Intelligent driving adaptive control method and control system

Abstract

The invention provides an intelligent driving self-adaptive control method which comprises the following steps: S1) starting a vehicle, and judging whether an intelligent driving self-adaptive control system is activated or not; s2) if the self-adaptive control system is activated, a fusion sensing module collects local data and online networking information of a vehicle end in real time, multi-source information is fused, and road, traffic and weather conditions are accurately recognized; s3) the calculation processing module makes rapid comprehensive judgment and analysis processing on the current environment through the fusion information, makes an optimal decision according to the current scene, and sends a targeted adaptive control adjustment instruction to an adjustment control module; s4) the adjustment control module executes a corresponding control response after receiving the self-adaptive control adjustment instruction sent by the calculation processing module; and S5) quitting intelligent driving adaptive control. The invention further provides an intelligent driving self-adaptive control system which comprises a fusion sensing module, a calculation processing module and an adjustment control module.

Description

Technical Field

[0001] The present invention relates to an adaptive control method and a control system, and in particular to an intelligent driving adaptive control method and a control system. Background Art

[0002] With the continuous development of intelligent connected vehicle technology, the penetration rate of intelligent driving functions has increased significantly. Intelligent driving functions of different manufacturers and capabilities have been configured on more and more models of different prices, becoming the main highlight to attract consumers and the driving force to promote consumption. At the same time, consumers' acceptance and usage experience requirements for intelligent driving functions are getting higher and higher. Continuously improving the safety and stability of intelligent driving functions and continuously enhancing the environmental adaptation and self-adjustment capabilities of intelligent driving systems have also become important contents for the development of intelligent driving and even the entire automotive industry. At present, the intelligent driving functions on the market can be divided into three categories according to the scene: driving, parking and active safety, such as NOA pilot assistance, APA automatic parking assistance, AEB automatic emergency braking, etc. The intelligent driving system calculates and processes according to the vehicle's perception information to assist the driver in realizing the longitudinal and lateral motion control of the vehicle. However, current intelligent driving solutions generally lack the ability to adapt to different road surfaces, weather, traffic and other conditions, and lack the ability to make instant adjustments to special scenarios. For example, when the vehicle is on dry and wet roads, or in clear and foggy weather, the system cannot promptly and accurately identify different scenarios and adjust the control scheme. Instead, it uses the same obstacle distance perception, judgment reminder and motion control strategies and parameters. It cannot, like manual driving, take targeted measures such as increasing the following distance, reducing the frequency of lane changes, and appropriately braking in advance on wet roads to ensure driving safety. Instead, it leads to an increased risk of accidents due to the use of intelligent driving functions, and the driver is frequently required to take over and exit functions, which seriously affects the driver's driving confidence and user experience, and increases driving tension. For example, when faced with complex traffic scenarios such as congestion, current intelligent driving solutions generally lack game thinking and reasoning strategies. They only follow fixed vehicle distance and speed control schemes, and are unable to take targeted response measures in a timely manner, resulting in frequent cut-ins or collisions with other vehicles. On the other hand, current intelligent driving solutions generally rely on "single-vehicle intelligent" sensors, with a single source of perception information and a lack of real-time comparative references and redundant judgments, which can easily cause inaccurate or delayed perception. For example, the system can relatively accurately identify slippery scenes in the rain, but it is easy to miss slippery scenes after the rain. At the same time, although end-to-end large models and other solutions have been installed on vehicles and applied to intelligent driving, the actual performance relies on massive amounts of high-quality data training and lacks clear rules to provide a bottom-line guarantee. The behavior when dealing with special scenarios and dangerous situations is uncertain and random, and cannot guarantee stable and effective risk early judgment and timely avoidance and adjustment.In summary, the existing technical solutions have the following main deficiencies: 1) They lack timely and accurate recognition of special scenarios such as different road surfaces, weather, and traffic, as well as hierarchical, gradient, and targeted human-computer interaction reminders and vehicle motion control strategies. They lack environmental adaptation and instant adjustment capabilities, have an obvious "machine feel" in driving, have single functions, and lack an anthropomorphic driving style; 2) They increase the risk of accidents in special scenarios such as slippery roads and bad weather, seriously affecting the driver's driving confidence, frequently requiring the driver to take over the exit function, making the use intermittent and unsmooth, and resulting in poor user experience, which increases the tension of driving. Instead of being a capable assistant to the driver, they become a necessary tool in special situations. driving burden; 3) lack of game thinking and reasoning strategies in the face of complex traffic scenarios such as congestion, and no targeted response and adjustment measures, resulting in frequent cutting in or collisions with other vehicles; 4) single source of perception information, relying on "single-vehicle intelligence" sensors, lack of redundant judgment and comparative reference of multiple schemes, which can easily cause perception inaccuracy and delay; 5) Although intelligent driving solutions such as end-to-end big models have been installed on the vehicle, the actual performance depends on massive high-quality data training, and there is a lack of clear rules for bottom-line protection. The behavior in response to special scenarios and dangerous situations is uncertain and random, and there is a lack of logical and systematic strategies to stably and effectively judge risks in advance and avoid adjustments in time. Summary of the invention

[0003] The purpose of the present invention is to provide a safe, intelligent, and reliable intelligent driving adaptive control method and control system that is composed of multiple sensors, controllers, actuators, etc., and can integrate the vehicle's current perceived environment and big data information such as weather and traffic in real time, and adaptively adjust the vehicle's acceleration, braking, steering, and human-computer interaction reminder strategies according to actual road conditions, weather, traffic, etc., and can adapt to different situations.

[0004] The present invention provides an intelligent driving adaptive control method, the method comprising the following steps:

[0005] S1) The vehicle is started and whether the intelligent driving adaptive control system is activated;

[0006] S2) If the adaptive control system is activated, the fusion perception module collects local vehicle data and online network information in real time, fuses multi-source information, and accurately identifies road, traffic and weather conditions;

[0007] S3) the computing and processing module uses the fusion information to make a quick comprehensive judgment and analysis of the current environment, makes an optimal decision based on the current scenario, and issues a targeted adaptive control adjustment instruction to the adjustment control module;

[0008] S4) the adjustment control module executes a corresponding control response after receiving the adaptive control adjustment instruction sent by the calculation processing module;

[0009] S5) Exit intelligent driving adaptive control.

[0010] Furthermore, the step S3) is based on the distribution index of environmental factors including weather and road conditions and the adaptively adjustable interval division standard, integrating the real-time variable traffic game strategy, and performing step-by-step progressive adaptive control through the evaluation function coefficient.

[0011] Furthermore, the evaluation function is Y=AX1+BX2, where X1 represents the braking force and X2 represents the braking time (TTC). The coefficient A is the dynamic gain of the braking force and the coefficient B is the time weight of the braking time, and the two cooperate to realize step-by-step progressive adaptive control.

[0012] The step S3) further comprises:

[0013] S31) Comprehensively process the multi-source information of the fusion perception module, and intelligently score and comprehensively calculate the current weather conditions and road conditions according to the principle of "the more unfavorable to driving safety, the higher the score", and respectively obtain the current weather index and road condition index, and at the same time determine the danger level of the current situation by referring to the actual vehicle test calibration, combining the actual performance of the vehicle and the real-time scene analysis and judgment of the intelligent driving AI controller;

[0014] S32) The system performs adaptive rough adjustment and adaptive fine adjustment on coefficients A and B in sequence according to the distribution of environmental factors obtained in the previous step and the determined danger level. First, the system performs rough adjustment on coefficients A and B to prevent the vehicle from exceeding the adhesion limit and thus slipping. At the same time, the system determines the rear-end collision risk in combination with the perception information to improve driving safety. Based on the above rough adjustment, the system continues to perform fine adjustment to optimize and adjust the braking curve to improve driving comfort.

[0015] S33) If the current vehicle is in a special dangerous scenario, no complex traffic situation judgment and game control will be performed; if the current weather and road conditions are both at a low risk level, the system will automatically judge the complex traffic situation and game control. According to the information from the fusion perception module, when the vehicle is in a congested traffic situation, the system will appropriately reduce the following distance while ensuring the braking distance. If the system identifies a special vehicle or a neighboring vehicle that is extremely close to the vehicle, the game strategy will be canceled to leave parking space for passing.

[0016] The step S31) determines the danger level dividing lines a1, a2 and b1, b2 of the current situation, and divides the weather index and road condition index coordinates into four areas 10, 20, 30, and 40. Area 30 indicates that the current weather and road conditions are both at a low danger level, area 10 indicates that the current weather conditions are at a high danger level, area 40 indicates that the current road conditions are at a high danger level, and area 20 indicates that the current weather and road conditions are both at a high danger level.

[0017] The step S32) further comprises:

[0018] S321) If the analysis result of the current situation falls in area 30, it means that the current weather and road conditions are both at a low risk level, and the system does not need to take special targeted adaptive adjustment measures, and continues to perform conventional intelligent driving function operations;

[0019] S322) If the analysis result of the current situation falls in area 10, it means that the current weather conditions are at a high risk level and the road conditions are at a low risk level. At this time, the system's perception ability is limited, and the coefficient A needs to be incrementally adjusted according to the actual vehicle standard and real-time scene analysis, reducing the vehicle speed, and increasing the braking force when encountering obstacles to achieve collision avoidance. At the same time, the sensitivity of distance perception, warning, and braking is automatically adjusted to the highest, adjusted to a conservative driving style, reducing the frequency of lane changes, and promptly sending reminders to the driver to inform him of the current environment and the adjustment measures taken by the system, so as to help the driver understand the vehicle behavior and determine the timing of taking over;

[0020] S323) If the analysis result of the current situation falls in area 40, it means that the current road condition is at a high risk level and the weather condition is at a low risk level. At this time, the adhesion coefficient is reduced, and the coefficient B needs to be incrementally adjusted according to the actual vehicle mark quantitative and real-time scene analysis to maintain a larger vehicle distance. While taking into account the distance to the rear vehicle to prevent rear-end collision, the driver is given a warning and reminder in advance and brakes are applied. At the same time, a conservative driving style is adjusted to reduce the frequency of lane changes, and a notification reminder is issued to the driver;

[0021] S324) If the analysis result of the current situation falls in area 20, it means that the current weather and road conditions are at a high risk level. At this time, the system's perception ability is limited, and the adhesion coefficient is reduced. It is necessary to comprehensively deal with the above scenarios, reduce the vehicle speed, and automatically adjust the sensitivity of distance perception, warning, and braking to the highest. At the same time, adjust to a conservative driving style, reduce the frequency of lane changes, and send a notification reminder to the driver.

[0022] The present invention provides an intelligent driving adaptive control system, comprising a fusion perception module, a calculation processing module and an adjustment control module. The fusion perception module realizes the recognition and prediction of the current environmental conditions by real-time fusion of vehicle-side local data and online network information, the calculation processing module makes a judgment on the current environment, makes an optimal decision according to the current scene, and sends an adaptive control adjustment instruction to the adjustment control module, and the control adjustment module executes the corresponding control response according to the adaptive adjustment control instruction.

[0023] Among them, the fusion perception module includes the vehicle perception system and the intelligent network connection system. The vehicle perception system includes: cameras, sunlight and rain sensors, wipers, lights, air conditioners, temperature and humidity sensors, lidars, chassis sensors, body sensors, navigation systems, millimeter wave radars. The intelligent network connection system includes a navigation system and online weather, road, traffic information networks and crowdsourcing big data systems.

[0024] The computing and processing module includes an intelligent driving AI controller.

[0025] The adjustment control module includes a chassis control execution system and a cockpit human-computer interaction system. The chassis control execution system includes vehicle drive, steering, braking systems and various actuators and related parts. The cockpit human-computer interaction system includes a vehicle instrument screen, a central control screen, a HUD, ambient lights and audio. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The drawings constituting a part of this application are used to provide a further understanding of the present invention. The illustrative embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0028] In addition, the drawings are not drawn in a 1:1 ratio, and the relative sizes of the various elements are drawn in the drawings only as examples and not necessarily in true proportion.

[0029] Figure 1 A flowchart of an intelligent driving adaptive control method preferred by an embodiment of the present invention;

[0030] Figure 2 A schematic diagram of a module of an intelligent driving adaptive control system preferably used in an embodiment of the present invention;

[0031] Figure 3 The overall logic block diagram of the adaptive control system preferred in the embodiment of the present invention;

[0032] Figure 4 A schematic diagram of the associated components and main signals of the fusion perception module preferred in an embodiment of the present invention;

[0033] Figure 5 A logic block diagram of an adaptive control algorithm solution preferred in an embodiment of the present invention;

[0034] Figure 6A regional distribution map of environmental factors preferred by the embodiment of the present invention;

[0035] Figure 7 This is a schematic diagram of the associated components and main signals of the adjustment control module preferably according to an embodiment of the present invention.

[0036] Reference numerals:

[0037] Fusion perception module-1;

[0038] Ego perception system-11;

[0039] Camera - 111;

[0040] Sunlight and rain sensor - 112;

[0041] Wiper - 113;

[0042] Lighting - 114;

[0043] Air conditioning - 115;

[0044] Temperature and humidity sensor - 116;

[0045] LiDAR-117;

[0046] Millimeter wave radar-118;

[0047] Chassis suspension sensor - 119;

[0048] Body posture sensor-120;

[0049] Intelligent network system-12;

[0050] Navigation system - 121;

[0051] Online weather, roads, communications, and information networks 122;

[0052] Crowdsourcing big data systems 123;

[0053] Computation processing module-2;

[0054] Intelligent driving AI controller-21;

[0055] Adjustment control module-3;

[0056] Chassis control execution system-31;

[0057] Drive-311;

[0058] Turn - 312;

[0059] Braking - 313;

[0060] Cockpit human-machine interaction system-32;

[0061] Instrument screen - 321;

[0062] Central control screen - 322;

[0063] HUD-323;

[0064] Ambient lighting - 324;

[0065] Speaker - 325. DETAILED DESCRIPTION

[0066] In order to make the purpose, technical scheme and advantages of the implementation of this application clearer, the technical scheme in the embodiment of this application will be described in more detail below in conjunction with the drawings in the embodiment of this application. In the drawings, the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions. The described embodiments are part of the embodiments of this application, not all of them. The embodiments described below with reference to the drawings are exemplary and are intended to be used to explain this application, and should not be construed as limitations on this application. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0067] In addition, it should be noted that, unless otherwise clearly specified and limited, the words "installed", "connected", "connected" and similar terms used in the description of this application should be understood in a broad sense. For example, the connection can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, an indirect connection through an intermediate medium, or the internal connection of two components. Technical personnel in the field can understand their specific meanings in this application according to the specific circumstances.

[0068] Figure 2 Schematic diagram of a preferred intelligent driving adaptive control system module according to an embodiment of the present invention. Figure 2 The intelligent driving adaptive control system shown includes a fusion perception module 1, a calculation and processing module 2 and an adjustment control module 3.

[0069] The fusion perception module 1 includes a vehicle perception system 11 and an intelligent network connection system 12, which realizes accurate identification of the current actual road surface, weather, traffic and other environmental conditions and prediction of the conditions along the route by real-time fusion of vehicle-side local data and online network connection information. Specifically, the fusion perception module 1 realizes accurate and timely identification of various weather, road, traffic and other conditions through multi-source synchronous fusion of vehicle-side local information, online network connection and crowdsourcing big data, providing a basic guarantee for the adaptive control system to make correct and effective adjustment strategies.

[0070] Specifically, Figure 2The self-vehicle perception system 11 shown mainly includes a camera 111, a laser radar 117, a millimeter-wave radar 118, a sunlight and rain sensor 112, a wiper 113, a headlight, an air conditioner 115, a temperature and humidity sensor 116, a chassis suspension sensor 119, a wheel sensor, a speed sensor, a body posture sensor 120, etc. On the one hand, the camera 111, the laser radar 117, and the millimeter-wave radar 118 are the main intelligent driving function sensors, which collect information such as the position, direction, and speed of obstacles around the vehicle in real time. On the other hand, the camera 111 is also an important direct information source for the adaptive control system. Through image recognition and large-scale model training and feature target extraction of massive data, it accurately identifies complex scene features such as precipitation and snowfall in the sky, pedestrian umbrellas and raincoats, water and snow on the road, lane lines covered, sand and mud, etc., and provides the main input and basis for the adaptive control system to judge the current weather, road and other special environmental conditions; the sunlight and rain sensor 112 provides the system with real-time lighting and precipitation information, and provides direct reference information for the adaptive control system to judge the current weather conditions based on the image perception of the camera 111; the wiper 113 and lights 114 provide indirect judgment information and redundant comparison for the adaptive control system. The system assists in judging the current precipitation intensity and visibility by reading the gear position of wipers 113 and the status information of lights 114 such as headlights and fog lights, as a supplement and confirmation based on the direct perception results of the camera 111 and sensors; the vehicle air-conditioning 115 filter sensor and the temperature and humidity sensor 116 perceive the PM2.5 and temperature and humidity conditions of the current environment in real time, and are mainly used for the recognition and judgment of weather such as haze; the chassis suspension sensor 119, wheel sensor, speed sensor, body posture sensor 120, etc. are mainly used for the system to perceive and identify road conditions, and obtain road adhesion coefficient, excitation amplitude and frequency, vehicle speed, corner speed, as well as position, posture, brake disc temperature and humidity, etc. The vehicle perception system 11 mainly uses this complete set of direct and indirect perception solutions deployed locally on the vehicle to accurately identify weather conditions such as sunny, rainy, snowy, foggy, hail, and road information such as dry, wet, sandy, muddy, flat, bumpy, slope, and curvature, providing input basis for adaptive control.

[0071] Specifically, Figure 2The intelligent network system 12 shown mainly includes a navigation system 121 and an online weather, road, and traffic information network 122 and a crowdsourcing big data system 123. It obtains the weather, road, and traffic conditions of the vehicle's location in real time based on the vehicle positioning and map navigation information, and makes a trip prediction according to the navigation route to obtain weather, road, and traffic forecast information along the way. At the same time, users can also upload the weather, road, and traffic conditions of their current location to the cloud, and provide and obtain real-time references to each other in the form of crowdsourcing. The intelligent network system 12 mainly obtains accurate online weather, road, and traffic data of the current environment and trip forecast through this big data solution, and integrates it with the local environment perception data of the vehicle end of the vehicle perception system 11 to provide a judgment basis for timely and accurate scene recognition.

[0072] like Figure 2 The calculation and processing module 2 shown preferably includes an intelligent driving AI controller 21. The intelligent driving AI controller 21 has powerful computing power. In addition to performing conventional intelligent driving function calculations, it also supports adaptive control strategy analysis and processing in real time. Through the fusion information of the self-vehicle perception system 11 and the intelligent network connection system 12, it makes a comprehensive judgment on the current environment, makes the best decision according to the current scene, and sends targeted adaptive control adjustment instructions to the adjustment control module 3, such as increasing the following distance, reducing the frequency of lane changes, and appropriately braking in advance.

[0073] like Figure 2 As shown, the adjustment control module 3 includes a chassis control execution system 31 and a cockpit human-computer interaction system 32. The adjustment control module 3 includes a chassis control execution system 31 and a cockpit human-computer interaction system 32. After receiving the adaptive control adjustment instruction issued by the computing and processing module 2, it executes a corresponding control response, including longitudinal and lateral motion control of the vehicle and a combination of multiple optical and sound reminders in the cockpit, to help the driver better cope with special scenarios. The adjustment control module 3 preferably includes vehicle drive 311, steering 312, braking 313 systems and various actuators and related parts, vehicle instrument screen 321, central control screen 322, HUD323, ambient light 324, audio 325 and other in-cabin equipment. After the adjustment control module 3 receives the adaptive control adjustment instruction issued by the computing and processing module 2, the cockpit human-computer interaction system 32 promptly sends a combination of multiple optical and sound reminders to the driver to inform the occupants of the current special environmental conditions and the adjustment measures taken by the system. At the same time, the chassis control execution system 31 also performs targeted adjustment and control of the longitudinal and lateral movements of the vehicle according to the instructions, helping the driver to better cope with special scenarios and ensure driving safety.

[0074] Specifically, Figure 2The chassis control execution system 31 shown includes vehicle drive 311, steering 312, braking 313 systems and various actuators and related parts. After receiving the instructions issued by the calculation and processing module 2, each line control unit responds to the corresponding acceleration and deceleration and steering handshake request signal, and adjusts the acceleration and deceleration, torque, angle, angular velocity and related turn signal parameters such as turn signals and brake lights in time according to the instructions. The numerical value and status bit are adjusted to control the longitudinal and lateral movement of the vehicle in a targeted manner.

[0075] Figure 1 This is a flow chart of a preferred intelligent driving adaptive control method according to an embodiment of the present invention. Figure 3 This is an overall logic block diagram of an adaptive control system preferably used in an embodiment of the present invention. Figure 4 This is a schematic diagram of the associated components and main signals of the fusion perception module preferably used in the embodiment of the present invention. Figure 5 This is a logic block diagram of the preferred adaptive regulation and control algorithm solution of the embodiment of the present invention. Figure 6 This is a regional distribution diagram of environmental factors preferred in an embodiment of the present invention. Figure 7 This is a schematic diagram of the preferred adjustment control module associated components and main signals of the embodiment of the present invention. Figures 3 to 7 shown.

[0076] like Figure 1 As shown, the intelligent driving adaptive control method comprises the following steps:

[0077] S1) The vehicle is started, and it is determined whether the intelligent driving adaptive control system is activated.

[0078] S2) If the adaptive control system is activated, the fusion perception module 1 collects local vehicle data and online network information in real time, fuses multi-source information, and accurately identifies road, traffic and weather conditions.

[0079] Specifically, if the intelligent driving adaptive control system switch is activated in the previous step, the camera 111, lidar 117, millimeter-wave radar 118, sunlight and rainfall sensor 112, wiper 113, headlights, air conditioning 115, temperature and humidity sensor 116, chassis suspension sensor 119, wheel sensor, speed sensor, body posture sensor 120, navigation and networking equipment and other devices included in the self-vehicle perception system 11 and the intelligent network system 12 of the fusion perception module 1 collect local vehicle data and online network information in real time, and integrate multi-source input information through image recognition training, feature target extraction, multi-source information synchronization, redundant comparison reference, etc., to accurately and timely identify weather conditions such as sunny days, rain and snow, haze, hail, and road information such as dry, slippery, sandy, muddy, flat, bumpy, slope, curvature, etc., and obtain current and along-the-way weather, road, and traffic forecast conditions based on navigation information and big data networking and crowdsourcing systems.

[0080] S3) The computing and processing module 2 uses the fused information to make a quick comprehensive judgment and analysis of the current environment, makes the best decision based on the current scenario, and sends targeted adaptive control adjustment instructions to the adjustment control module 3, such as increasing the following distance, reducing the frequency of lane changes, and braking in advance appropriately.

[0081] Figure 5 FIG. 1 is a logic block diagram of an adaptive control algorithm solution preferred in an embodiment of the present invention. Figure 5 The intelligent driving adaptive control system described in the present invention proposes an innovative regulation and control algorithm. The step S3) includes an environmental factor distribution index based on weather and road conditions and an adaptively adjustable interval division standard, integrating a real-time variable traffic game strategy, and performing step-by-step progressive adaptive control through an evaluation function coefficient.

[0082] Further, the evaluation function is Y=AX1+BX2. Specifically, the function Y=AX1+BX2 is a quantitative characterization evaluation function of driving safety in special dangerous scenes, where X1 represents the braking force, X2 represents the braking time (TTC), the coefficient A is the dynamic gain of the braking force, and the coefficient B is the time weight of the braking time, and the two cooperate to realize step-by-step progressive adaptive control. When the vehicle is in a special dangerous scene such as a slippery road with a low adhesion coefficient or bad weather with low visibility, it is necessary to take measures such as controlling the speed, maintaining a larger safe distance to cope with the possible longer braking distance, and appropriately braking in advance while taking into account the distance to the rear vehicle to prevent being rear-ended, so as to improve driving safety. Therefore, in this function, X1 (braking force) and X2 (braking time TTC) are the main parameters affecting the driving safety in special dangerous scenes, and in special dangerous scenes, on the premise of ensuring that the vehicle does not lose stability and slip, and is not rear-ended, the larger the parameter value, the higher the safety. The adaptive control described in the present invention mainly adjusts the coefficients A and B of the parameters X1 and X2 in a targeted manner in combination with specific scene conditions.

[0083] like Figure 5 As shown, the step S3) further comprises:

[0084] S31) Comprehensively process the multi-source information of the fusion perception module 1, and intelligently score and comprehensively calculate the current weather conditions and road conditions according to the principle of "the more unfavorable to driving safety, the higher the score", and respectively obtain the current weather index and road condition index. At the same time, by referring to the actual vehicle test calibration, combined with the actual performance of the vehicle and the real-time scene analysis and judgment of the intelligent driving AI controller 21, the danger level of the current situation is determined.

[0085] Figure 6 This is a regional distribution diagram of environmental factors preferred in the embodiment of the present invention. Specifically, Figure 6As shown, intelligent environmental factor distribution analysis and interval division calibration are performed. Intelligent scoring and comprehensive calculation are performed on the current weather conditions such as precipitation, light, haze visibility, and road conditions such as ice, powder snow, wet and slippery, sandy, and epoxy floor, and the current weather index and road condition index are obtained respectively. By referring to the actual vehicle test calibration, combined with the actual performance of the vehicle and the real-time scene analysis and judgment of the intelligent driving AI controller 21, the danger level dividing lines a1, a2 and b1, b2 of the current situation are determined, and the weather index and road condition index coordinates are divided into four areas 10, 20, 30, 40.

[0086] S32) Step-by-step progressive adaptive control. Based on the distribution of environmental factors obtained in the previous step and the interval division of adaptive calibration, the system sequentially performs adaptive rough adjustment of coefficients A and B representing safety and adaptive fine adjustment representing comfort. First, the coefficients A and B are roughly adjusted to prevent the vehicle from exceeding the adhesion limit and thus slipping. At the same time, the system combines the perception information to determine the rear-end collision risk and improve driving safety.

[0087] The step S32) further comprises:

[0088] S321) If the analysis result of the current situation falls in area 30, it means that the current weather and road conditions are at a low risk level. The system does not need to take special targeted adaptive adjustment measures and continues to perform regular intelligent driving function operations to make the subsequent third step judgment.

[0089] S322) If the analysis result of the current situation falls in area 10, it means that the current weather conditions are at a high-risk level and the road conditions are at a low-risk level, such as low-visibility weather conditions such as night, haze, and normal dry road scenes. At this time, the system's perception ability is limited, the recognition distance is shortened, and it is easy to cause rear-end collisions. The coefficient A needs to be incrementally adjusted according to the actual vehicle mark quantity and real-time scene analysis, reduce the vehicle speed, and increase the braking force when encountering obstacles to achieve collision avoidance. At the same time, the sensitivity of distance perception, warning, and braking is automatically adjusted to the highest, adjusted to a conservative driving style, and the frequency of lane changes is reduced. The driver is reminded in a timely manner to inform him of the current environment and the adjustment measures taken by the system to help the driver understand the vehicle behavior and determine the timing of takeover.

[0090] S323) If the analysis result of the current situation falls in area 40, it means that the current road condition is at a high-risk level and the weather condition is at a low-risk level, such as in rainy and snowy slippery roads, epoxy floors and other scenarios. At this time, the adhesion coefficient is reduced. It is impossible to apply a large braking force while ensuring that the vehicle is stable and does not slip. The vehicle braking distance increases, and the coefficient B needs to be incrementally adjusted according to the actual vehicle standard quantity and real-time scene analysis to maintain a larger vehicle distance. While taking into account the distance to the rear vehicle to prevent being rear-ended, the driver is given a warning and reminder in advance and brakes are performed. At the same time, the driving style is adjusted to a conservative one, the frequency of lane changes is reduced, and notifications and reminders are issued to the driver.

[0091] S324) If the analysis result of the current situation falls in area 20, it means that the current weather and road conditions are at a high risk level, such as low visibility weather conditions such as night, haze, rain, snow and strong wind, and slippery roads. At this time, the system's perception ability is limited, and the adhesion coefficient is reduced. It is necessary to comprehensively deal with the above scenarios, reduce the speed, and automatically adjust the sensitivity of distance perception, warning, and braking to the highest. At the same time, adjust to a conservative driving style, reduce the frequency of lane changes, and send a notification reminder to the driver. Based on the above coarse adjustment, the system continues to make fine adjustments, optimize and adjust the braking curve, and improve driving comfort.

[0092] S33) Complex traffic game. In the previous step, if the analysis result of the current situation falls in area 10, area 40 or area 20, it means that the current vehicle is in a dangerous special scene, and no complex traffic situation judgment and game control are performed; if the analysis result of the current situation falls in area 30, it means that the current weather and road conditions are at a low risk level, and the system automatically judges the complex traffic situation and controls the game. The game strategy means that the system knows from the information of the fusion perception module 1 that if the vehicle is currently in a congested traffic situation, at the following speed of the adaptive calibration threshold, the following distance will be appropriately reduced while ensuring the braking distance to prevent being squeezed in by the vehicle next to it. If the system identifies special vehicles such as ambulances and police cars or vehicles next to it that are extremely close to squeeze in, the game strategy will be canceled to leave parking space for passing.

[0093] S4) The adjustment control module 3 executes a corresponding control response after receiving the adaptive control adjustment instruction sent by the calculation processing module 2.

[0094] Adjust the associated components and main signals of the control module 3 such as Figure 7 shown.

[0095] Specifically, the chassis control execution system 31 of the adjustment control module 3 and the cockpit human-computer interaction system 32, including the vehicle drive 311, steering 312, brake 313 systems and various actuators and associated parts, the vehicle instrument screen 321, the central control screen 322, the HUD 323, the ambient light 324, the audio 325 and other in-cabin equipment, perform corresponding control responses after receiving the adaptive control adjustment command issued by the calculation and processing module 2 in the previous step. The cockpit human-computer interaction system 32 promptly sends a combination of multiple optical and audio reminders to the driver to inform the occupants of the current special environmental conditions and the adjustment measures taken by the system. At the same time, the chassis control execution system 31 also performs targeted adjustment and control of the longitudinal and lateral movements of the vehicle according to the instructions, helping the driver to better cope with special scenes and ensure driving safety.

[0096] Specifically, the chassis control execution system 31 includes vehicle drive 311, steering 312, braking 313 systems and various actuators and related parts. After receiving the instructions issued by the calculation and processing module 2, each line control unit responds to the corresponding acceleration and deceleration and steering handshake request signal, and adjusts the acceleration and deceleration, torque, turning angle, angular velocity and related turn signal parameters such as turn signals and brake lights in time according to the instructions. The numerical value and status bit, perform targeted adjustment and control of the longitudinal and lateral movement of the vehicle.

[0097] Specifically, the cockpit human-machine interaction system 32 mainly includes the vehicle instrument screen 321, the central control screen 322, the HUD 323, the ambient light 324, the audio 325 and other in-cabin equipment. After receiving the command issued by the computing and processing module 2, it promptly sends a combination of optical and audio reminders to the driver, informing the occupants of the current special environmental conditions and the adjustment measures taken by the system, helping the driver understand the vehicle behavior and judge the timing of taking over. The user can personalize the specific reminder method, such as the pop-up text and style, display position, duration, the color and flashing frequency of the ambient light 324, the sound tone volume and the complexity / simplicity of the broadcast, etc., and can also customize the tactile reminders, such as automatic tightening of the seat belt, etc.

[0098] S5) Exit intelligent driving adaptive control.

[0099] The present invention has the following beneficial effects:

[0100] 1) It can timely and accurately identify special scenarios such as different road surfaces, weather, and traffic, and automatically implement targeted human-machine interaction reminders and vehicle motion control adjustment strategies. It has powerful environmental adaptation and instant adjustment capabilities, making driving styles more intelligent and rich;

[0101] 2) Effectively reduce the risk of accidents in special scenarios such as slippery roads and bad weather, improve the driver's driving confidence and the experience of using the intelligent driving function, help the driver to better cope with special scenarios to the greatest extent, ensure driving safety, further improve the ability boundary, safety and stability of the intelligent driving system, and become the main highlight to attract consumers and the driving engine to promote consumption;

[0102] 3) When faced with complex traffic scenarios such as congestion, game thinking and reasoning strategies can be adopted to make targeted and effective response and adjustment measures in a timely manner to avoid being cut in by other vehicles to the greatest extent possible, thereby improving traffic efficiency while also improving safety and avoiding collisions;

[0103] 4) It has multi-source perception information input and redundant comparison reference, synchronously collects local direct and indirect perception data of the vehicle, online network big data, and crowdsourcing system information, comprehensively refers to current information and forecast information, and realizes accurate and timely identification and judgment of various weather, road, traffic and other conditions;

[0104] 5) Get rid of the dependence on model training data, have clear, logical, and systematic rules to provide a bottom-line guarantee, and deal with special scenarios and dangerous situations without uncertainty and randomness, so that stable and effective risk pre-judgment and timely avoidance and adjustment can be carried out;

[0105] 6) Propose an innovative regulation and control algorithm, introduce an environmental factor distribution index based on weather and road conditions and an adaptively adjustable interval division standard, integrate a real-time variable traffic game strategy, and perform step-by-step progressive adaptive control through the evaluation function coefficient.

[0106] The above-described embodiments are only further explanations of the present invention, and are not intended to limit the present invention in other forms. The present invention may also have other various embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art may make various corresponding modifications and changes according to the present invention, but these corresponding modifications and changes should all fall within the protection scope of the present invention.

[0107] In the description of the present application, it should be noted that the terms used herein are only for describing specific embodiments, and are not intended to limit the exemplary embodiments according to the present application. For ease of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship. The technology, methods and equipment known to ordinary technicians in the relevant field may not be discussed in detail, but in appropriate cases, the technology, methods and equipment should be regarded as part of the authorization specification. In all examples shown and discussed here, any specific value should be interpreted as merely exemplary, rather than as a limitation. Therefore, other examples of the exemplary embodiments may have different values. It should be noted that similar numbers and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

[0108] It should be noted that, in the present application, the terms "comprises", "includes" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. It should also be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved. For example, the described method may be performed in an order different from that described, and various steps may be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0109] The above embodiments are provided for persons familiar with the art to implement or use the present application. Personnel familiar with the art can make various modifications or changes to the above embodiments without departing from the application concept of the present application. Therefore, the protection scope of the present application is not limited to the above embodiments, but should be the maximum scope of the innovative features mentioned in the claims.

Claims

1. An intelligent driving adaptive control method, characterized in that: The method comprises the following steps: S1) The vehicle is started and whether the intelligent driving adaptive control system is activated; S2) If the adaptive control system is activated, the fusion perception module collects local data and online network information on the vehicle side in real time, integrates multi-source information, and accurately identifies road, traffic and weather conditions; S3) the computing and processing module uses the fusion information to make a quick comprehensive judgment and analysis of the current environment, makes an optimal decision based on the current scenario, and issues a targeted adaptive control adjustment instruction to the adjustment control module; S4) the adjustment control module executes a corresponding control response after receiving the adaptive control adjustment instruction sent by the calculation processing module; S5) Exit intelligent driving adaptive control.

2. The intelligent driving adaptive control method according to claim 1, characterized in that: The step S3) includes integrating the environmental factor distribution index based on weather and road conditions and the adaptively adjustable interval division standard, integrating the real-time variable traffic game strategy, and performing step-by-step progressive adaptive control through the evaluation function coefficient.

3. The intelligent driving adaptive control method according to claim 2, characterized in that: The evaluation function is Y=AX1+BX2, X1 represents the braking force, X2 represents the braking time (TTC), the coefficient A is the dynamic gain of the braking force, and the coefficient B is the time weight of the braking time. The two work together to achieve step-by-step progressive adaptive control.

4. The intelligent driving adaptive control method according to claim 3, characterized in that: The step S3) further comprises: S31) Comprehensively process the multi-source information of the fusion perception module, and intelligently score and comprehensively calculate the current weather conditions and road conditions according to the principle of "the more unfavorable to driving safety, the higher the score", and respectively obtain the current weather index and road condition index, and at the same time determine the danger level of the current situation by referring to the actual vehicle test calibration, combining the actual performance of the vehicle and the real-time scene analysis and judgment of the intelligent driving AI controller; S32) The system performs adaptive rough adjustment and adaptive fine adjustment on coefficients A and B in sequence according to the distribution of environmental factors obtained in the previous step and the determined danger level. First, the system performs rough adjustment on coefficients A and B to prevent the vehicle from exceeding the adhesion limit and thus slipping. At the same time, the system determines the rear-end collision risk in combination with the perception information to improve driving safety. Based on the above rough adjustment, the system continues to perform fine adjustment to optimize and adjust the braking curve to improve driving comfort. S33) If the current vehicle is in a special dangerous scenario, no complex traffic situation judgment and game control will be performed; if the current weather and road conditions are both at a low risk level, the system will automatically judge the complex traffic situation and game control. According to the information from the fusion perception module, when the vehicle is in a congested traffic situation, the system will appropriately reduce the following distance while ensuring the braking distance. If the system identifies a special vehicle or a neighboring vehicle that is extremely close to the vehicle, the game strategy will be canceled to leave parking space for passing.

5. The intelligent driving adaptive control method according to claim 4, characterized in that: The step S31) determines the danger level dividing lines a1, a2 and b1, b2 of the current situation, and divides the weather index and road condition index coordinates into four areas 10, 20, 30, and 40. Area 30 indicates that the current weather and road conditions are both at a low danger level, area 10 indicates that the current weather conditions are at a high danger level, area 40 indicates that the current road conditions are at a high danger level, and area 20 indicates that the current weather and road conditions are both at a high danger level.

6. The intelligent driving adaptive control method according to claim 5, characterized in that: The step S32) further comprises: S321) If the analysis result of the current situation falls in area 30, it means that the current weather and road conditions are both at a low risk level, and the system does not need to take special targeted adaptive adjustment measures, and continues to perform conventional intelligent driving function operations; S322) If the analysis result of the current situation falls in area 10, it means that the current weather conditions are at a high risk level and the road conditions are at a low risk level. At this time, the system's perception ability is limited, and the coefficient A needs to be incrementally adjusted according to the actual vehicle standard and real-time scene analysis, reducing the vehicle speed, and increasing the braking force when encountering obstacles to achieve collision avoidance. At the same time, the sensitivity of distance perception, warning, and braking is automatically adjusted to the highest, adjusted to a conservative driving style, reducing the frequency of lane changes, and promptly sending reminders to the driver to inform him of the current environment and the adjustment measures taken by the system, so as to help the driver understand the vehicle behavior and determine the timing of taking over; S323) If the analysis result of the current situation falls in area 40, it means that the current road condition is at a high risk level and the weather condition is at a low risk level. At this time, the adhesion coefficient is reduced, and the coefficient B needs to be incrementally adjusted according to the actual vehicle mark quantitative and real-time scene analysis to maintain a larger vehicle distance. While taking into account the distance to the rear vehicle to prevent rear-end collision, the driver is given a warning and reminder in advance and brakes are applied. At the same time, a conservative driving style is adjusted to reduce the frequency of lane changes, and a notification reminder is issued to the driver; S324) If the analysis result of the current situation falls in area 20, it means that the current weather and road conditions are at a high risk level. At this time, the system's perception ability is limited, and the adhesion coefficient is reduced. It is necessary to comprehensively deal with the above scenarios, reduce the vehicle speed, and automatically adjust the sensitivity of distance perception, warning, and braking to the highest. At the same time, adjust to a conservative driving style, reduce the frequency of lane changes, and send a notification reminder to the driver.

7. An intelligent driving adaptive control system, characterized in that: It includes a fusion perception module, a calculation and processing module and an adjustment control module. The fusion perception module recognizes and predicts the current environmental conditions by real-time fusion of local vehicle data and online network information. The calculation and processing module makes a judgment on the current environment, makes the best decision according to the current scenario, and sends an adaptive control adjustment instruction to the adjustment control module. The control adjustment module executes the corresponding control response according to the adaptive adjustment control instruction.

8. The intelligent driving adaptive control system according to claim 7, characterized in that: The fusion perception module includes a self-vehicle perception system and an intelligent network connection system. The self-vehicle perception system includes: cameras, sunlight and rain sensors, wipers, lights, air conditioners, temperature and humidity sensors, lidars, chassis sensors, body sensors, navigation systems, and millimeter-wave radars. The intelligent network connection system includes a navigation system and online weather, road, and traffic information networks and crowdsourcing big data systems.

9. The intelligent driving adaptive control system according to claim 7, characterized in that: The computing and processing module includes an intelligent driving AI controller.

10. The intelligent driving adaptive control system according to claim 7, characterized in that: The adjustment control module includes a chassis control execution system and a cockpit human-computer interaction system. The chassis control execution system includes vehicle drive, steering, braking systems and various actuators and related parts. The cockpit human-computer interaction system includes a vehicle instrument screen, a central control screen, a HUD, ambient lights and audio.

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