An integrated multi-function advanced driver assistance system

Abstract

The application discloses a kind of integrated multifunctional advanced automatic driving auxiliary system, including five kinds of vehicle driving modes of constant speed cruise mode (CC), low performance adaptive cruise mode (LACC), high performance adaptive cruise mode (HACC), autonomous lane changing mode (ALC), automatic emergency braking mode (AEB). Vehicle detects the state information of surrounding vehicles and environment through sensing device, calculates characteristic quantity and correlation function in real time through extension control strategy, determines the extension set and measure mode of belonging, and further determines the driving mode of vehicle. When the state information of surrounding vehicles and environment changes, vehicle can switch between different driving modes. When the speed of current vehicle continues to drop and the surrounding environment meets the lane changing condition, vehicle can automatically switch to autonomous lane changing mode (ALC), switch the following object, and improve driving efficiency. When the current vehicle stops due to failure or suddenly appears obstacle in front, vehicle can automatically switch to automatic emergency braking (AEB) mode, shorten the reaction time, and improve driving safety.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automatic driving, in particular to an integrated multifunctional advanced automatic driving assistance system. BACKGROUND

[0002] In recent years, the automatic driving technology has developed rapidly, which is conducive to relieving traffic congestion, improving road traffic efficiency, reducing traffic accidents, improving traffic safety, and improving fuel economy. Among them, the adaptive cruise control (ACC) system as one of the driving assistance functions also plays a very important role in improving traffic efficiency and safety.

[0003] However, the implementation of the conventional ACC function is limited to a single lane, and the vehicle can only passively follow the front vehicle in the current lane. When the front vehicle slows down due to failure or traffic congestion, the vehicle still slows down to follow the front vehicle, which does not conform to the driving thinking of normal human drivers, does not consider the traffic situation of the adjacent lane, and does not fully utilize the resources of the adjacent lane, resulting in a decrease in road traffic efficiency. When the driving state of the front vehicle changes, the vehicle should autonomously decide to select a following object. If the road traffic condition of the adjacent lane is better than that of the current lane, the vehicle should consider switching the following object when the front vehicle slows down, so as to change lanes and drive, which can effectively improve the road traffic efficiency.

[0004] At the same time, the conventional ACC function is difficult to apply in extreme conditions, especially when the front vehicle suddenly slows down or an obstacle suddenly appears in front, the control right of the vehicle needs to be transferred to the human driver. After taking over the vehicle, the human driver needs a certain reaction time to make a decision of emergency braking or emergency steering, which may cause an accident due to untimely collision avoidance. If the vehicle can autonomously judge the emergency degree of the current driving environment and automatically enter the emergency collision avoidance state, the reaction time of the vehicle can be effectively reduced, the success rate of collision avoidance can be improved, and the safety of the vehicle driving process can be improved. SUMMARY

[0005] In view of the above problems, the present application provides an integrated multifunctional advanced automatic driving assistance system, which can detect the driving state of the vehicle in the current lane and the adjacent lane in real time, autonomously select a following object, and improve the road traffic efficiency. On the other hand, it can judge the emergency state of the driving environment, and when the collision risk is high, it can automatically switch to the automatic emergency braking state to ensure the safety of the vehicle driving process.

[0006] Technical scheme:

[0007] The application discloses an integrated multifunctional advanced automatic driving assistance system, which comprises five vehicle driving modes (vehicle driving mode technology itself is prior art in the field and is not the innovation point of the application), namely, a constant speed cruise mode (CC), a low performance adaptive cruise mode (LACC), a high performance adaptive cruise mode (HACC), an autonomous lane change mode (ALC) and an automatic emergency braking mode (AEB). The vehicle detects state information of surrounding vehicles and environment through a sensing device (the sensing device is an existing device in the field and is not the innovation point of the application), and determines a corresponding vehicle driving mode through a three-dimensional extension controller (the controller is an existing device in the field and is not the innovation point of the application) which can extend control strategies, calculate characteristic quantities and correlation functions in real time, determine a corresponding extension set and measure mode, and further determine a suitable vehicle driving mode. When the state information of surrounding vehicles and environment changes, the vehicle can be switched among the five different driving modes. When the current vehicle speed continuously decreases and the surrounding environment meets the lane change condition, the vehicle can be automatically switched to the autonomous lane change mode (ALC) to switch a following vehicle and improve driving efficiency; when the current vehicle is suddenly stopped due to a fault or an obstacle suddenly appears in front of the vehicle, the vehicle can be automatically switched to the automatic emergency braking mode (AEB) to shorten the reaction time and improve driving safety.

[0008] The sensing system is formed by sensing devices on the vehicle, and the sensing system automatically detects the speed and distance of a vehicle in front of a current lane and vehicles in front of and behind a neighboring lane after the vehicle is started.

[0009] During the process that the vehicle follows the front vehicle, if the driving state of surrounding vehicles changes, such as the front vehicle accelerates, decelerates or stops, the vehicle is switched among driving modes through an extension control strategy.

[0010] The driving modes comprise a constant speed cruise mode (Cruise Control, CC), a low performance adaptive cruise mode (Low Performance Adaptive Cruise Control, LACC), a high performance adaptive cruise mode (High Performance Adaptive Cruise Control, HACC), an automatic emergency braking mode (Autonomous Emergency Braking, AEB) and an autonomous lane change mode (Autonomous Lane Change, ALC).

[0011] The CC mode refers to the vehicle traveling at a given target speed; the LACC mode refers to the vehicle following the vehicle in front in the same lane while maintaining a safe distance when the input error is small, which is easy to control and has low performance requirements for the control algorithm; the HACC mode refers to the vehicle following the vehicle in front in the same lane while maintaining a safe distance when the input error is large, which is difficult to control and has high performance requirements for the control algorithm; the AEB mode refers to the vehicle rapidly decelerating to a stop according to the calculated braking deceleration; and the ALC mode refers to the vehicle switching to an adjacent lane according to a planned lane-changing trajectory.

[0012] The extension control strategy, as a software module, runs in the controller of the vehicle's autonomous driving assistance system and is used for switching driving modes.

[0013] The aforementioned extension control strategy, specifically, includes four steps in its algorithm:

[0014] Step 1, extract feature quantities:

[0015] The extended control strategy comprises three characteristic quantities τ, σ, and ξ, forming the characteristic quantity state S(τ,σ,ξ). Sensing data is provided to the controller in real time via sensing devices, and the controller calculates and extracts the characteristic quantities according to the following formula:

[0016] The feature quantity τ is inversely proportional to the collision time (TTC), as expressed by the following formula:

[0017]

[0018] The characteristic quantity σ is inversely proportional to the longitudinal distance d between the vehicle and the vehicle in front, as expressed by the following formula:

[0019]

[0020] Wherein, the characteristic quantity ξ is the distance d between the vehicle and the vehicle behind in the adjacent lane. nr Let be a function of the independent variable, and the formula is as follows:

[0021]

[0022] Where, d min For the minimum braking distance, v nr t represents the speed of the vehicle following in the adjacent lane. LC v represents the time taken for the vehicle to change lanes, and v represents the vehicle's speed.

[0023] Step 2, construct and partition the extension set:

[0024] Based on the feature quantity extracted in step 1, a three-dimensional extension set is constructed, which is divided into a classic field, an extension field and a non-field. Different extension sets correspond to different vehicle driving modes. The extension set is represented by a three-dimensional Cartesian coordinate system, where the X-axis represents the feature quantity τ, the Y-axis represents the feature quantity σ, and the Z-axis represents the feature quantity ξ.

[0025] In the X-axis, Y-axis and Z-axis coordinate directions, there are critical values between different extension sets. The formula of the critical value is as follows:

[0026]

[0027]

[0028] Z:ξ emin =-v nrmax ·t LC -d min ,ξ emax =d max +v max -d min

[0029] where d s is the safety distance between the ego vehicle and the preceding vehicle, which is calculated by a selected safety distance algorithm (which is itself prior art in the field and not an innovation point of the present application), TTC nf is the collision time between the ego vehicle and the preceding vehicle in the adjacent lane, TTC min is the minimum collision time between the ego vehicle and the preceding vehicle, d max is the maximum distance that can be detected by the ego vehicle sensor, d nf is the longitudinal distance between the ego vehicle and the preceding vehicle in the adjacent lane, d min is the shortest distance between the ego vehicle and the preceding vehicle, v nrmax is the maximum speed of the rear vehicle in the adjacent lane, τ om , τ e , τ m are the boundary values of the feature quantity τ for dividing the classic field, the extension field and the non-field, σ om , σ e , σ m are the boundary values of the feature quantity σ for dividing the classic field, the extension field and the non-field, ξ emin , ξ emax are the upper and lower bounds of the feature quantity ξ in the classic field, the extension field and the non-field.

[0030] The range of the classic field corresponding to the feature state S(τ,σ,ξ) in step 1 is:

[0031]

[0032] The range of the extension field corresponding to the feature state S(τ, σ, ξ) in step 1 is:

[0033]

[0034] The extension field is further subdivided into extension fields and The formula is as follows:

[0035]

[0036]

[0037] The range of the non-field corresponding to the feature state S(τ, σ, ξ) in step 1 is:

[0038]

[0039] Step 3, design the correlation function:

[0040] The origin of the three-dimensional extension coordinate system is defined as S0(0, 0, 0), and the two-dimensional extension distance of a point P in space is The two-dimensional extension distance of the outer boundary line intersection point of the classical field in the X, Y direction is The two-dimensional extension distance of the outer boundary intersection point of the extension field in the X, Y direction is The two-dimensional extension distance of the outer boundary intersection point of the extension field in the X, Y direction is

[0041] The correlation functions K1(S) and K2(S) are defined, and the formula is as follows:

[0042]

[0043] Step 4, measure pattern recognition:

[0044] According to the calculation results of the designed correlation functions K1(S) and K2(S), measure pattern recognition is carried out, which is divided into five measure patterns: M1, M 21 , M 22 , M 23 , and M3. The formula is as follows:

[0045] M1: {S | K1(S) ≥ 0}

[0046] M 21 : {S | α ≤ K1(S) < 0}

[0047] M 22 : {S | -1 ≤ K1(S) < 0, K2(S) ≤ 0}

[0048] M​23 :{S|-1≤K1(S)<0,K2(S)>0}

[0049] M3:{S|K1(S)<-1}

[0050] wherein,

[0051] When K1(S)≥0, the measure mode is M1, and the characteristic state is in the classical domain R os . At this time, the distance between the ego vehicle and the front vehicle is greater than the safe vehicle distance, the speed of the front vehicle is high, the collision risk is minimal, and the vehicle is in the safest state, which can be switched to the CC mode to travel at the set speed.

[0052] When α≤K1(S)<0, the measure mode is M 21 , and the characteristic state is in the extension domain R s1 . At this time, the distance between the ego vehicle and the front vehicle is slightly less than the safe vehicle distance, and the vehicle should control the ego vehicle to maintain a safe distance from the front vehicle. At the same time, the distance between the ego vehicle and the front vehicle is greater than the longitudinal distance between the ego vehicle and the front vehicle in the adjacent lane, which is not sufficient to generate a lane change intention, and the TTC of the ego vehicle and the front vehicle is greater than the TTC of the ego vehicle and the front vehicle in the adjacent lane, and the lane change risk is greater. In the M 21 measure mode, the error between the actual vehicle distance and the ideal vehicle distance of the ACC system is small, the control difficulty is low, a relatively simple control algorithm can be selected, and the LACC mode is switched to travel, and the ego vehicle follows the front vehicle at a reduced speed.

[0053] When -1≤K1(S)<0,K2(S)≤0, the measure mode is M 22 , and the characteristic state is in the extension domain R s2 . At this time, the distance between the ego vehicle and the front vehicle is less than the safe vehicle distance, and the vehicle should control the ego vehicle to maintain a safe distance from the front vehicle. At the same time, the distance between the ego vehicle and the front vehicle is less than the longitudinal distance between the ego vehicle and the front vehicle in the adjacent lane, the TTC of the ego vehicle and the front vehicle is greater than the TTC of the ego vehicle and the front vehicle in the adjacent lane, and the ego vehicle has a certain risk of lane change. In the measure mode, the error between the actual vehicle distance and the ideal vehicle distance of the ACC system is large, the control difficulty is high, a control algorithm with more precise control effect should be selected, or the parameters should be adjusted to improve the performance of the control algorithm. The ego vehicle should be switched to the HACC mode to travel and follow the front vehicle at a reduced speed.

[0054] When -1≤K1(S)<0,K2(S)>0, the measure mode is M 23 , and the characteristic state is in the extension domain R s3At this time, the distance between the ego vehicle and the front vehicle is less than the safe distance, and the vehicle should control the ego vehicle to keep a safe distance from the front vehicle, but the distance between the ego vehicle and the front vehicle is less than the longitudinal distance between the ego vehicle and the front vehicle in the adjacent lane, and the TTC between the ego vehicle and the front vehicle is also less than the TTC between the ego vehicle and the front vehicle in the adjacent lane, the ego vehicle meets the lane changing condition, the risk is small, and therefore the ego vehicle switches to the ALC mode, uniformly changes lanes to the adjacent lane, and follows the front vehicle in the adjacent lane. Compared with before lane changing, the distance between the ego vehicle and the front vehicle increases after lane changing, the collision time becomes longer, the average vehicle speed level of the ego vehicle is improved, and the road traffic efficiency is also improved.

[0055] At this time, the measure mode is, and the feature state is in the non-domain. At this time, the distance between the ego vehicle and the front vehicle is less than the minimum braking distance and the TTC is less than the minimum collision time TTC min , the collision risk is extremely high, and therefore the vehicle should switch to the AEB mode and immediately slow down to drive, thereby avoiding collision to the greatest extent and improving the driving safety of the vehicle.

[0056] The application is a kind of advanced automatic driving auxiliary system, which integrates CC, ACC, ALC, AEB and other driving auxiliary functions, and can effectively improve the traffic efficiency of the road and the safety during the driving of the vehicle. The ego vehicle detects the running state of the vehicle in the current lane and the adjacent lane in real time during driving, and determines the driving mode of the vehicle. When the ego vehicle follows the front vehicle, if the current vehicle speed slightly decreases, the ego vehicle speed will also decrease if the ego vehicle keeps the current driving mode. At this time, the ego vehicle detects the running state of the vehicle in the adjacent lane, and if there is a better following object in the adjacent lane and the lane changing condition is met, the ego vehicle changes lanes to follow the front vehicle in the adjacent lane, and the vehicle speed will not decrease, thereby improving the road traffic efficiency. When the ego vehicle follows the front vehicle, if the current vehicle suddenly stops due to failure or suddenly appears an obstacle in front of the road, the vehicle can automatically switch to the AEB mode and brake urgently to avoid collision. The control right of the vehicle does not need to be transferred to the driver, the reaction time is saved, the reaction speed of the vehicle to the emergency working condition is improved, and the driving safety of the vehicle is improved. BRIEF DESCRIPTION OF DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the embodiments of the application, the drawings needed in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can also be obtained according to these drawings without creative labor for those skilled in the art.

[0058] Figure 1 is a schematic diagram of a kind of integrated multifunctional advanced automatic driving auxiliary system provided by the embodiments of the application.

[0059] Figure 2 is the algorithm steps of the control strategy of the application.

[0060] Figure 3 is a flow chart of mode switching provided by an embodiment of the present application.

[0061] Figure 4 is a driving environment diagram when an embodiment of the present application is applied.

[0062] Reference signs: 101 is a classical domain R os , 102 is an extension domain R s1 , 103 is an extension domain R s2 , 104 is an extension domain R s3 , 105 is a non-domain R n . DETAILED DESCRIPTION

[0063] The technical solutions in the present application will be further described below in combination with the drawings. It should be noted that the specific embodiments described herein are limited to illustrating and explaining the present application, and belong to one implementation manner of the present application, and do not limit the application of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor also belong to the protection scope of the present application.

[0064] An application embodiment of the present application will be specifically described below in combination with Figure 1 , Figure 2 , Figure 3 and Figure 4 :

[0065] Figure 1 is a schematic diagram of an integrated multi-functional advanced automatic driving assistance system, which clearly shows the relative position relationship of the five driving modes of CC, LACC, ALC, HACC and AEB in the extension set. Among them, Figure 1 (b) is Figure 1 the left side view of (a). The five driving modes of CC, LACC, ALC, HACC and AEB are in one-to-one correspondence with the extension set through the correlation function. The X axis, Y axis and Z axis in the figure correspond to the characteristic quantities τ, σ and ξ respectively. When the change amount of the value of the characteristic quantity does not exceed the range of the extension set to which it belongs, the vehicle maintains the current driving mode; when the change amount of the value of the characteristic quantity exceeds the range of the extension set to which it belongs, the driving mode in which the vehicle is located changes accordingly, and the vehicle can switch between adjacent driving modes.

[0066] The ego vehicle is a vehicle equipped with advanced sensing devices such as millimeter wave radar, laser radar, etc., and has an L2 level of automatic driving. Initially, the ego vehicle is in a parking state. After the vehicle starts, the sensing system automatically detects the speed, distance, etc. of the vehicle in front of the current lane and the vehicles in front and rear of the adjacent lane. According to the detected information, the characteristic quantity and the correlation function are calculated, the measure mode is identified, and the current driving mode of the vehicle is determined.

[0067] For example, after the vehicle starts, it is in the CC mode, and the vehicle travels at the ideal speed calculated by the upper decision module. The ideal speed can be a constant value or a time-varying curve. If there is no vehicle in front or the distance between the ego vehicle and the front vehicle exceeds the maximum detection range of the sensing device, the ego vehicle remains in the CC mode.

[0068] Suppose the front vehicle in the current lane is beyond the maximum distance that the ego vehicle can sense, and the speed of the front vehicle gradually decreases. When the ego vehicle sensing system detects the front vehicle, it is known through the calculation of the characteristic quantity and the correlation function that the current measure mode is M 21 , the vehicle switches to the LACC mode, and the ego vehicle follows the front vehicle. At this time, the input error of the adaptive cruise control system is small, the control difficulty is low, and a low-performance control algorithm can be used to improve efficiency.

[0069] As the speed of the front vehicle gradually decreases, the error between the distance between the ego vehicle and the front vehicle and the ideal distance increases, the TTC between the ego vehicle and the front vehicle increases, and the adaptive cruise control of the vehicle becomes more difficult. The vehicle should switch to the HACC mode or the ALC mode. According to the vehicle motion state information of the current lane and the adjacent lane detected by the ego vehicle through the sensing device, the values of the corresponding characteristic quantity and correlation function are calculated. If the TTC and the distance between the ego vehicle and the front vehicle in the adjacent lane are both greater than the TTC and the distance between the ego vehicle and the front vehicle in the current lane, and the rear vehicle in the adjacent lane has a small speed and a long distance, the characteristic state is in the extension domain , the measure mode is M 23 , and the vehicle should switch to the ALC mode to change lanes. At this time, the motion state between the vehicles is as shown in Figure 4 (a); if the motion state of the front and rear vehicles in the adjacent lane does not meet the lane changing condition, the characteristic state is in the extension domain , the measure mode M 22 , and the vehicle should switch to the HACC mode. At this time, the motion state between the vehicles is as shown in Figure 4 (b), and a high-performance control algorithm is applied to follow the front vehicle.

[0070] Suppose the vehicle is currently in the HACC mode, and the front vehicle is emergency braking due to a fault. At this time, the characteristic state belongs to the non-domain, the measure mode is AEB, and the vehicle should switch to the AEB mode. The motion state between the vehicles is as shown in Figure 4 (c), and the vehicle immediately emergency brakes to avoid collision to the greatest extent.

[0071] The above is only one switching mode of the vehicle driving mode, and different driving modes can be switched arbitrarily, and the driving state of the ego vehicle and surrounding vehicles determines.

Claims

1. An integrated multi-function advanced driver assistance system, characterized by, The vehicle driving mode includes five driving modes: a constant speed cruise mode (CC), a low performance adaptive cruise mode (LACC), a high performance adaptive cruise mode (HACC), an autonomous lane change mode (ALC), and an automatic emergency braking mode (AEB); The vehicle driving mode includes five driving modes: a constant speed cruise mode (CC), a low performance adaptive cruise mode (LACC), a high performance adaptive cruise mode (HACC), an autonomous lane change mode (ALC), and an automatic emergency braking mode (AEB); The vehicle driving mode includes five driving modes: a constant speed cruise mode (CC), a low performance adaptive cruise mode (LACC), a high performance adaptive cruise mode (HACC), an autonomous lane change mode (ALC), and an automatic emergency braking mode (AEB); The vehicle driving mode includes five driving modes: a constant speed cruise mode (CC), a low performance adaptive cruise mode (LACC), a high performance adaptive cruise mode (HACC), an autonomous lane change mode (ALC), and an automatic emergency braking mode (AEB); The vehicle driving mode includes five driving modes: a constant speed cruise mode (CC), a low performance adaptive cruise mode (LACC), a high performance adaptive cruise mode (HACC), an autonomous lane change mode (ALC), and an automatic emergency braking mode (AEB); The vehicle driving mode includes five driving modes: a constant speed cruise mode (CC), a low performance adaptive cruise mode (LACC), a high performance adaptive cruise mode (HACC), an autonomous lane change mode (ALC), and an automatic emergency braking mode (AEB); The controllable strategy contains three characteristic quantities , and , and a characteristic state ; the perception data are provided to the controller in real time by the perception device, and the controller calculates and extracts the characteristic quantities according to the following formula: wherein the feature quantity is inversely proportional to the time-to-collision (TTC), which is expressed by the following formula: Wherein the characteristic quantity is inversely proportional to the longitudinal distance between the ego vehicle and the preceding vehicle , which is expressed by the following formula: Among them, the characteristic quantity is the distance between the ego vehicle and the rear vehicle of the adjacent lane is a function of the independent variable, and the formula is as follows: wherein, is the minimum braking distance, is the speed of the vehicle behind the adjacent lane, is the time experienced by the ego vehicle during the lane changing process, is the ego vehicle speed; The vehicle driving mode includes five driving modes: a constant speed cruise mode (CC), a low performance adaptive cruise mode (LACC), a high performance adaptive cruise mode (HACC), an autonomous lane change mode (ALC), and an automatic emergency braking mode (AEB); A three-dimensional extension set is constructed based on the feature quantity extracted in step 1, the extension set is divided into a classic field, an extension field, and a non-field, different extension sets correspond to different vehicle driving modes; the extension set is expressed by a three-dimensional Cartesian coordinate system, an X axis represents a feature quantity , a Y axis represents a feature quantity , and a Z axis represents a feature quantity . The vehicle driving mode includes five driving modes: a constant speed cruise mode (CC), a low performance adaptive cruise mode (LACC), a high performance adaptive cruise mode (HACC), an autonomous lane change mode (ALC), and an automatic emergency braking mode (AEB); wherein, is the safety distance between the ego vehicle and the front vehicle, calculated by a selected safety distance algorithm, is the collision time between the ego vehicle and the front vehicle in the adjacent lane, is the minimum collision time between the ego vehicle and the front vehicle, is the maximum distance that the sensor of the ego vehicle can detect, is the longitudinal distance between the ego vehicle and the front vehicle in the adjacent lane, is the shortest distance between the ego vehicle and the front vehicle, is the maximum speed of the rear vehicle in the adjacent lane, is the characteristic quantity is the boundary value of the classical domain, the extension domain and the non-domain, is the characteristic quantity is the boundary value of the classical domain, the extension domain and the non-domain, is the characteristic quantity is the upper and lower limit of the value of the classical domain, the extension domain and the non-domain; The vehicle driving mode includes five driving modes: a constant speed cruise mode (CC), a low performance adaptive cruise mode (LACC), a high performance adaptive cruise mode (HACC), an autonomous lane change mode (ALC), and an automatic emergency braking mode (AEB); The origin of the three-dimensional extension coordinate system is defined as The two-dimensional extension distance of a point P in space is The two-dimensional extension distance of the intersection point of the outer boundary line of the classical field in the X, Y direction is The two-dimensional extension distance of the intersection point of the outer boundary of the extension field in the X, Y direction is The two-dimensional extension distance of the intersection point of the outer boundary of the extension field in the X, Y direction is The two-dimensional extension distance of the intersection point of the outer boundary of the extension field in the X, Y direction is ​ Defining the correlation function and The formula is expressed as follows: The vehicle driving mode includes five driving modes: a constant speed cruise mode (CC), a low performance adaptive cruise mode (LACC), a high performance adaptive cruise mode (HACC), an autonomous lane change mode (ALC), and an automatic emergency braking mode (AEB); According to the design of the correlation function and The results of the calculation are measured pattern recognition, a total of five measurement mode: The formula is as follows: wherein ; When the measure mode is , switch to CC mode driving; When , the measure mode is , switch to LACC mode driving; When the measure mode is , switch to HACC mode driving; When , the measure mode is , switch to ALC mode; When the measure mode is , the feature state is in the non-domain, switch to the AEB mode. The vehicle driving mode includes five driving modes: a constant speed cruise mode (CC), a low performance adaptive cruise mode (LACC), a high performance adaptive cruise mode (HACC), an autonomous lane change mode (ALC), and an automatic emergency braking mode (AEB); The feature states described in step 1 The corresponding classical domain ranges from: The characteristic states described in step 1 The corresponding extension field ranges from: The extension domain is further subdivided into extension domains , and , which are expressed by the following formulas: The feature states described in step 1 The corresponding non-domain ranges are: 。 3. The system of claim 1, wherein, The vehicle driving mode includes five driving modes: a constant speed cruise mode (CC), a low performance adaptive cruise mode (LACC), a high performance adaptive cruise mode (HACC), an autonomous lane change mode (ALC), and an automatic emergency braking mode (AEB); When the measure mode is and the feature state is in the classical domain ; When , the measure mode is , the characteristic state is in the extension domain ; When , the measure mode is , the characteristic state is in the extension domain ; When , the measure mode is , the characteristic state is in the extension domain ; When the measure mode is and the feature state is in the non-domain.

4. The system of claim 1, wherein, The vehicle driving mode includes five driving modes: a constant speed cruise mode (CC), a low performance adaptive cruise mode (LACC), a high performance adaptive cruise mode (HACC), an autonomous lane change mode (ALC), and an automatic emergency braking mode (AEB); 5. The system of claim 1, wherein, ​ ​

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