Adaptive cruise control method, apparatus, device, and storage medium

Abstract

The application discloses a kind of adaptive cruise control method, device, equipment and storage medium, it is related to automobile automation technical field, the adaptive cruise control method includes: obtaining sensor data and preset boundary threshold;According to the sensor data and the preset boundary threshold, determine driving scene state;According to the driving scene state determines target vehicle, so that ego car is according to the adaptive cruise control of target vehicle.By the adaptive cruise control method of supplementary selection follow-up target, reduce the misidentification situation when lane line is not clear, improve the accuracy of adaptive cruise control, to enhance driving safety and comfort.

Description

TECHNICAL FIELD

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

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

[0003] Existing adaptive cruise control methods mainly determine the driving state and predicted path of the vehicle through lane line information collected by vehicle sensors. These systems can usually work well on structured roads, using lane line information to assist in determining the driving intention of the vehicle and selecting the following target. However, when the vehicle enters a lane line-free or lane line-unclear section, the performance of these systems will decrease significantly.

[0004] In a lane line-free or lane line-unclear section, existing ACC systems may lose the ability to accurately predict the vehicle's driving path, resulting in incorrect selection of the following target. This may result in inaccurate prediction of the steering wheel trajectory for the driver's intention, increasing the risk of misselecting adjacent lanes through vehicles, thereby affecting driving safety. In addition, systems that rely on surrounding vehicle information may frequently adjust acceleration and deceleration in dense vehicle conditions due to different vehicle speeds and heading angles, which not only may affect the identification of individual dangerous vehicles, but also may reduce driving comfort. Therefore, how to accurately predict the vehicle's driving path in a lane line-unclear scenario has become a problem to be solved. SUMMARY

[0005] The present application aims to provide an adaptive cruise control method, device, equipment and storage medium, which aims to solve the technical problem of how to accurately predict the vehicle's driving path in a lane line-unclear scenario.

[0006] To achieve the above-mentioned purpose, the present application provides an adaptive cruise control method, which comprises:

[0007] obtaining sensor data and a preset boundary threshold value;

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

[0009] determining a target vehicle according to the driving scene state, so that the ego vehicle performs adaptive cruise control according to the target vehicle.

[0010] In an embodiment, determining the driving scene state based on the sensor data and the preset boundary threshold comprises:

[0011] Obtaining a preset lane line width threshold;

[0012] Obtaining lane line data and road boundary data from 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, determining the driving scene state based on the road boundary data and the preset boundary threshold.

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

[0016] Obtaining a target distance between a center line of the ego vehicle and a road boundary line based on the road boundary data;

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

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

[0019] In an embodiment, if the target distance is greater than or equal to the preset boundary threshold, the driving scene state is not driving close to the road edge, and further comprises:

[0020] Obtaining first reference vehicle data of a driving direction;

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

[0022] If the first target vehicle trajectory and the first predicted ego vehicle trajectory match, determining a first target vehicle based on the first target vehicle trajectory;

[0023] Completing adaptive cruise control through the first target vehicle and a preset following distance.

[0024] In an embodiment, if the target distance is less than the preset boundary threshold, the driving scene state is driving close to the road edge, and further comprises:

[0025] Obtaining a preset distance threshold;

[0026] Identifying a lane line of a driving direction through the lane line data;

[0027] if the travel direction lane line is a single lane line and continuous, obtaining a first compensated lane line based on the travel direction lane line;

[0028] when a cut-in vehicle is detected based on the first compensated 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 compensated lane line;

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

[0031] In an embodiment, after the travel direction lane line is identified through the lane line data, the method further comprises:

[0032] if the travel direction lane line is a single lane and interrupted, obtaining a second compensated lane line according to the current lane line and the travel direction lane line;

[0033] when a cut-in vehicle is detected based on the second compensated lane line, taking the cut-in vehicle as a third target vehicle, and obtaining a lateral speed and a lateral collision time of the third target vehicle;

[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, performing deceleration control to complete adaptive cruise control;

[0035] if the travel direction lane line is a curve, obtaining second reference vehicle data of the travel direction;

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

[0037] if the second target vehicle trajectory and the second predicted ego vehicle trajectory match, determining a fourth target vehicle according to the second target vehicle trajectory, and completing adaptive cruise control through the fourth target vehicle and a preset following distance.

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

[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, a target vehicle is determined according to the sensor data.

[0042] In addition, to achieve the above object, the present application further provides a self-adaptive cruise control device, which comprises:

[0043] an acquisition module, configured to acquire sensor data and a preset boundary threshold value;

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

[0045] a completion module, configured to determine a target vehicle according to the driving scene state, so that a host vehicle performs self-adaptive cruise control according to the target vehicle.

[0046] In addition, to achieve the above object, the present application further provides a self-adaptive cruise control device, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the self-adaptive cruise control method.

[0047] In addition, to achieve the above object, the present application further provides a storage medium, which is a computer readable storage medium, and the storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the self-adaptive cruise control method.

[0048] In addition, to achieve the above object, the present application further provides a computer program product, which comprises a computer program, and the computer program is executed by a processor to implement the steps of the self-adaptive cruise control method.

[0049] The one or more technical solutions provided by the present application have at least the following technical effects:

[0050] The application firstly collects real-time information of the surrounding environment of the vehicle, including key data such as lane lines and road boundaries, and provides basic data required for decision-making in combination with preset boundary thresholds. Then, by using the data collected by the sensor and the preset boundary thresholds, the current driving environment can be accurately identified, and it can be judged whether the vehicle is in a state of driving close to the road edge or not. Finally, after the driving scene state is determined, the suitable following target can be more accurately identified and selected, and the adaptive cruise control can be provided with an accurate reference vehicle. The application can accurately judge the driving scene state by real-time acquisition and analysis of sensor data in combination with preset boundary thresholds, 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 not clear or there is no lane line, the relationship between the vehicle and the road boundary is intelligently judged, the following strategy is optimized, and the driving safety is ensured. BRIEF DESCRIPTION OF DRAWINGS

[0051] The drawings incorporated into the specification and forming a part thereof show embodiments consistent with the present application and, together with the specification, serve to explain the principles of the application.

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

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

[0054] Figure 2 The flowchart provided for the second embodiment of the adaptive cruise control method of the present application;

[0055] Figure 3 The brief flowchart of the adaptive cruise control method of the embodiment of the present application;

[0056] Figure 4 The module structure diagram of the adaptive cruise control device of the embodiment of the present application;

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

[0058] The purpose implementation, functional characteristics and advantages of the present application will be further described with reference to the embodiments and the drawings. DETAILED DESCRIPTION

[0059] It should be understood that the specific embodiments described herein are merely for the purpose of illustration of the technical solutions of the present application and are not for the purpose of limiting the present application.

[0060] In order to better understand the technical solutions of the present application, the following will be described in detail in combination with the drawings of the specification and specific embodiments.

[0061] With the development of automotive automation technology, adaptive cruise control (ACC) has become one of the key technologies to improve driving safety and comfort. 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, ACC system relies on clear lane lines to predict the driving path of the vehicle and select the following target. Existing adaptive cruise control methods mainly determine the driving state and predicted path of the vehicle through lane line information collected by vehicle sensors. These systems can usually work well on structured roads, and lane line information is used to assist in determining the driving intention of the vehicle and selecting the following target. However, when the vehicle enters a road segment without lane lines or with unclear lane lines, the performance of these systems will decrease significantly. In a road segment without lane lines or with unclear lane lines, the existing ACC system may lose the ability to accurately predict the driving path of the vehicle, resulting in the inability to correctly select the following target. This may lead to inaccurate prediction of the steering wheel trajectory for the driver's intention, increasing the risk of misselecting adjacent lanes through vehicles, thereby affecting driving safety. In addition, systems that rely on surrounding vehicle information may frequently adjust acceleration and deceleration in the presence of dense vehicles due to different vehicle speeds and heading angles, which not only may affect the identification of individual dangerous vehicles, but also may reduce driving comfort.

[0062] The main solution of the embodiments of the present application is that the embodiments first collect real-time information of the vehicle's surrounding environment, including lane lines, road boundaries and other key data, and combine 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, the current driving environment can be accurately identified, and it can be judged whether the vehicle is in a state of driving close to the road edge or not. Finally, after the driving scene state is clear, the appropriate following target can be more accurately identified and selected, providing an accurate reference vehicle for adaptive cruise control. The embodiments can accurately judge the driving scene state by real-time acquisition and analysis of sensor data combined with preset boundary thresholds, thereby effectively identifying and selecting the following target. This method can improve the accuracy and reliability of adaptive cruise control, especially in scenes where lane lines are not clear 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.

[0063] It should be noted that the execution subject of the embodiments of the present application can 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, an electronic control unit, etc. capable of realizing the above functions. The electronic control unit is taken as an example to describe the embodiments of the present application and the following embodiments.

[0064] Based on this, the present application provides a kind of adaptive cruise control method, refer to Figure 1 , Figure 1 The flowchart of the first embodiment of the adaptive cruise control method of the present application is shown in the figure.

[0065] In the present embodiment, the adaptive cruise control method comprises steps S10-S30:

[0066] Step S10, obtain sensor data and preset boundary threshold value;

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

[0068] It can be understood that real-time road and environmental information such as lane lines, road boundaries, positions and speeds of surrounding vehicles is collected by sensors mounted on the vehicle, while referring to pre-set safety and operation parameters (such as lane width threshold, distance threshold, etc.), to provide the required data support for decision-making and control of adaptive cruise control, to ensure correct response and adjustment in various driving scenarios.

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

[0070] It should be noted that the driving scene state can be the specific road environment and driving conditions of the vehicle according to the sensor data and the preset boundary threshold. These states can include: road boundary fitting state, the position of the vehicle relative to the road boundary (such as lane line or road edge), to determine whether the vehicle is driving along the road edge or deviating from the road edge; driving environment complexity, to determine the complexity of the driving environment according to the sensor data, for example, whether at an intersection, a curve, a traffic congestion area or other special situations. Determining the driving scene state directly affects how to respond to environmental changes, how to choose a suitable following target, and how to adjust the speed and distance to maintain safe and comfortable driving.

[0071] It can be understood that by using real-time data collected by vehicle sensors such as lane lines, road boundaries, positions and speeds of surrounding vehicles, etc., in combination with pre-set safety and operation parameters, the current road environment and driving conditions of the vehicle are analyzed and judged, so as to identify the state of the vehicle driving in the standard lane, close to the road edge, or driving in a specific scene without lane lines, so as to make corresponding adjustments and responses.

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

[0073] It should be noted that the target vehicle can be determined according to sensor data and preset boundary threshold analysis, which is used as a reference for the ego vehicle (i.e. the vehicle equipped with ACC system) cruise control. 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; same lane, the target vehicle should be in the same lane as the ego vehicle or on a parallel lane to facilitate the ego vehicle to follow and maintain driving within the lane; driving intention, the driving intention of the target vehicle and the ego vehicle should be consistent, such as straight ahead at an intersection, the target vehicle should also have the intention to go straight; dynamic change, the selection of the target vehicle should also consider its dynamic change, such as vehicles cutting in or out of the lane, and vehicles that may affect the 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 safely drive and automatically adjust the speed and distance of the ego vehicle according to the driving state of the target vehicle, achieving intelligent cruise control.

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

[0075] The embodiment provides a self-adaptive cruise control method. The embodiment first collects real-time information of a surrounding environment of a vehicle, including key data such as lane lines and road boundaries, and combines preset boundary thresholds to provide basic data required for decision-making. Then, the data collected by the sensor and the preset boundary thresholds are used to accurately identify the current driving environment and determine whether the vehicle is in a state of driving close to the road edge or not. Finally, after the driving scene state is determined, a suitable following target can be accurately identified and selected, thereby providing an accurate reference vehicle for self-adaptive cruise control. The embodiment can accurately determine the driving scene state by real-time acquisition and analysis of sensor data in combination with preset boundary thresholds, thereby effectively identifying and selecting a following target. This method can improve the accuracy and reliability of self-adaptive cruise control, especially in scenes where lane lines are not clear or there are no lane lines. By intelligently determining the relationship between the vehicle and the road boundary, the following strategy is optimized to ensure driving safety.

[0076] Based on the first embodiment of the application, the same or similar contents as the above-mentioned embodiment one can be referred to the above description, and will not be repeated hereinafter. On this basis, please refer to Figure 2 , Figure 2 The flowchart of the second embodiment of the self-adaptive cruise control method of the application is shown in the figure. The steps S20 of the self-adaptive cruise control method include steps S21-S24.

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

[0078] It should be noted that the preset lane line width threshold can be a value preset in the self-adaptive cruise control system, which is used 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 range of values preset according to road design standards and regulatory requirements is used to identify and verify whether the lane line detected by the sensor is within the normal width range, so as to ensure that the subsequent scene judgment selects a suitable following target through the judgment result.

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

[0081] It should be noted that the lane line data can be information such as the position, type (solid or dashed), and color of the lane line detected by the sensor, and can include geometric features of the lane line such as the width, continuity, and relationship with other lane lines, for determining the position of the vehicle in the lane and predicting the trajectory of the vehicle in the lane. The road boundary data can be the position of the road edge or boundary identified by the sensor, such as the curb, obstacles or road signs on both sides of the road, including geometric features of the boundary such as straight or curved shape, and relationship with the lane line, for assisting in determining the position of the vehicle relative to the road, especially when the lane line is unclear or missing, the road boundary data is crucial for determining the driving intention and safety boundary of the vehicle.

[0082] It can be understood that information such as the position, type, and continuity of the lane line, and data such as the position and features of the road edge or boundary are collected by sensors such as cameras, radars, or lidars on the vehicle, which together constitute the lane line data and the road boundary data, providing accurate position of the vehicle on the road and detailed information of the surrounding environment, thereby helping to make accurate judgments and control decisions in various driving scenarios.

[0083] In step S23, the current lane width is obtained based on the lane line data.

[0084] It should be noted that the current lane width can be the actual distance between the lane lines detected by the sensor data, i.e. 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 evaluate road conditions and vehicle position.

[0085] It can be understood that by analyzing the lane line position information detected by the sensor, the actual distance between the two lane lines, i.e. the width of the current lane, is measured and calculated, which is crucial for evaluating road conditions, determining whether the lane line is clear, and assisting in vehicle positioning and path planning.

[0086] In step S24, if the current lane width is greater than the preset lane line width threshold, the driving scenario state is determined based on the road boundary data and the preset boundary threshold.

[0087] It can be understood 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 are used to evaluate the position of the vehicle relative to the road boundary, thereby determining whether the vehicle is driving close to the road edge 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, 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; and if the target lane line state is unclear, determining a target vehicle according to the sensor data.

[0089] The target lane line data can be detailed data obtained by performing edge detection on the lane line by a sensor such as a camera, including accurate position, shape, and continuity of the lane line, etc. Edge detection can be an image processing technique used to identify the edges of the lane line in an image, thereby extracting the accurate position of the lane line. The target lane line data provides 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 can be an evaluation of the current condition of the lane line based on the target lane line data, including the clarity, visibility, and whether the lane line is intact. The state evaluation involves judging whether the lane line is clear and discernible, or unclear due to wear, obstruction, damage, or other factors. The target lane line state determines whether the lane line data can be relied on to predict the driving path of the vehicle and control the driving state of the vehicle.

[0090] Specifically, first, the accurate position and shape of the lane line are extracted from the sensor data by edge detection technology to form target lane line data; then, these data are analyzed to evaluate the clarity and state of the lane line, and to determine whether the lane line is clear and visible; if the lane line state is judged to be unclear, the lane line data cannot be relied on to select a following target, at which 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, determining the driving scene state based on the road boundary data and the preset boundary threshold includes: obtaining a target distance between a center line of the ego vehicle and a boundary line of the road 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 close to the road edge; and if the target distance is less than the preset boundary threshold, the driving scene state is driving close to the road edge.

[0092] The target distance can be the distance between the center line of the ego vehicle and the nearest road boundary line measured by the sensor. This measurement reflects the position of the vehicle relative to the road edge, which is a key parameter for determining the driving state and driving scenario of the vehicle. Specifically, the center line of the ego vehicle can be the longitudinal center line of the vehicle, which can be regarded as the symmetry axis of the vehicle. The road boundary line can be the line of the road edge, which can be a lane line, a road edge, an obstacle on both sides of the road, or other forms of road boundary markings.

[0093] Specifically, the distance between the center line of the ego 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 is determined to be in a state of not driving close to the road edge; on the contrary, if the target distance is less than the preset boundary threshold, the vehicle is determined to be in a state of driving close to the road edge. Such determination helps the system to adjust the driving strategy according to the position of the vehicle relative to the road edge, ensuring 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 scenario state after not driving close to the road edge further includes: obtaining first reference vehicle data in the travel direction; based on a kinematic trajectory prediction model, the sensor data and the first reference vehicle data, obtaining a first target vehicle trajectory and a first predicted ego vehicle trajectory; if the first target vehicle trajectory and the first predicted ego vehicle trajectory match, determining a first target vehicle according to the first target vehicle trajectory; and completing adaptive cruise control through the first target vehicle and a preset following distance.

[0095] The first reference vehicle data can be the relevant information of the vehicle in front (i.e., the front vehicle) in all directions, including its speed, acceleration, position, driving direction, and other dynamic data. 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 ego vehicle accordingly. The kinematic trajectory prediction model can be a mathematical model that predicts the driving trajectory of a vehicle in the future based on its dynamic data such as speed, acceleration, steering angle, etc. This model takes into account the physical motion characteristics of the vehicle and can simulate the actual motion of the vehicle on the road, helping to 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 ego vehicle trajectory can be the driving path of the ego vehicle predicted based on its current speed, acceleration, and other sensor data. The two trajectories are matched to determine the relative position and motion relationship between the ego vehicle and the front vehicle. The first target vehicle can be determined based on the matching results of the first target vehicle trajectory and the first predicted ego vehicle trajectory, which is the target vehicle for adaptive cruise control, and the speed of the ego vehicle is adjusted based on the position and driving state of this target vehicle to maintain a safe distance. The preset following distance can be a pre-set safe distance to maintain the space between the ego vehicle and the front vehicle. This distance is usually determined based on safety standards, traffic regulations, road conditions, and vehicle performance, etc., to ensure sufficient braking distance in emergency situations.

[0096] Specifically, first, the data of the reference vehicle (i.e., the vehicle in front) in the direction of the ego vehicle is collected, including its speed, position, and other information. Then, the kinematic trajectory prediction model is used to calculate the expected driving trajectory of the first target vehicle (the vehicle in front) and the predicted driving trajectory of the ego vehicle based on sensor data and the first reference vehicle data. If the two trajectories match in the system, indicating that the front vehicle is confirmed as the target vehicle, the speed of the ego vehicle will be automatically adjusted based on the position of this target vehicle and the preset safe following distance to maintain an appropriate 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 after driving close to the road edge includes: 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 cut-in vehicle is detected based on the first compensation lane line, the cut-in vehicle is taken as a second target vehicle; based on the second target vehicle and the first compensation lane line, a target lateral distance is obtained; when the target lateral distance is less than the preset distance threshold, deceleration control is performed to complete adaptive cruise control.

[0098] The preset distance threshold can be a preset safe distance value for determining 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 is triggered to avoid potential collision. The travel direction lane line can be the lane line in the current travel direction of the ego vehicle, which is 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 can be a virtual compensation lane line generated based on the lane line in the travel direction when the lane line is single and continuous, which expands the boundary of the actual lane line to assist in determining the driving path when the lane line is unclear or missing. The second target vehicle can refer to a vehicle detected from the adjacent lane cutting into the lane of the ego vehicle 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 can be the lateral distance between the ego vehicle and the cut-in vehicle (the second target vehicle), i.e. 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 to take evasive measures.

[0099] Specifically, first, a preset distance threshold is set as the lateral distance standard for safe driving, and the lane line in the travel direction is identified by analyzing the lane line data. When the lane line is detected to be single and continuous, a virtual lane line that is expanded, i.e. the first compensation lane line, is generated based on the lane line to assist vehicle positioning. When a vehicle cuts into this compensation lane line, the vehicle is identified as the second target vehicle, and the target lateral distance between the ego vehicle and the second target vehicle, i.e. the lateral distance between the two vehicles in the lane, is calculated. If the lateral distance is less than the preset safe distance threshold, the deceleration control is automatically executed to maintain a safe distance and complete the adaptive cruise control, ensuring driving safety.

[0100] As an example, after the lane line data is used to identify the lane line in the direction of travel, the method further includes: if the lane line in the direction of travel is a single lane and interrupted, obtaining a second compensation lane line 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, regarding the cut-in vehicle as a third target vehicle, obtaining a lateral speed and a time to lateral collision of the third target vehicle; if the lateral speed is greater than a preset lateral speed threshold and the time to lateral collision is less than a preset time threshold, performing deceleration control to complete adaptive cruise control; if the lane line in the direction of travel is a curve, obtaining second reference vehicle data in the direction of travel; based on a 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 and the second predicted ego vehicle trajectory match, determining a fourth target vehicle according to the second target vehicle trajectory, and completing adaptive cruise control by the fourth target vehicle and a preset following distance.

[0101] The current lane line can be a lane line of a lane in which the vehicle is currently located, which is a direct indication of the driving path of the vehicle and is identified in real time through sensor data. The second compensation lane line can be a continuous lane line virtually constructed according to the current lane line and the lane line in the direction of travel when the lane line in the direction of travel is a single lane and interrupted, to assist the positioning and navigation of the vehicle when the lane line is discontinuous. The third target vehicle can be a cut-in vehicle detected based on the second compensation lane line, which is regarded as a target vehicle that may affect the driving safety of the ego vehicle and needs to be monitored in particular. The lateral speed can be a speed of the third target vehicle relative to the ego vehicle in the lateral direction (i.e., left and right directions). The time to lateral collision (TTC) can be a time remaining before the ego vehicle collides with the third target vehicle at the current speed. The preset lateral speed threshold can be a preset lateral speed value 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 used to determine whether the time to lateral collision is too short, i.e., whether there is a risk of collision. The second target vehicle trajectory can be a driving path of a preceding vehicle predicted based on the second reference vehicle data, and the second predicted ego vehicle trajectory can be a driving path of the ego vehicle predicted based on the sensor data. The fourth target vehicle can be another target vehicle determined according to the second target vehicle trajectory if the second target vehicle trajectory and the second predicted ego vehicle trajectory match, serving as a reference for adaptive cruise control. The preset following distance can be a preset safe distance value used to maintain a distance between the ego vehicle and the fourth target vehicle to ensure driving safety.

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

[0103] The embodiment first ensures that there is a reference standard for determining whether the detected lane line meets the expected road design standard, providing a basis for subsequent lane width comparison. Secondly, through the data collected by the sensor, the specific position 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 measure to determine 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 close to or far from the road edge. The embodiment can accurately determine 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 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 road boundary-based driving scene state judgment, so as to more accurately control the vehicle driving and improve the adaptability and safety of adaptive cruise control.

[0104] For the sake of understanding the implementation process of the adaptive cruise control method obtained after the above-mentioned embodiment one, an example is provided as follows: Figure 3 , Figure 3 A schematic diagram of the brief process of the adaptive cruise control method is provided, specifically:

[0105] Firstly, the driving scene state is determined by collecting data through sensors and preset boundary threshold. If the current lane width exceeds the preset threshold, the road boundary data will be used to further determine whether the vehicle is close to or far from the road edge. Then, the target distance between the ego vehicle and the road boundary line is calculated according to the road boundary data to determine the driving scene state. After determining the driving scene state, the reference vehicle data is obtained, and the kinematic trajectory prediction model is used to predict the trajectories of the target vehicle and the ego vehicle. If the trajectories match, the target vehicle is determined, and the adaptive cruise control is completed according to the preset following distance. If the lane line is single lane and continuous or interrupted, the compensation lane line is generated, and when the cut-in vehicle is detected, the deceleration control is performed according to the lateral distance and lateral speed and other parameters. For curved lanes, the second reference vehicle data is obtained, and the trajectories of the target vehicle and the ego vehicle are predicted. After matching, the target vehicle is determined, and the cruise control is completed according to the preset following distance. Finally, if the lane line data is not clear, the target vehicle is directly determined according to the sensor data to ensure the accuracy and safety of the adaptive cruise control.

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

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

[0108] The acquisition module 10 is used for acquiring sensor data and preset boundary threshold;

[0109] The determination module 20 is used for determining the driving scene state according to the sensor data and the preset boundary threshold;

[0110] The completion module 30 is used for determining the target vehicle according to the driving scene state, so that the ego 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 driving path of the vehicle in the scene where the lane line is not clear. Compared with the prior art, the adaptive cruise control device provided by the present application has the same beneficial effects as 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 above embodiment method, which will not be repeated here.

[0112] The application provides an adaptive cruise control device, which comprises at least one processor and a memory connected with 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 to enable the at least one processor to perform the adaptive cruise control method in the embodiment one.

[0113] Reference will now be made to the drawings, in which Figure 5 which shows a structural diagram of an adaptive cruise control device suitable for implementing the embodiments of the application. The adaptive cruise control device in the embodiments of the application can include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (Personal Digital Assistant), PADs (Portable Application Description), PMPs (Portable Media Player), vehicle-mounted terminals (for example, vehicle-mounted navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. Figure 5 The adaptive cruise control device shown is only an example, and should not bring any limitation to the functions and use range of the embodiments of the application.

[0114] As Figure 5As shown, the adaptive cruise control device can include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.) that can perform various appropriate actions and processes according to programs stored in a read only memory (ROM) 1002 or programs loaded from a storage device 1003 into a random access memory (RAM) 1004. Various programs and data required for operation of the adaptive cruise control device are also stored in the RAM 1004. The processing device 1001, the ROM 1002, and the RAM 1004 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: input devices 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; the 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 wirelessly or by wire with other devices to exchange data. Although the adaptive cruise control device having various systems is shown in the figure, it should be understood that all of the shown systems are not required to be implemented or possessed. More or less systems can be alternatively implemented or possessed.

[0115] In particular, according to embodiments of the present disclosure, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network by a communication device, or installed from the storage device 1003, or installed from the ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the methods of embodiments of the present disclosure are performed.

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

[0117] It should be understood that various aspects of the disclosure can be implemented in 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 appropriate manner in any one or more embodiments or examples.

[0118] The above description is merely illustrative of the application and is not intended to limit the scope of the application. Any modifications or equivalents of the application should be construed as falling within the scope of the application. The scope of the application should be determined by the appended claims.

[0119] The application provides a computer readable storage medium having stored thereon computer readable program instructions (i.e., a computer program) for performing the adaptive cruise control method in the above embodiments.

[0120] The computer readable storage medium provided by the application may, for example, be a U disk, but is not limited to an electric, magnetic, optical, electromagnetic, infrared, or semiconductor system, system, or device, or any combination thereof. More specific examples of the computer readable storage medium can include, but are not limited to, an electric connection with one or more conductive 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 appropriate combination thereof. In the present embodiment, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer readable storage medium can be transmitted by any appropriate medium, including but not limited to an electric wire, an optical cable, an RF (Radio Frequency), etc., or any appropriate combination thereof.

[0121] The above computer readable storage medium can be included in an adaptive cruise control device; or can exist separately and not be assembled into an adaptive cruise control device.

[0122] The computer readable storage medium described above carries one or more programs, when the one or more programs are executed by the adaptive cruise control device, cause the adaptive cruise control device to: acquire sensor data and a preset boundary threshold value; determine a driving scene state according to the sensor data and the preset boundary threshold value; and determine a target vehicle according to the driving scene state, so that the ego vehicle is controlled according to the target vehicle.

[0123] Computer program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can 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 the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0124] The flow diagrams and the block diagrams in the drawings are illustrations of architectures, functionalities, and operations of possible implementations of systems, methods, and computer program products according to various embodiments of present application. In this regard, each block in the flow diagrams or block diagrams can represent a module, a procedure, or a part of code, which comprises one or more executable instructions for implementing the specified functions. It should also be noted that, in some alternative implementations, the functions noted in the blocks can occur in a different order than that noted in the figures. For example, two blocks noted in succession can in fact be executed substantially concurrently or in the opposite order, depending on the functionality involved. It should also be noted that each block in the block diagrams and / or flow diagrams, and combinations of blocks in the block diagrams and / or flow diagrams, can be implemented by dedicated hardware-based systems that perform the specified functions or operations, or can be implemented by a combination of dedicated hardware-based systems and computer instructions.

[0125] The modules involved in the embodiments of the present application can be implemented in the form of software or in the form of hardware. In some cases, the name of the module does not constitute a limitation on the module itself.

[0126] The readable storage medium provided by the application is a computer readable storage medium, and the computer readable storage medium stores computer readable program instructions (namely, a computer program) for executing the adaptive cruise control method, and can solve the technical problem of how to accurately predict the driving path of the vehicle in the scene where the lane line is not clear. Compared with the prior art, the computer readable storage medium provided by the application has the same beneficial effects as the adaptive cruise control method provided by the above-mentioned embodiments, and will not be described here.

[0127] The application further provides a computer program product comprising a computer program, which, when executed by a processor, implements the steps of the adaptive cruise control method as described above.

[0128] The computer program product provided by the application can solve the technical problem of how to accurately predict the driving path of the vehicle in the scene where the lane line is not clear. Compared with the prior art, the computer program product provided by the application has the same beneficial effects as the adaptive cruise control method provided by the above-mentioned embodiments, and will not be described here.

[0129] The above-mentioned is only part of the embodiments of the application, and does not limit the patent scope of the application, and any equivalent structural transformation, direct / indirect application in other related technical fields made by using the content of the application specification and drawings under the technical concept of the application are included in the patent protection scope of the application.

Claims

1. A method of adaptive cruise control, characterized by, The method comprises: obtaining sensor data and a preset boundary threshold value; determining a driving scene state according to the sensor data and the preset boundary threshold value; determining a target vehicle according to the driving scene state, so that the ego vehicle performs adaptive cruise control according to the target vehicle; the step of determining a driving scene state according to the sensor data and the preset boundary threshold value comprises: obtaining a preset lane line width threshold value; obtaining lane line data and road boundary data from 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 value, determining a driving scene state based on the road boundary data and the preset boundary threshold value; the step of determining a driving scene state based on the road boundary data and the preset boundary threshold value comprises: obtaining a target distance between the center line of the ego 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 value, the driving scene state is not driving close to the road edge; if the target distance is less than the preset boundary threshold value, the driving scene state is driving close to the road edge; after the step of if the target distance is less than the preset boundary threshold value, the driving scene state is driving close to the road edge, comprising: obtaining a preset distance threshold value; identifying a 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 cut-in vehicle is detected based on the first compensation lane line, regarding 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 value, performing deceleration control to complete adaptive cruise control.

2. The method of claim 1, wherein, after the step of if the target distance is greater than or equal to the preset boundary threshold value, the driving scene state is not driving close to the road edge, further comprising: obtaining first reference vehicle data in the direction of travel; obtaining a first target vehicle trajectory and a first predicted ego 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 ego vehicle trajectory match, determining a first target vehicle according to the first target vehicle trajectory; performing adaptive cruise control through the first target vehicle and a preset following distance.

3. The method of claim 1, wherein, after the step of identifying a lane line in the direction of travel through the lane line data, further comprising: if the lane line in the direction of travel is a single lane and interrupted, obtaining a second compensation lane line 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, regarding the cut-in vehicle as a third target vehicle, obtaining a lateral speed and a lateral collision time of the third target vehicle; if the lateral speed is greater than a preset lateral speed threshold value and the lateral collision time is less than a preset time threshold value, performing deceleration control to complete adaptive cruise control; if the lane line in the direction of travel is a curve, obtaining second reference vehicle data in the direction of travel; obtaining a second target vehicle trajectory and a second predicted ego vehicle trajectory based on the kinematic trajectory prediction model, the sensor data and the second reference vehicle data; if the second target vehicle trajectory and the second predicted ego vehicle trajectory match, determining a fourth target vehicle according to the second target vehicle trajectory, and completing adaptive cruise control through the fourth target vehicle and a preset following distance.

4. The method of claim 1, wherein, if the current lane width is greater than the preset lane line width threshold, the method further comprises: 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, determining a target vehicle according to the sensor data.

5. An adaptive cruise control device characterized by comprising: The device comprises: an acquisition module configured to acquire sensor data and a preset boundary threshold; a determination module configured to determine a driving scene state according to the sensor data and the preset boundary threshold; the determination module is further configured to acquire a preset lane line width threshold; obtaining lane line data and road boundary data from 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, determining a driving scene state based on the road boundary data and the preset boundary threshold; the determination module is further configured to obtain a target distance between an ego vehicle center line and a road boundary line from 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 close to the road edge; if the target distance is less than the preset boundary threshold, the driving scene state is driving close to the road edge; the determination module is further configured to obtain a target distance between an ego vehicle center line and a road boundary line from 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 close to the road edge; if the target distance is less than the preset boundary threshold, the driving scene state is driving close to the road edge; the determination module is further configured to acquire a preset distance threshold; identifying a travel direction lane line through the lane line data; if the travel direction lane line is a single lane line and continuous, obtaining a first compensation lane line based on the travel direction lane line; when a cut-in vehicle is detected based on the first compensation lane line, regarding 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, performing deceleration control to complete adaptive cruise control; a completion module configured to determine a target vehicle according to the driving scene state, so that the ego vehicle performs adaptive cruise control according to the target vehicle.

6. An adaptive cruise control device characterized by comprising: The device comprises a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the adaptive cruise control method according to any one of claims 1 to 4.

7. A storage medium, characterized by The storage medium is a computer readable storage medium, and the storage medium stores a computer program. The computer program is executed by the processor to implement the steps of the adaptive cruise control method in any one of claims 1 to 4.

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