Self-adaptive cruise control method, device, equipment and storage medium

Abstract

The invention discloses a self-adaptive cruise control method, device and equipment and a storage medium, and relates to the technical field of automobile automation, and the self-adaptive cruise control method comprises the steps: obtaining sensor data and a preset boundary threshold; determining a driving scene state according to the sensor data and the preset boundary threshold; and determining a target vehicle according to the driving scene state, so that the vehicle performs adaptive cruise control according to the target vehicle. According to the adaptive cruise control method for reselecting the vehicle following target, the misrecognition condition when the lane line is not clear is reduced, and the accuracy of adaptive cruise control is improved, so that the driving safety and comfort are enhanced.

Description

Technical Field

[0001] The present application relates to the field of automobile automation technology, and in particular to an adaptive cruise control method, device, equipment and storage medium. Background Art

[0002] With the development of automobile automation technology, adaptive cruise control (ACC) has become one of the key technologies to improve driving safety and comfort. The ACC system can automatically adjust the vehicle speed to maintain a safe distance from the vehicle in front, reducing the driver's operating burden. Under ideal conditions, the ACC system relies on clear lane lines to predict the vehicle's driving path and select the target vehicle to follow.

[0003] Existing adaptive cruise control methods mainly use lane information collected by vehicle sensors to determine the vehicle's driving state and predict the path. These systems usually work well on structured roads, using lane information to assist in determining the vehicle's driving intention and selecting the target to follow. However, when the vehicle enters a road section without lane lines or with unclear lane lines, the performance of these systems will drop significantly.

[0004] On roads without lane lines or with unclear lane lines, existing ACC systems may lose the ability to accurately predict the vehicle's path, resulting in an inability to correctly select the vehicle to follow. This may cause the steering wheel's predicted trajectory to inaccurately judge the driver's intentions, increasing the risk of misselecting a passing vehicle in the adjacent lane, thereby affecting driving safety. In addition, systems that rely on information about surrounding vehicles may frequently adjust acceleration and deceleration in densely populated areas due to the influence of different vehicle speeds and heading angles, which may not only affect the identification of individual dangerous vehicles, but also reduce driving comfort. Therefore, how to accurately predict the vehicle's path in scenarios with unclear lane lines has become an urgent problem to be solved. Summary of the invention

[0005] The purpose of this application is to provide an adaptive cruise control method, device, equipment and storage medium, aiming to solve the technical problem of how to accurately predict the vehicle's driving path in a scenario where lane lines are unclear.

[0006] To achieve the above objectives, the present application proposes an adaptive cruise control method, the method comprising:

[0007] Get sensor data and preset boundary thresholds;

[0008] Determining a driving scene state according to the sensor data and the preset boundary threshold;

[0009] A target vehicle is determined according to the driving scene state, so that the vehicle performs adaptive cruise control according to the target vehicle.

[0010] In one embodiment, determining the driving scene state according to the sensor data and the preset boundary threshold includes:

[0011] Get the preset lane width threshold;

[0012] Obtain lane line data and road boundary data according to the sensor data;

[0013] Obtaining a current lane width based on the lane line data;

[0014] If the current lane width is greater than the preset lane line width threshold, the driving scene state is determined based on the road boundary data and the preset boundary threshold.

[0015] In one embodiment, determining the driving scene state based on the road boundary data and the preset boundary threshold includes:

[0016] Obtaining a target distance between the center line of the vehicle and the road boundary line according to the road boundary data;

[0017] If the target distance is greater than or equal to the preset boundary threshold, the driving scene state is driving not in line with the road edge;

[0018] If the target distance is less than the preset boundary threshold, the driving scene state is driving close to the edge of the road.

[0019] In one embodiment, if the target distance is greater than or equal to the preset boundary threshold, then after the driving scene state is driving not in close contact with the road edge, the method further includes:

[0020] Acquire first reference vehicle data of a traveling direction;

[0021] Based on the kinematic trajectory prediction model, the sensor data and the first reference vehicle data, a first target vehicle trajectory and a first predicted ego vehicle trajectory are obtained;

[0022] If the first target vehicle trajectory matches the first predicted ego vehicle trajectory, determining the first target vehicle according to the first target vehicle trajectory;

[0023] Adaptive cruise control is accomplished through the first target vehicle and a preset following distance.

[0024] In one embodiment, if the target distance is less than the preset boundary threshold, the driving scene state is after driving close to the edge of the road, including:

[0025] Get the preset distance threshold;

[0026] Identify the lane line in the travel direction through the lane line data;

[0027] If the lane line in the direction of travel is a single lane line and is continuous, obtaining a first compensation lane line based on the lane line in the direction of travel;

[0028] When a cut-in vehicle is detected based on the first compensation lane line, taking the cut-in vehicle as a second target vehicle;

[0029] Obtaining a target lateral distance based on the second target vehicle and the first compensation lane line;

[0030] When the target lateral distance is less than the preset distance threshold, deceleration control is performed to complete the adaptive cruise control.

[0031] In one embodiment, after identifying the lane line in the travel direction through the lane line data, the method further includes:

[0032] If the lane line in the direction of travel is a single lane and is interrupted, a second compensation lane line is obtained according to the current lane line and the lane line in the direction of travel;

[0033] When a cut-in vehicle is detected based on the second compensation lane line, the cut-in vehicle is taken as a third target vehicle, and a lateral speed and a lateral collision time of the third target vehicle are obtained;

[0034] If the lateral speed is greater than a preset lateral speed threshold and the lateral collision time is less than a preset time threshold, deceleration control is performed to complete the adaptive cruise control;

[0035] If the lane line in the traveling direction is a curve, obtaining second reference vehicle data in the traveling direction;

[0036] Based on the kinematic trajectory prediction model, the sensor data and the second reference vehicle data, obtaining a second target vehicle trajectory and a second predicted ego vehicle trajectory;

[0037] If the second target vehicle trajectory matches the second predicted self-vehicle trajectory, a fourth target vehicle is determined according to the second target vehicle trajectory, and adaptive cruise control is completed through the fourth target vehicle and a preset following distance.

[0038] In one embodiment, if the current lane width is greater than the preset lane line width threshold, after determining the driving scene state based on the road boundary data and the preset boundary threshold, the method further includes:

[0039] Performing edge detection on the lane line data to obtain target lane line data;

[0040] Determining a target lane line state based on the target lane line data;

[0041] If the target lane line state is unclear, the target vehicle is determined according to the sensor data.

[0042] In addition, to achieve the above-mentioned purpose, the present application also proposes an adaptive cruise control device, the device comprising:

[0043] An acquisition module, used to acquire sensor data and preset boundary thresholds;

[0044] A determination module, used to determine the driving scene state according to the sensor data and the preset boundary threshold;

[0045] The completion module is used to determine the target vehicle according to the driving scene state, so that the vehicle can perform adaptive cruise control according to the target vehicle.

[0046] In addition, to achieve the above-mentioned purpose, the present application also proposes an adaptive cruise control device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the adaptive cruise control method described above.

[0047] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the adaptive cruise control method described above are implemented.

[0048] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the adaptive cruise control method described above are implemented.

[0049] One or more technical solutions proposed in this application have at least the following technical effects:

[0050] This application first collects real-time information about the vehicle's surroundings, including key data such as lane lines and road boundaries, and combines it with preset boundary thresholds to provide the basic data required for decision-making. Then, using the data collected by the sensor and the preset boundary threshold, it can accurately identify the current driving environment and determine whether the vehicle is in a state of driving close to the edge of the road or not. Finally, after clarifying the driving scene state, it can more accurately identify and select suitable following targets to provide accurate reference vehicles for adaptive cruise control. This application can accurately determine the driving scene state by acquiring and analyzing sensor data in real time, combined with preset boundary thresholds, so as to effectively identify and select following targets. This method can improve the accuracy and reliability of adaptive cruise control, especially in scenarios where lane lines are unclear or there are no lane lines, by intelligently judging the relationship between the vehicle and the road boundary, optimizing the following strategy, and ensuring driving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0052] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0053] Figure 1 A flowchart of the first embodiment of the adaptive cruise control method of the present application is provided;

[0054] Figure 2 A flowchart diagram of the second embodiment of the adaptive cruise control method of the present application;

[0055] Figure 3 A brief flowchart of the adaptive cruise control method according to an embodiment of the present application;

[0056] Figure 4 This is a schematic diagram of the module structure of the adaptive cruise control device according to an embodiment of the present application;

[0057] Figure 5 Schematic diagram of the device structure of the hardware operating environment involved in the adaptive cruise control method in the embodiment of the present application.

[0058] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0059] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0060] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0061] With the development of automobile automation technology, adaptive cruise control (ACC) has become one of the key technologies to improve driving safety and comfort. The ACC system can automatically adjust the vehicle speed to maintain a safe distance from the vehicle in front and reduce the driver's operating burden. Under ideal conditions, the ACC system relies on clear lane lines to predict the vehicle's driving path and select the target to follow. Existing adaptive cruise control methods mainly determine the vehicle's driving state and predicted path through lane line information collected by vehicle sensors. These systems usually work well on structured roads, using lane line information to assist in judging the vehicle's driving intention and selecting the target to follow. However, the performance of these systems will significantly decrease when the vehicle enters a section without lane lines or unclear lane lines. On sections without lane lines or unclear lane lines, the existing ACC system may lose the ability to accurately predict the vehicle's driving path, resulting in the inability to correctly select the target to follow. This may cause the steering wheel's predicted trajectory to inaccurately judge the driver's intention, increase the risk of misselecting a passing vehicle in the adjacent lane, and thus affect driving safety. In addition, systems that rely on information from surrounding vehicles may frequently adjust acceleration and deceleration due to different vehicle speeds and heading angles in densely packed traffic conditions, which may not only affect the identification of individual dangerous vehicles but also reduce driving comfort.

[0062] The main solution of the embodiment of the present application is: the embodiment first collects real-time information of the vehicle's surrounding environment, including key data such as lane lines and road boundaries, and combines the preset boundary threshold to provide the basic data required for decision-making. Then, using the data collected by the sensor and the preset boundary threshold, the current driving environment can be accurately identified to determine whether the vehicle is in a state of driving close to the edge of the road or not in a state of driving close to the edge of the road. Finally, after clarifying the driving scene state, it can more accurately identify and select a suitable following target, and provide an accurate reference vehicle for adaptive cruise control. The embodiment can accurately determine the driving scene state by acquiring and analyzing sensor data in real time, combined with the preset boundary threshold, so as to effectively identify and select the following target. This method can improve the accuracy and reliability of adaptive cruise control, especially in the scene where the lane line is unclear or there is no lane line, by intelligently judging the relationship between the vehicle and the road boundary, optimizing the following strategy, and ensuring driving safety.

[0063] It should be noted that the execution subject of the embodiment of the present application may be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, an electronic control unit, etc. The following takes the electronic control unit as an example to illustrate this embodiment and the following embodiments.

[0064] Based on this, the embodiment of the present application provides an adaptive cruise control method, referring to Figure 1 , Figure 1 This is a flowchart of the first embodiment of the adaptive cruise control method of the present application.

[0065] In this embodiment, the adaptive cruise control method includes steps S10 to S30:

[0066] Step S10, obtaining sensor data and a preset boundary threshold;

[0067] It should be noted that sensor data can be real-time environmental information collected by various sensors on the vehicle, which is crucial for the adaptive cruise control (ACC) system. These sensors may include but are not limited to: radar sensors for detecting the distance and speed of the vehicle in front; cameras for identifying lane lines, road signs, traffic signals and surrounding vehicles; laser radar (LiDAR) for providing high-precision distance measurement and three-dimensional environmental mapping; infrared sensors for detecting objects in front at night or in bad weather conditions; vehicle dynamic sensors for collecting vehicle dynamic information such as speed, acceleration, and steering angle. The preset boundary threshold can be a series of parameter values ​​preset according to safety and operating standards during design for judgment and decision-making.

[0068] It is understandable that real-time road and environmental information, such as lane lines, road boundaries, the position and speed of surrounding vehicles, etc., are collected through the sensors installed in the vehicle, and reference is made to pre-set safety and operating parameters (such as lane width thresholds, distance thresholds, etc.) to provide the required data support for adaptive cruise control decision-making and control, ensuring correct responses and adjustments in various driving scenarios.

[0069] Step S20, determining the driving scene state according to the sensor data and the preset boundary threshold;

[0070] It should be noted that the driving scene state can be the specific road environment and driving conditions of the vehicle obtained by analyzing the sensor data and the preset boundary threshold. These states may include: road boundary fit state, the position of the vehicle relative to the road boundary (such as the lane line or the curb), judging whether the vehicle is driving close to the edge of the road or deviating from the edge of the road; driving environment complexity, judging the complexity of the driving environment based on sensor data, for example, whether it is at an intersection, a curve, a traffic congestion area or other special circumstances. Determining the driving scene state directly affects how to respond to environmental changes, how to select an appropriate target to follow, and how to adjust the speed and distance to maintain safe and comfortable driving.

[0071] It can be understood that by utilizing real-time data collected by vehicle sensors, such as lane lines, road boundaries, the position and speed of surrounding vehicles, etc., combined with pre-set safety and operating parameters, the vehicle's current road environment and driving conditions are analyzed and judged, thereby identifying whether the vehicle is driving in a standard lane, close to the edge of the road, or in specific scenarios such as no lane lines, so that corresponding adjustments and responses can be made.

[0072] Step S30, determining a target vehicle according to the driving scene state, so that the vehicle performs adaptive cruise control according to the target vehicle.

[0073] It should be noted that the target vehicle can be determined based on the analysis of sensor data and preset boundary thresholds, and is used as a reference for cruise control of the ego vehicle (i.e., a vehicle equipped with an ACC system). The selection of the target vehicle is based on the following factors: safe distance, the distance between the target vehicle and the ego vehicle should be within a safe range to ensure sufficient reaction time and braking distance in an emergency; the same lane, the target vehicle should be in the same lane as the ego vehicle or in a parallel lane so that the ego vehicle can follow and maintain driving within the lane; driving intention, the driving intention of the target vehicle and the driving intention of the ego vehicle should be consistent, such as when going straight at an intersection, the target vehicle should also have the intention to go straight; dynamic changes, the selection of the target vehicle should also consider its dynamic changes, such as vehicles cutting in and out of the lane, and those that may affect the driving safety of the ego vehicle. By monitoring and evaluating these factors in real time, the target vehicle is dynamically selected or replaced to ensure that the ego vehicle can drive safely, and the speed and distance of the ego vehicle are automatically adjusted according to the driving status of the target vehicle to achieve intelligent cruise control.

[0074] It can be understood that by analyzing the driving scene status information collected by the sensor, such as lane width, relative position of the vehicle and the road boundary, etc., a suitable preceding vehicle is intelligently identified and selected as the target vehicle. Subsequently, the speed of the vehicle will be adjusted according to the speed and position of the target vehicle to maintain a safe distance and perform adaptive cruise control to ensure driving comfort and safety.

[0075] This embodiment provides an adaptive cruise control method. This embodiment first collects real-time information about the vehicle's surrounding environment, including key data such as lane lines and road boundaries, and combines a preset boundary threshold to provide basic data required for decision-making. Then, using the data collected by the sensor and the preset boundary threshold, the current driving environment can be accurately identified to determine whether the vehicle is in a state of driving close to the edge of the road or not in a state of driving close to the edge of the road. Finally, after clarifying the driving scene state, it is possible to more accurately identify and select a suitable following target, and provide an accurate reference vehicle for adaptive cruise control. This embodiment can accurately determine the driving scene state by acquiring and analyzing sensor data in real time, combined with a preset boundary threshold, so as to effectively identify and select the following target. This method can improve the accuracy and reliability of adaptive cruise control, especially in the scene where the lane line is unclear or there is no lane line, by intelligently judging the relationship between the vehicle and the road boundary, optimizing the following strategy, and ensuring driving safety.

[0076] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can refer to the above introduction, and will not be repeated later. Figure 2 , Figure 2 This is a flow chart of the second embodiment of the adaptive cruise control method of the present application. Step S20 of the adaptive cruise control method includes steps S21 to S24:

[0077] Step S21, obtaining a preset lane line width threshold;

[0078] It should be noted that the preset lane line width threshold may be a value pre-set in the adaptive cruise control system to define the standard width range of the lane line. This threshold is based on road design standards and regulatory requirements and is used to determine whether the currently detected lane line meets the expected width.

[0079] It can be understood that a numerical range is pre-set according to road design standards and regulatory requirements to identify and verify whether the lane line detected by the sensor is within the normal width range, so as to ensure subsequent scene judgment and select the appropriate following target through the judged scene.

[0080] Step S22, obtaining lane line data and road boundary data according to the sensor data;

[0081] It should be noted that lane line data can be information such as the position, type (solid or dashed), color, etc. of lane lines detected by sensors, and can include geometric features of lane lines, such as lane line width, continuity, and relationship with other lane lines, which are used to determine the position of the vehicle in the lane and predict the movement trajectory of the vehicle in the lane. Road boundary data can be the position of the edge or boundary of the road identified by the sensor, such as curbs, obstacles on both sides of the road, or road signs, including geometric features of the road boundary, such as the straight or curved shape of the boundary, and the relationship with the lane line, which is used to assist in determining the position of the vehicle relative to the road. In particular, when the lane line is unclear or missing, road boundary data is crucial for judging the vehicle's driving intention and safety boundary.

[0082] It can be understood that through sensors on the vehicle, such as cameras, radars or lidars, information such as the position, type and continuity of lane lines, as well as data such as the position and characteristics of road edges or boundaries are collected. This information together constitutes lane line data and road boundary data, providing the vehicle's precise position on the road and detailed information about the surrounding environment, thereby helping to make accurate judgments and control decisions in various driving scenarios.

[0083] Step S23, obtaining the current lane width based on the lane line data;

[0084] It should be noted that the current lane width can be the actual distance between lane lines detected by sensor data, that is, the distance from one lane line to the other lane line. This width data is a key parameter used in adaptive cruise control systems to assess road conditions and vehicle positions.

[0085] It is understandable that by analyzing the lane line position information detected by the sensor, the actual distance between the two lane lines, that is, the width of the current lane, is measured and calculated. This measurement result is crucial for evaluating road conditions, judging whether the lane lines are clear, and assisting vehicle positioning and path planning.

[0086] Step S24: If the current lane width is greater than the preset lane line width threshold, the driving scene state is determined based on the road boundary data and the preset boundary threshold.

[0087] It is understandable that when the lane width measured by the sensor exceeds the preset standard lane width threshold, the road boundary data and the corresponding preset boundary threshold will be used to evaluate the vehicle's position relative to the road boundary, so as to determine whether the vehicle is driving close to the edge of the road or in other driving scenarios, so as to make corresponding control decisions, such as adjusting the vehicle speed or warning the driver.

[0088] As an example, if the current lane width is greater than the preset lane line width threshold, then based on the road boundary data and the preset boundary threshold, after determining the driving scene state, it also includes: performing edge detection on the lane line data to obtain target lane line data; based on the target lane line data, determining the target lane line state; if the target lane line state is unclear, determining the target vehicle based on the sensor data.

[0089] The target lane line data may be detailed data obtained after edge detection of the lane line by a sensor such as a camera, including information such as the precise position, shape and continuity of the lane line. Edge detection may be an image processing technology used to identify the edge of the lane line in an image, thereby extracting the exact position of the lane line. The target lane line data provides the necessary information so that the system can understand the position of the vehicle in the lane and the geometric characteristics of the lane. The target lane line state may be an assessment of the current condition of the lane line based on the target lane line data, including the clarity, visibility and integrity of the lane line. The state assessment involves determining whether the lane line is clear and identifiable, or whether it is unclear due to wear, occlusion, damage or other factors. The target lane line state determines whether the lane line data can be relied upon to predict the vehicle's driving path and control the vehicle's driving state.

[0090] Specifically, the precise position and shape of the lane line are first extracted from the sensor data through edge detection technology to form the target lane line data; then, these data are analyzed to evaluate the clarity and status of the lane line to determine whether the lane line is clearly visible; if the lane line status is judged to be unclear, the lane line data cannot be relied upon to select the target vehicle to follow. At this time, other sensor data, such as radar or camera information, will be used to determine the target vehicle in front to maintain the continuity and safety of adaptive cruise control.

[0091] As an example, based on the road boundary data and the preset boundary threshold, the driving scene state is determined, including: obtaining the target distance between the center line of the vehicle and the road boundary line according to the road boundary data; if the target distance is greater than or equal to the preset boundary threshold, the driving scene state is not driving in line with the edge of the road; if the target distance is less than the preset boundary threshold, the driving scene state is driving in line with the edge of the road.

[0092] The target distance can be the distance between the center line of the vehicle and the nearest road boundary line measured by the sensor. This measurement value reflects the position of the vehicle relative to the edge of the road and is a key parameter for judging the vehicle's driving state and driving scene. Specifically: the center line of the vehicle can be the longitudinal center line of the vehicle, which can be regarded as the axis of symmetry of the vehicle; the road boundary line can be a line on the edge of the road, which can be a lane line, a curb, obstacles on both sides of the road, or other forms of road boundary markings.

[0093] Specifically, the distance between the center line of the vehicle and the nearest road boundary line, i.e., the target distance, is measured by analyzing the road boundary data. If this target distance reaches or exceeds the preset boundary threshold, the vehicle will be judged as not driving close to the road edge; on the contrary, if the target distance is less than the preset boundary threshold, the vehicle will be judged as driving close to the road edge. Such judgment helps the system adjust the driving strategy according to the position of the vehicle relative to the road edge to ensure safe and effective adaptive cruise control.

[0094] As an example, if the target distance is greater than or equal to the preset boundary threshold, the driving scene state is that the driving does not follow the edge of the road, and further includes: obtaining first reference vehicle data of the direction of travel; obtaining a first target vehicle trajectory and a first predicted self-vehicle trajectory based on a kinematic trajectory prediction model, the sensor data and the first reference vehicle data; if the first target vehicle trajectory and the first predicted self-vehicle trajectory match, determining the first target vehicle according to the first target vehicle trajectory; and completing adaptive cruise control through the first target vehicle and the preset following distance.

[0095] Among them, the first reference vehicle data can be relevant information of vehicles (i.e., the front vehicle) in all directions of travel, including dynamic data such as its speed, acceleration, position, and driving direction. These data are used to help the system predict the driving trajectory of the front vehicle and adjust the speed and driving path of the vehicle accordingly. The kinematic trajectory prediction model can be a mathematical model used to predict the driving trajectory of the vehicle in the future based on the dynamic data of the vehicle (such as speed, acceleration, steering angle, etc.). The model takes into account the physical motion characteristics of the vehicle, can simulate the actual movement of the vehicle on the road, and help predict the driving path and behavior of the vehicle. The first target vehicle trajectory can be the driving path of the front vehicle predicted based on the first reference vehicle data. The first predicted self-vehicle trajectory can be the driving path of the self-vehicle predicted based on the current speed, acceleration, and other sensor data of the self-vehicle. The two trajectories are matched to determine the relative position and motion relationship between the self-vehicle and the front vehicle. The first target vehicle can be a target vehicle for adaptive cruise control determined based on the matching result of the first target vehicle trajectory and the first predicted self-vehicle trajectory, and the speed of the self-vehicle is adjusted according to the position and driving state of the target vehicle to maintain a safe distance. The preset following distance can be a pre-set safety distance used to maintain a space gap between the vehicle and the vehicle in front. This distance is usually determined based on factors such as safety standards, traffic regulations, road conditions and vehicle performance to ensure sufficient braking distance in an emergency.

[0096] Specifically, the data of the reference vehicle (i.e., the vehicle in front) located in the direction of the vehicle's travel is first collected, including its speed, position, and other information. Then, the kinematic trajectory prediction model is used to combine the sensor data and the first reference vehicle data to calculate the expected driving trajectory of the first target vehicle (the vehicle in front) and the predicted driving trajectory of the vehicle. If the two trajectories match in the system, it means that the vehicle in front is confirmed as the target vehicle, and the speed of the vehicle will be automatically adjusted according to the position of the target vehicle and the preset safe following distance to maintain an appropriate vehicle distance and complete adaptive cruise control.

[0097] As an example, if the target distance is less than the preset boundary threshold, the driving scene state is after driving close to the edge of the road, including: obtaining a preset distance threshold; identifying the lane line in the direction of travel through the lane line data; if the lane line in the direction of travel is a single lane line and continuous, obtaining a first compensation lane line based on the lane line in the direction of travel; when a cutting-in vehicle is detected based on the first compensation lane line, taking the cutting-in vehicle as the second target vehicle; obtaining a target lateral distance based on the second target vehicle and the first compensation lane line; when the target lateral distance is less than the preset distance threshold, performing deceleration control to complete adaptive cruise control.

[0098] Among them, the preset distance threshold may be a pre-set safety distance value, which is used to determine the minimum lateral distance that should be maintained between the ego vehicle and the cut-in vehicle. When the actually measured lateral distance is less than this threshold, the deceleration control will be triggered to avoid potential collision. The lane line in the direction of travel may be the lane line in the current direction of travel of the ego vehicle, and these lane lines are identified through sensor data to determine the position of the ego vehicle in the lane and the boundary of the lane. The first compensation lane line may be a virtual compensation lane line generated based on these lane lines when the lane line in the direction of travel is a single lane line and continuous. This compensation lane line extends the boundary of the actual lane line and is used to assist in determining the driving path when the lane line is unclear or missing. The second target vehicle may refer to a vehicle detected to cut into the lane where the ego vehicle is located from an adjacent lane during the travel of the ego vehicle. These cut-in vehicles are regarded as target vehicles that may affect the driving safety of the ego vehicle and need special attention and processing. The target lateral distance may be the lateral distance between the ego vehicle and the cut-in vehicle (the second target vehicle), that is, the distance between the two vehicles in the lane width direction. This distance is an important parameter for evaluating the relative position of the two vehicles and determining whether avoidance measures need to be taken.

[0099] Specifically, a preset distance threshold is first set as the lateral distance standard for safe driving, and the lane lines in the direction of travel are identified by analyzing the lane line data. When these lane lines are detected as single lane lines and continuous, an extended virtual lane, namely the first compensation lane line, is generated based on these lane lines to assist in vehicle positioning. When a vehicle cuts into this compensation lane line, the vehicle is identified as the second target vehicle, and then the target lateral distance between the vehicle and the second target vehicle is calculated, that is, the lateral distance between the two vehicles in the lane. If this lateral distance is less than the preset safety distance threshold, deceleration control will be automatically performed to maintain a safe distance and complete adaptive cruise control to ensure driving safety.

[0100] As an example, after the lane line in the direction of travel is identified through the lane line data, it also includes: if the lane line in the direction of travel is a single lane and interrupted, a second compensation lane line is obtained based on the current lane line and the lane line in the direction of travel; when a cutting-in vehicle is detected based on the second compensation lane line, the cutting-in vehicle is used as a third target vehicle, and the lateral speed and lateral collision time of the third target vehicle are obtained; if the lateral speed is greater than a preset lateral speed threshold and the lateral collision time is less than a preset time threshold, deceleration control is performed to complete adaptive cruise control; if the lane line in the direction of travel is a curve, the second reference vehicle data in the direction of travel is obtained; based on the kinematic trajectory prediction model, the sensor data and the second reference vehicle data, a second target vehicle trajectory and a second predicted self-vehicle trajectory are obtained; if the second target vehicle trajectory matches the second predicted self-vehicle trajectory, a fourth target vehicle is determined based on the second target vehicle trajectory, and adaptive cruise control is completed through the fourth target vehicle and a preset following distance.

[0101] Among them, the current lane line can be the lane line of the lane where the vehicle is currently located, which is a direct indication of the vehicle's driving path and is identified in real time through sensor data. The second compensation lane line can be a continuous lane line virtually constructed based on the current lane line and the lane line information of the traveling direction when the lane line in the traveling direction is a single lane and interrupted, called the second compensation lane line, to assist the vehicle in positioning and navigation when the lane line is discontinuous. The third target vehicle can be a cut-in vehicle detected based on the second compensation lane line. These vehicles are regarded as target vehicles that may affect the driving safety of the ego vehicle and need to be specially monitored. The lateral speed can be the speed of the third target vehicle relative to the ego vehicle in the lateral direction (i.e., left and right directions). The lateral collision time (TTC) can be the time remaining before the ego vehicle and the third target vehicle collide laterally at the current speed. The preset lateral speed threshold can be a preset lateral speed value, which is used to determine whether the lateral movement of the third target vehicle is too fast and may pose a threat to the ego vehicle. The preset time threshold can be a preset time value, which is used to determine whether the lateral collision time is too short, that is, whether there is a collision risk. The second target vehicle trajectory may be a path of the preceding vehicle predicted based on the second reference vehicle data, and the second predicted ego vehicle trajectory may be a path of the ego vehicle predicted based on the sensor data. The fourth target vehicle may be another target vehicle determined based on the second target vehicle trajectory if the second target vehicle trajectory matches the second predicted ego vehicle trajectory, serving as a reference for adaptive cruise control. The preset following distance may be a pre-set safety distance value used to maintain the distance between the ego vehicle and the fourth target vehicle to ensure driving safety.

[0102] Specifically, when a vehicle cuts in based on the second compensation lane line, these cutting-in vehicles will be identified as third target vehicles, and their lateral speeds and predicted lateral collision times with the ego vehicle will be calculated. If the lateral speed of the third target vehicle exceeds the preset lateral speed threshold, and the lateral collision time is less than the preset safety time threshold, deceleration control will be performed to maintain a safe distance. On the other hand, if the lane line in the direction of travel is a curve, the data of the second reference vehicle will be collected, and the kinematic trajectory prediction model will be used to combine the sensor data and the second reference vehicle data to predict the trajectory and the predicted trajectory of the ego vehicle. When these two trajectories match, the fourth target vehicle is determined, and the ego vehicle speed is adjusted according to this target vehicle and the preset following distance to complete adaptive cruise control and ensure driving safety.

[0103] This embodiment first ensures that there is a reference standard for judging whether the detected lane line meets the expected road design standard, providing a benchmark for subsequent lane width comparison. Secondly, through the data collected by the sensor, the specific location of the lane line and the road boundary can be identified, providing accurate information for vehicle positioning and path planning. Then, by analyzing the lane line data, the actual lane width is calculated, which is a key measurement for judging whether the lane line is clear and continuous. Finally, when the actual lane width exceeds the preset threshold, the road boundary data is used to determine the driving scene state of the vehicle to determine whether the vehicle is driving close to or away from the edge of the road. This embodiment can accurately judge the lane state and vehicle position under various road conditions, identify and measure the lane line through the preset lane line width threshold and sensor data, and then adjust the degree of dependence on the road boundary data according to whether the lane width exceeds the preset threshold. When the lane line width is abnormal, it can flexibly switch to the driving scene state judgment based on the road boundary, so as to more accurately control the vehicle driving and improve the adaptability and safety of adaptive cruise.

[0104] For example, to help understand the implementation process of the adaptive cruise control method obtained by combining this embodiment with the above-mentioned embodiment 1, please refer to Figure 3 , Figure 3 A brief flow chart of an adaptive cruise control method is provided, specifically:

[0105] First, the driving scene state is determined by collecting data from sensors and preset boundary thresholds. If the current lane width exceeds the preset threshold, the road boundary data will be used to further determine whether the vehicle is driving close to or away from the edge of the road. Then, the target distance between the ego vehicle and the road boundary line is calculated based on the road boundary data to determine the driving scene state. After determining the driving scene state, the reference vehicle data will be obtained, and the kinematic trajectory prediction model will be used to predict the trajectory of the target vehicle and the ego vehicle. If the trajectory matches, the target vehicle is determined, and adaptive cruise control is completed according to the preset following distance. If the lane line is a single lane and is continuous or interrupted, a compensation lane line will be generated, and when a cut-in vehicle is detected, deceleration control will be performed according to parameters such as lateral distance and lateral speed. For curved lanes, the second reference vehicle data will be obtained, and the trajectory of the target vehicle and the ego vehicle will be predicted. After matching, the target vehicle is determined, and cruise control is completed according to the preset following distance. Finally, if the lane line data is unclear, the target vehicle will be determined directly based on the sensor data to ensure the accuracy and safety of adaptive cruise.

[0106] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the adaptive cruise control method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0107] This application also provides an adaptive cruise control device, please refer to Figure 4 , the adaptive cruise control device comprises:

[0108] An acquisition module 10, used to acquire sensor data and a preset boundary threshold;

[0109] A determination module 20, configured to determine a driving scene state according to the sensor data and the preset boundary threshold;

[0110] The completion module 30 is used to determine a target vehicle according to the driving scene state, so that the vehicle performs adaptive cruise control according to the target vehicle.

[0111] The adaptive cruise control device provided by the present application adopts the adaptive cruise control method in the above embodiment, which can solve the technical problem of how to accurately predict the vehicle's driving path in a scene where the lane line is unclear. Compared with the prior art, the beneficial effects of the adaptive cruise control device provided by the present application are the same as the beneficial effects of the adaptive cruise control method provided by the above embodiment, and other technical features in the adaptive cruise control device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0112] The present application provides an adaptive cruise control device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the adaptive cruise control method in the above-mentioned embodiment one.

[0113] Reference below Figure 5 , which shows a schematic diagram of the structure of an adaptive cruise control device suitable for implementing the embodiment of the present application. The adaptive cruise control device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The adaptive cruise control device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0114] like Figure 5As shown, the adaptive cruise control device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the adaptive cruise control device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the adaptive cruise control device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows an adaptive cruise control device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0115] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0116] The adaptive cruise control device provided by the present application adopts the adaptive cruise control method in the above embodiment, which can solve the technical problem of how to accurately predict the vehicle's driving path in a scene where the lane line is not clear. Compared with the prior art, the beneficial effects of the adaptive cruise control device provided by the present application are the same as the beneficial effects of the adaptive cruise control method provided by the above embodiment, and the other technical features in the adaptive cruise control device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0117] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0118] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0119] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the adaptive cruise control method in the above-mentioned embodiment.

[0120] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0121] The computer-readable storage medium may be included in the adaptive cruise control device; or may exist independently without being assembled into the adaptive cruise control device.

[0122] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the adaptive cruise control device, the adaptive cruise control device: obtains sensor data and a preset boundary threshold; determines a driving scene state based on the sensor data and the preset boundary threshold; determines a target vehicle based on the driving scene state, so that the vehicle performs adaptive cruise control according to the target vehicle.

[0123] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0124] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0125] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0126] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned adaptive cruise control method, and can solve the technical problem of how to accurately predict the vehicle's driving path in a scene where the lane line is unclear. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the adaptive cruise control method provided by the above-mentioned embodiment, and will not be repeated here.

[0127] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned adaptive cruise control method when executed by a processor.

[0128] The computer program product provided by the present application can solve the technical problem of how to accurately predict the vehicle's driving path in a scenario where the lane lines are unclear. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the adaptive cruise control method provided by the above embodiment, which will not be described in detail here.

[0129] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. An adaptive cruise control method, characterized in that: The method comprises: Get sensor data and preset boundary thresholds; Determining a driving scene state according to the sensor data and the preset boundary threshold; A target vehicle is determined according to the driving scene state, so that the vehicle performs adaptive cruise control according to the target vehicle.

2. The method according to claim 1, characterized in that The determining the driving scene state according to the sensor data and the preset boundary threshold comprises: Get the preset lane width threshold; Obtain lane line data and road boundary data according to the sensor data; Obtaining a current lane width based on the lane line data; If the current lane width is greater than the preset lane line width threshold, the driving scene state is determined based on the road boundary data and the preset boundary threshold.

3. The method according to claim 2, characterized in that The determining of the driving scene state based on the road boundary data and the preset boundary threshold comprises: Obtaining a target distance between the center line of the vehicle and the road boundary line according to the road boundary data; If the target distance is greater than or equal to the preset boundary threshold, the driving scene state is driving not in line with the road edge; If the target distance is less than the preset boundary threshold, the driving scene state is driving close to the edge of the road.

4. The method according to claim 3, characterized in that If the target distance is greater than or equal to the preset boundary threshold, then the driving scene state is not in line with the road edge, and further includes: Acquire first reference vehicle data of a traveling direction; Based on the kinematic trajectory prediction model, the sensor data and the first reference vehicle data, a first target vehicle trajectory and a first predicted ego vehicle trajectory are obtained; If the first target vehicle trajectory matches the first predicted ego vehicle trajectory, determining the first target vehicle according to the first target vehicle trajectory; Adaptive cruise control is accomplished through the first target vehicle and a preset following distance.

5. The method according to claim 3, characterized in that If the target distance is less than the preset boundary threshold, the driving scene state is driving close to the road edge, including: Get the preset distance threshold; Identify the lane line in the travel direction through the lane line data; If the lane line in the direction of travel is a single lane line and is continuous, obtaining a first compensation lane line based on the lane line in the direction of travel; When a cut-in vehicle is detected based on the first compensation lane line, taking the cut-in vehicle as a second target vehicle; Obtaining a target lateral distance based on the second target vehicle and the first compensation lane line; When the target lateral distance is less than the preset distance threshold, deceleration control is performed to complete the adaptive cruise control.

6. The method according to claim 5, characterized in that After the lane line of the traveling direction is identified through the lane line data, the method further includes: If the lane line in the direction of travel is a single lane and is interrupted, a second compensation lane line is obtained according to the current lane line and the lane line in the direction of travel; When a cut-in vehicle is detected based on the second compensation lane line, the cut-in vehicle is taken as a third target vehicle, and a lateral speed and a lateral collision time of the third target vehicle are obtained; If the lateral speed is greater than a preset lateral speed threshold and the lateral collision time is less than a preset time threshold, deceleration control is performed to complete the adaptive cruise control; If the lane line in the traveling direction is a curve, obtaining second reference vehicle data in the traveling direction; Based on the kinematic trajectory prediction model, the sensor data and the second reference vehicle data, obtaining a second target vehicle trajectory and a second predicted ego vehicle trajectory; If the second target vehicle trajectory matches the second predicted self-vehicle trajectory, a fourth target vehicle is determined according to the second target vehicle trajectory, and adaptive cruise control is completed through the fourth target vehicle and a preset following distance.

7. The method according to claim 2, characterized in that If the current lane width is greater than the preset lane line width threshold, after determining the driving scene state based on the road boundary data and the preset boundary threshold, the method further includes: Performing edge detection on the lane line data to obtain target lane line data; Determining a target lane line state based on the target lane line data; If the target lane line state is unclear, the target vehicle is determined according to the sensor data.

8. An adaptive cruise control device, characterized in that: The device comprises: An acquisition module, used to acquire sensor data and preset boundary thresholds; A determination module, used to determine the driving scene state according to the sensor data and the preset boundary threshold; The completion module is used to determine the target vehicle according to the driving scene state, so that the vehicle can perform adaptive cruise control according to the target vehicle.

9. An adaptive cruise control device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the adaptive cruise control method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the adaptive cruise control method according to any one of claims 1 to 7 are implemented.

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