drive to the target location safely and efficiently while complying with traffic rules and avoiding all obstacles.

[0081] The vehicle control method provided in this embodiment can ensure that the path planning closely follows the obstacle contour while maintaining a certain safety distance to avoid collision by generating a driving route based on external contour points, and can ensure that the planned path will not cause safety problems due to insufficient space during actual driving by detecting whether multiple driving spaces are smaller than the vehicle's own passage space.

[0082] In a possible implementation, the method further includes:

[0083] Step b1, obtaining the position of the obstacle closest to the front of the vehicle and the position of the obstacle closest to the rear of the vehicle.

[0084] When the vehicle is driving automatically in the lane where the vehicle is located, it is necessary to detect the closest obstacles in front of and behind the vehicle. Among them, the closest obstacles in front of and behind the vehicle can be detected by sensors configured in the vehicle, or by other means, which are not specifically limited here and can be implemented by those skilled in the art. For example: there are obstacles A1, A2, and A3 in front of the vehicle, the distance between A1 and the vehicle is M1, the distance between A2 and the vehicle is M2, the distance between A3 and the vehicle is M3, and M1 is greater than M2 and greater than M3, then obstacle A3 is the closest obstacle in front of the vehicle.

[0085] Step b2, determining a second target distance according to the position of the vehicle and the position of the obstacle closest to the front of the vehicle.

[0086] Step b3, determining a third target distance according to the position of the vehicle and the position of the obstacle closest to the rear of the vehicle.

[0087] According to the current position of the vehicle and the position of the obstacle closest to the front of the vehicle, the distance between the two is calculated, that is, the second target distance. The second target distance is used to evaluate the driving safety of the obstacle in front of the vehicle. Similarly, according to the current position of the vehicle and the position of the obstacle closest to the rear of the vehicle, the distance between the two is calculated, that is, the third target distance. The third target distance is used to evaluate the driving safety of the rear obstacle to the vehicle.

[0088] Step b4, detecting whether the second target distance is within the first dangerous driving range.

[0089] The calculated second target distance is compared with a preset first dangerous driving range. If the second target distance is less than or equal to the range, it means that the obstacle ahead is too close and may pose a serious driving threat.

[0090] Step b5: if the second target distance is within the first dangerous driving range, an alarm prompt is generated and displayed to the user.

[0091] If the second target distance is within the first dangerous driving range, the system should immediately generate an alarm prompt and display the prompt to the user through a display screen or other means to attract the user's attention and take corresponding safety measures.

[0092] Step b6: If the second target distance is within the second dangerous driving range, detect whether the third target distance is within the safe driving range.

[0093] Step b7: If the third target distance is within the safe driving range, control the vehicle to brake.

[0094] If after the alarm is issued, no safety measures are taken to improve, or the safety measures are not effective in leaving the first dangerous driving range and entering the second dangerous driving range, the system needs to further check the third target distance. When it is confirmed that the second target distance is within the second dangerous driving range and the third target distance is within the safe driving range, in order to avoid potential collision risks, the system can automatically control the vehicle to brake.

[0095] The vehicle control method provided in this embodiment can detect potential collision risks in a timely manner by real-time monitoring the distance between the vehicle and the nearest obstacles in front and behind. When the second target distance (i.e., the distance between the vehicle and the obstacle in front) is in the first dangerous driving range, an alarm prompt will be immediately generated and displayed to the user, thereby reminding the user to take emergency braking or other avoidance measures. When the second target distance is in the second dangerous driving range and the third target distance (i.e., the distance between the vehicle and the obstacle behind) is in the safe driving range, the system will control the vehicle to brake to avoid rear-end collisions.

[0096] In a possible implementation, the method further includes:

[0097] Step c1: If the third target distance is not within the safe driving range, detect whether there are obstacles in the adjacent lanes.

[0098] When the system determines that the third target distance (i.e., the distance to the closest obstacle behind the vehicle) is not within the safe driving range, it means that the space behind the vehicle may not be sufficient for safe braking or evasive action. At this time, the system needs to detect whether there are other obstacles or vehicles in the adjacent lanes (usually the lanes on the left and right sides of the vehicle) to evaluate whether it is possible to avoid potential dangers by changing lanes.

[0099] Step c2: If there is no obstacle in the adjacent lane, control the vehicle to travel through the adjacent lane.

[0100] If the system detects that there are indeed no obstacles or vehicles in the adjacent lane, or the obstacles or vehicles are far enough away and will not pose a threat to the self-vehicle's lane change, the system can automatically control the self-vehicle to smoothly change to the adjacent lane. This can avoid collisions with obstacles that are too close to the rear, while maintaining the driving safety of the self-vehicle.

[0101] The vehicle control method provided in this embodiment provides a potential escape path for the vehicle by detecting whether the adjacent lane is safe when the distance between the vehicle and the rear obstacle is too close and there is a risk of rear-end collision. If there is no obstacle in the adjacent lane, the vehicle can change lanes safely, so that the vehicle can quickly identify and use the space in the adjacent lane in an emergency, thereby improving the emergency response and risk avoidance capabilities of the vehicle.

[0102] In a possible implementation, the method further includes:

[0103] Step d1, obtaining the vehicle's status information.

[0104] The state information of the vehicle may represent the relative relationship between the vehicle and other vehicles, for example, the vehicle and other vehicles are following or meeting each other, which is not specifically limited here.

[0105] Step d2, detecting whether the vehicle's status information is in the preset status information.

[0106] The preset state information can represent that the vehicle and other vehicles are following or meeting each other. Specifically, the state information of the vehicle can be detected by a sensor configured by the vehicle, and then whether the state information of the vehicle is in the preset state information.

[0107] Step d3, if the vehicle status information is in the preset status information, obtain vehicle speed information, radar ranging information, and light sensor information.

[0108] Once the vehicle status information is confirmed to be in the preset state, the system needs to obtain more detailed information to make further decisions. This information includes vehicle speed information (the current vehicle speed), radar ranging information (the distance between the vehicle and surrounding obstacles), and light sensor information (current light conditions, such as day, night, tunnel, etc.).

[0109] Step d4, determining and adjusting the light source status of the vehicle according to the vehicle speed information, radar ranging information and light sensing information.

[0110] The system comprehensively determines whether the vehicle's lighting status needs to be adjusted based on vehicle speed, radar distance measurement, and light sensor information. For example, if the vehicle is moving at a high speed and the surrounding light is dim, the system may decide to turn on the high beam to increase the lighting range; if the radar detects an obstacle ahead and the distance is close, the system may decide to reduce the brightness of the light source or switch to low beam to avoid dazzling other drivers.

[0111] As an example, a vehicle is traveling on a highway at a speed of 120 km / h. The radar detects that there is no obstacle ahead, or the obstacle is far away (such as more than 200 meters). The light sensor detects that the current environment is night and the surrounding light is dim. It is judged that the current environment needs to improve the lighting range, so the high beam is automatically turned on to provide a better view ahead.

[0112] As an example, a vehicle is traveling at 60 km / h on a city road. The radar detects that there is a vehicle ahead and the distance is relatively close (e.g., about 50 meters). The light sensor detects that it is evening and the sky is getting dark but not completely dark. It determines that the current environment needs to avoid dazzling other drivers, so it automatically switches to low beam and adjusts the light brightness according to the vehicle speed and radar ranging information to ensure safe driving.

[0113] As an example, a vehicle is driving in a tunnel at a speed of 80 km / h. The radar detects that there is no obstacle ahead or the obstacle is far away. The light sensor detects that the current environment is a tunnel and the light is very dim. It is determined that the current environment needs to increase the lighting brightness, so the tunnel mode light is automatically turned on. Usually, the light brightness in this mode is higher than that in normal night driving to cope with the lack of light in the tunnel.

[0114] The vehicle control method provided in this embodiment can quickly respond to road and weather changes, adjust the light source status in time, and ensure driving safety by monitoring the status information of the vehicle in real time and immediately obtaining the vehicle speed, radar ranging and light sensing information when the status information is in a preset condition. In addition, according to the vehicle speed, surrounding environment and light conditions, the system can intelligently adjust the brightness, illumination angle and mode of the lights to provide a more comfortable driving environment for the driver.

[0115] In this embodiment, a vehicle control device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0116] This embodiment provides a vehicle control device, such as Figure 3 As shown, it includes: an acquisition module 301, which is used to acquire the driving information of the vehicle at the target time; wherein the driving information includes the driving position and the driving direction; a first determination module 302, which is used to determine the target position and the target direction corresponding to the target time from the path planning route according to the driving information; a second determination module 303, which is used to determine the position deviation value according to the driving position and the target position, and determine the direction deviation value according to the driving direction and the target direction; an adjustment module 304, which is used to control the vehicle to adjust according to the position deviation value and the direction deviation value.

[0117] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0118] The vehicle control device in this embodiment is presented in the form of a functional unit, where the functional unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0119] The embodiment of the present invention also provides a computer device having the above Figure 3 Vehicle controls shown.

[0120] See also Figure 4 , Figure 4 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 4As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 4 A processor 10 is taken as an example.

[0121] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0122] The memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiment.

[0123] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0124] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0125] The computer device further includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means.

[0126] The input device 30 can receive input digital or character information, and generate key signal input related to the user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display and a plasma display. In some optional embodiments, the display device can be a touch screen.

[0127] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0128] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0129] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A vehicle control method, characterized in that: The method comprises: Acquire the driving information of the vehicle at the target time; wherein the driving information includes the driving position and the driving direction; Determine the target position and target direction corresponding to the target time from the path planning route according to the driving information; Determining a position deviation value according to the driving position and the target position, and determining a direction deviation value according to the driving direction and the target direction; The vehicle is controlled to make adjustments according to the position deviation value and the direction deviation value.

2. The vehicle control method according to claim 1, characterized in that: The process of determining the path planning route includes: Get the number of obstacles in the lane where the vehicle is located; If the number of obstacles is not less than a preset number, obtaining a first target distance between two adjacent obstacles in front of the vehicle; If the first target distance is less than the preset distance, the outer contour points of two adjacent obstacles and the ego vehicle passage space are obtained; Based on the external contour points and the vehicle's passage space, a path planning route is generated within the lane where the vehicle is located.

3. The vehicle control method according to claim 2, characterized in that: Based on the external contour points and the vehicle passage space, a path planning route is generated within the lane where the vehicle is located, including: generating a driving route of the vehicle based on the external contour points; Determining a plurality of driving spaces of the vehicle based on the driving route and the boundary of the lane in which the vehicle is located; Detecting whether the plurality of driving spaces are smaller than the own vehicle's own vehicle passing space; If the multiple driving spaces are not smaller than the vehicle passage space of the vehicle, a path planning route is generated within the lane where the vehicle is located based on the driving route.

4. The vehicle control method according to claim 1, characterized in that: The method further comprises: Obtain the position of the obstacle closest to the front of the vehicle and the position of the obstacle closest to the rear of the vehicle; Determining a second target distance according to the position of the vehicle and the position of the obstacle closest to the front of the vehicle; Determining a third target distance according to the position of the vehicle and the position of the obstacle closest to the rear of the vehicle; detecting whether the second target distance is within a first dangerous driving range; If the second target distance is within the first dangerous driving range, generating an alarm prompt, and displaying the alarm prompt to the user; If the second target distance is within a second dangerous driving range, detecting whether the third target distance is within a safe driving range; If the third target distance is within the safe driving range, the vehicle is controlled to brake.

5. The vehicle control method according to claim 4, characterized in that: The method further comprises: If the third target distance is not within the safe driving range, detecting whether there is an obstacle in an adjacent lane; If there are no obstacles in the adjacent lane, control the vehicle to travel through the adjacent lane.

6. The vehicle control method according to claim 1, characterized in that: The method further comprises: Get the status information of the vehicle; Detecting whether the state information of the vehicle is in preset state information; If the state information of the vehicle is in the preset state information, obtaining vehicle speed information, radar ranging information, and light sensor information; According to the vehicle speed information, the radar ranging information and the light sensing information, it is determined to adjust the light source state of the vehicle.

7. A vehicle control device, characterized in that: The device comprises: An acquisition module, used to acquire the driving information of the vehicle at the target time; wherein the driving information includes the driving position and the driving direction; A first determination module is used to determine a target position and a target direction corresponding to a target time from a path planning route according to the driving information; A second determination module, used to determine a position deviation value according to the driving position and the target position, and to determine a direction deviation value according to the driving direction and the target direction; The adjustment module is used to control the vehicle to adjust according to the position deviation value and the direction deviation value.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the vehicle control method according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the vehicle control method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the vehicle control method according to any one of claims 1 to 6.

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