CA3065617A1 - Method for predicting car-following behavior under apollo platform - Google Patents
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Abstract
Disclosed is a method for predicting a car-following behavior under the Apollo platform; during the construction process disclosed herein, the dynamic information and the static information during vehicle driving are obtained through understanding of the scene; the desired distance and reaction time are obtained by capturing the driver's behavior features, and the defuzzification process of the following-car model is improved using the heuristic search algorithm, and after computing based on the fuzzy inference model, the safety- and- comfort-based optimal solution of the following-car acceleration range is obtained. Meanwhile, the model is tested and validated using the Apollo simulation platform to ensure accuracy and utility of the model. The method as disclosed solves the issues that the conventional schemes utilizing fuzzy theories and artificial neural networks only focus on the velocities and accelerations of the leading car and the following car, as well as the interval therebetween, without consideration of driving environments.
Description
Method for Predicting Car-following behavior Under Apollo Platform FIELD
[0001] The present disclosure relates to car-following behaviors, and more particularly relates to a method for predicting a car-following behavior under the Apollo platform.
BACKGROUND
[0001] The present disclosure relates to car-following behaviors, and more particularly relates to a method for predicting a car-following behavior under the Apollo platform.
BACKGROUND
[0002] Traffic refers to the conveyances and environment for travel. With improvement of economic level, development of scientific and technical level, and acceleration of urbanization, the people's living standards are also improved, which imposes higher demands on travel provision and quality. However, growth and development of first and second-tier cities boost constant increase of urban population density; more and more urban citizens buy automobiles, causing urban traffic loads heavier year on year. Due to limits of city size and road network capacity, urban traffic becomes heavy and jammed, which seriously affects travel and commuting of urban residents. To handle the ever-serious traffic jam, the government has launched a series of policies including: vigorously developing public transportations, restricting use of private vehicles based on license plate numbers, practicing plate-number lottery or auction, charging street-parking, etc., but none of such moves succeeds in deterring car-parc surge. Car following is a common phenomenon in road traffic, especially in traffic jam which makes it impossible to change a lane or overtake. Therefore, study of car-following behaviors helps understand traffic-flow characteristics.
[0003] The theory of following-car model emerged in 1950s. According to the then model, measured vehicle data information was fitted to obtain a mathematical equation. However, a following-car model obtained based on that method has certain limitations. For example, when the data change, the model would become unsuitable any more, which does not facilitate promotion and extension of the model. Therefore, in recent years, a plurality of models have been proposed focusing on internal causes of car-following behaviors, which significantly enriches studies on the traffic-flow theory.
[0004] However, influenced by multiple sources of information, a driver's decision-making and judgment process exhibits a complex non-linear modality during driving, and the driver's psychological decision cannot be described with a simple mathematical expression. Fuzzy theories and artificial neural networks show certain operational advantages in handling complex non-linear issues and also exhibit a good learning capacity under big data samples.
Therefore, the fuzzy theory and artificial neural network are often used for simulating driving behaviors under different environments. However, the current schemes utilizing fuzzy theories and artificial neural networks only focus on the velocities and accelerations of the leading car and the following car, as well as the spacing therebetween, without considering driving environments.
Therefore, the fuzzy theory and artificial neural network are often used for simulating driving behaviors under different environments. However, the current schemes utilizing fuzzy theories and artificial neural networks only focus on the velocities and accelerations of the leading car and the following car, as well as the spacing therebetween, without considering driving environments.
[0005] In April 2017, Baidu released its open platform Apollo for autonomous driving; after iterations of multiple versions, the platform has been enabled for localization, sensing, decision, and simulation. Apollo may help its partners in the automotive and autonomous driving industries to quickly develop a set of their own autonomous driving systems in consideration of vehicles and hardware systems. In the Apollo simulation environment, environment information including traffic signs, index lines, and the relationships with surrounding vehicles may be inputted into Dreamview via corresponding interfaces to thereby construct a driving environment. Besides, the Apollo platform is further enabled for validating the following-car model and optimizing the relevant algorithm through a 3D
visual interface.
SUMMARY
visual interface.
SUMMARY
[0006] An object of the present disclosure is to provide a method for predicting a car-following behavior under the Apollo platform, which solves the issues that the conventional schemes utilizing fuzzy theories and artificial neural networks only focus on the velocities and accelerations of the leading car and the following car, as well as the spacing therebetween, but fail to consider driving environments.
[0007] The present disclosure relates to a method for predicting a car-following behavior under the Apollo platform, specifically comprising:
[0008] Step 1: differentiating scene information in an autonomous driving process of a vehicle into static information and dynamic information, and importing the static information and the dynamic information into Dreamview of the Apollo platform to construct a road scene;
[0009] Step 2: capturing the following-car driver's behavior features in the car-following state, computing a desired distance through a dynamics equation based on the driving data of the following-car driver, and fitting out a reaction time distribution function of the driver under the influence of velocity difference and relative distance using a polynomial regression method;
[0010] Step 3: first, performing fuzzification processing to the captured behavior feature data of the following-car driver using an improved fuzzy inference vehicle model; second, selecting a membership function based on analysis of the following-car driver behavior features, and formulating a fuzzy rule library; third, performing fuzzy inference using the Mamdani model; finally, improving defuzzification using heuristic learning to enhance solution efficiency; and
[0011] Step 4: predicting the following-car acceleration a using the improved vehicle inference model, i.e., the predicted acceleration value; introducing the computed predicted acceleration value a and the real acceleration a into the desired safe distance equation, the ratio a between the desired distance D' of the predicted acceleration value a and the desired distance D of the real acceleration a, and substituting a as the parameter factor into the desired safe distance equation to control feedback adjustment.
[0012] Preferably, wherein Step 1 specifically comprises: obtaining three-dimensional information and motion information of a traffic scene, wherein the three-dimensional .. information of the traffic scene refers to static information in the corresponding scene information ,and the motion information of the traffic scene refers to dynamic information in the scene information; preliminarily constructing a topological structure of the scene, wherein the topological information of the scene includes information such as the number of surrounding vehicles, the lanes occupied by surrounding vehicles, and the distance from road edge; inputting such information into Dreamview via a corresponding interface of Apollo;
configuring paths to specific modules based on the table of Module Output Interface Standards provided by the simulated environment, and performing, by respective modules in the standard, environment construction with reference to the traffic flow and the simulated environment resulting from understanding of the scene.
.. [0013] Preferably, wherein Step 2 specifically comprises:
[0014] Step 2.1 computing the desired distance: let the maximum threshold spacing for the following-car driver to receive the stimulus of the leading car be H., and the desired following spacing of the following car within the spacing Hmax be h,(t) ; the desired spacing should guarantee that when the leading car abruptly stops with the maximum deceleration, the following-car driver's post-reaction braking can safely avoid collision; the condition for preventing collision is:
he(t) +1(t) =r+L+k+x n+12 (t) x2 n (t) 2an+1 2an+1 where he (1) denotes safe spacing,x+1(t) is the velocity of the following car at moment t , denotes the driver' s reaction time, L denotes the vehicle length, k denotes the allowed buffer spacing between the head of the following car and the tail of the leading car (i.e., followed car) after stop, k is a constant, an and an+1 are maximum decelerations of the leading and following cars in the car-following behavior; it is seen from the equation above , that the safe spacing h(1) is dynamically correlated with the velocities of the leading and following cars; the driver desired spacing is:
/r n2 (t) ,L + k) D(t)= max(x1(t) = x12 (t) x + L + k + ________________________ n+
2an+1 2an+1 the larger one of the two distances as derived using the max function is the current driver desired distance;
100151 Step 2.2: computing the reaction time: first, computing the reaction time r based on the time sequence data of variations of the leading car acceleration and the following car acceleration; owing to different reaction time for different individuals, the corresponding reaction time may be inferred; then, each reaction time corresponds to a set of data (velocity difference, relative distance (Ay' Ax)); within the same reaction time, the leading car and the following car are paired; this velocity differences in the set of data include: velocity change and relative distance within the reaction time of each vehicle, wherein the velocity change refers to the velocity difference of each vehicle, while the relative distance is obtained through a relative distance equation based on the velocity difference and the acceleration difference;
finally, fitting out, by polynomial regression, the driver's reaction time distribution function under the influence of velocity difference and relative distance, wherein the reaction time distribution function has different function expressions for individual following-car drivers, e.g., exponential function, Sigmoid function.
100161 Preferably, wherein Step 3 specifically comprises:
[0017] Step 3.1 defining input parameters and output parameters: let the computation equation of the velocity difference be:
Avn = vn ¨ vn+, n the offset difference refers to the difference between the inter-vehicle distance AL(t)at moment t and the following-car desired spacing D(t) , the equation being as follows:
6 = ALn (i) ¨ D n (t) with the velocity difference and the offset difference as the input parameters and the angular velocity of the following car as the output parameter, the velocity difference and offset difference of the following car in the data set are computed based on the road traffic driving data set, and the velocity difference, offset difference and the range of acceleration are found by statistics;
[0018] Step 3.2 Fuzzification: with the velocity difference and the offset difference as the input parameters in the fuzzy inference system and the acceleration of the following car as the output parameter, each input parameter and each output parameter have 7 levels, which are represented as N3, N2, Ni, ZE, PI, P2, and P3, respectively; for the velocity difference in the input parameter, the level P3 represents that the value of the velocity difference is positive and largest; levels P2 and PI represent that the value of the velocity difference is positive but gradually smaller; level ZE represents that the value of velocity difference is 0, while levels Ni, N2, and N3 represent that the values of velocity difference are negative and gradually smaller; for the same reason, the seven levels of another input parameter offset value and output parameter acceleration are identical to the above.
[0019] Step 3.3 Selecting a Membership Function: let x*be an accurate value and A*
represent a converted fuzzy set; then the trigonometric membership function is:
Ix_ RA ) {(1 X* 1) 0a - x* s x - x >
where, it is seen from the trigonometric membership function distribution diagram that, a > ; when Ix 1> , the trigonometric membership function fuzzy set becomes a fuzzy single value; the larger a, the less the influence of variation of x* to PA*(x), i.e. when a is large enough, the method offers a strong enough anti-interference capability;
[0020] Step 3.4 establishing a fuzzy rule library:
the fuzzy relationship is ¨ ¨
wherein: A x: = 74(x) A lAY),in the equation, [A x B]T denotes a dimensional vector formed AxB ¨
by the matrix , i.e., if A b and ' , then therefore, when inputting h ', then r -C =[A X B.}
the conversion relationships of 7 levels of the velocity differences, offset differences, and accelerations of the two vehicles in car-following state are expressed with a conditional statement, thereby establishing 49 fuzzy rules; when the velocity differences and offset differences of the vehicles are all at level N3, it indicates that the following car is in a very safe driving state, the velocity of the leading car is greater than the velocity of the following car, and the actual inter-vehicle spacing is greater than the desired inter-vehicle spacing; in order to maintain a car-following state with respect to the leading car, the following car driver needs to accelerate to reduce the distance from the leading car so as to achieve a desired distance; therefore, with constant reduction of the offset difference, the driver's acceleration decreases;
[0021] Step 3.5: Fuzzy inference: with the Mamdani model as the fuzzy inference model, by resolving the smaller one of Cartisan products of fuzzy sets A and B in the Mamdani model, it is derived that:
(xs Y) = fiA (x) A PB (Y) [0022] Step 3.6: Defuzzification.
[0023] Step 3.6.1: obtaining the initial solution a of the acceleration of the following car by the center-of-gravity method, i.e., the following car acceleration a; then, letting the taboo table H be empty, i.e., H 0 .
.. [0024] Step 3.6.2: if a termination condition is satisfied, jumping to step 3.6.4; otherwise, n selecting a candidate set Can _ N(a")satisfying the taboo requirement from the neighboring domain N(H , an") of the initial solution an" , and then jumping to step 3.6.3;
wherein the taboo requirement is: neighboring domain a"n , satisfying a"" e N(H,a"")and a"" H ;the termination condition is: when the local optimal solutions resulting from twice iterations do not change any more, or the difference between the evaluation functions of the twice optimal solutions is not large, stopping iteration.
[0025] Step 3.6.3: selecting a solution anew with the best evaluation value from the now candidate set, updating the taboo table H = H L..)Can _N(a) , and setting it as the currently ext optimal solution anom ¨ an ; then shifting to step 2.
[0026] Step 3.6.4: output the computation result anou , and stopping searching.
[0027] Preferably, wherein the fuzzy rule in Step 3.4 is constructed as follows: the value ranges of the velocity difference Avn , offset difference s", and acceleration a,,1 are divided evenly into 7 levels: P3, P2, Pl, ZE, N1, N2, N3, wherein P3 denotes the positive maximum value; for the velocity difference Avn , when the velocity difference Avn is P3, it indicates that the velocity difference between the leading car and the following car is very large; P2 and P3 denote that the values of the velocity difference Avn , the offset difference En, and the acceleration an+1 decrease gradually; ZE denotes 0, while Ni, N2, and N3 indicate that the velocity difference Avn , the offset difference En, and the acceleration a0+1 are negative values that decrease gradually; therefore, the fuzzy rule with the velocity difference Avn and the offset difference En between the two vehicles is established as such:
let the set Q=11\13, N2, Ni, ZE, PI, P2, P31, Avnnsr, > a+I Avn e Q EnEQ a,, e Q
n given that Avn and n each have 7 states, 49 acceleration states are obtained.
[0028] Preferably, wherein in Step 4, the feedback adjustment is controlled based on a feedback adjustment equation below:
xn'+,2(t) 2(t),L + k) k(o=a=max(xn+,=r +L+k+
2aõ, 2an+, where, a denotes the feedback adjustment equation, based on which the feedback adjustment may be controlled.
[0029] The method for predicting a car-following behavior under the Apollo platform according to present disclosure offers the following beneficial effects: (1) by testing and validating algorithm feasibility with the Apollo simulation platform and applying a learning mechanism, model accuracy is improved; (2) by adding reaction time varied dependent on the driver's different stages into the desired distance equation and introducing a parameter factor and tuning the driver's desired distance, the simulation result of the model is more approximate to the car-following behavior of a real driver, empowering the model to adaptively adjust the feedback control; (3) the defuzzification process of the car-following behavior model is improved using a heuristic search algorithm, thereby finding the optimal solution for safety- and-comfort-based acceleration.
BRIEF DESCRIPTION OF THE DRAWINGS
[0030] In order to make more likely to be clearly understood that the content of the present invention, the following specific embodiment according to the present invention with the accompanying drawings which, for the present invention as described in further detail, wherein :
[0031] Fig. 1 shows the method for predicting a car-following behavior under the Apollo platform flow diagram;
[0032] Fig. 2 shows a schematic diagram of a safe spacing for emergent braking;
[0033] Fig. 3 shows a flow diagram of a fuzzy inference system;
[0034] Fig. 4 shows a trigonometric membership function diagram;
[0035] Fig. 5 shows a fussy inference diagram of the Mamdani model; and [0036] Fig. 6 shows a Dreamview interface for environment simulation.
DETAILED DESCRIPTION OF EMBODIMENTS
[0037] Hereinafter, the present disclosure will be described in detail through preferred embodiments with reference to the accompanying drawings.
[0038] The present disclosure provides a method for predicting a car-following behavior under the Apollo platform, wherein a structured description of a scene is first formed based on understanding of the scene to give restrictions in motion geometry and physical dimensions of the vehicle; by inputting environment information such as traffic signs, index lines, and the relationships with surrounding vehicles into the Dreamview of the Apollo platform, a real road scene is constructed; then, the driving behaviors of the following-car driver are divided into 4 stages: perception stage, inference stage, decision stage, and execution stage. During the perception stage, the obtained ambient information is processed to compute the desired spacing of the following-car driver and the velocity difference at the current moment; during the inference stage, the following-car driver's action plan is inferred based on a fuzzy interference rule; during the decision stage, the leading-car driver's safety-and-comfort-based optimal acceleration is obtained by defuzzifying the following-car driver's action plan; and during the executing stage, the following-car driver implements vehicle velocity change from in-brain decision to hand manipulation. Finally, the predicting method is tested and validated using the Apollo simulation platform to ensure accuracy and utility of the predicting method.
[0039] The present disclose provides a method for predicting a car-following behavior under the Apollo platform, a flow diagram of which is shown in Fig. 1, specifically comprising:
[0040] Step 1: differentiating scene information in an autonomous driving process of a vehicle into static information and dynamic information, and importing the static information and the dynamic information into Dreamview of the Apollo platform to construct a road scene, specifically including: obtaining three-dimensional information of a traffic scene and motion information, wherein the three-dimensional information of the traffic scene refers to static information in the corresponding scene information and the motion information of the traffic scene refers to dynamic information in the scene information; preliminarily constructing a topological structure of the scene, wherein the topological information of the scene includes information such as the number of surrounding vehicles, the lanes occupied by surrounding vehicles, and the distance from a road edge; inputting such information into Dreamview via a corresponding interface of Apollo; configuring paths to specific modules based on the Table of Module Output Interface Standards ( Table 3)provided by the simulation environment, and performing, by respective modules in the Standard, environment construction with reference to the traffic flow and simulated environment information resulting from understanding of the scene, as shown in Table 1 below:
[0041] Table 1 Table of Module Output Interface Standards Provided by Simulation Output data the simulation will provide:
such as the position, orientation, linear¨ velocity /apollo/localizati position and Localization linearacceleration, angular velocity in on/pose orientation of _ _ the following pose car Output data such as the simulation will provide:
positions, id,position, theta,velocity, length,width, /apollo/percepti orientations, on/obstacles velocities, height,type, polygon_point in Perception shapes, and etc. PerceptionObstacle of respective obstacles /apollo/percepti Output traffic the simulation will provide:
on/traffic_light light signals color,id and tracking_time in TrafficLight, Output data such as the /apollo/canbus/c velocity and the simulation will provide:
CAN bus hassis drive mode of speed_mps the following car the simulation will provide:
Output the /apollo/routing_ Routing Response, including the planned Router navigation response result navigation route from the current position to the destination 100421 Table 3 Table of Decision Planning Module Interface Standards Module Topic Description Fields Output the The developer must provide:
planned (1) timestamp_sec in Header /apollo/planni trajectory of the Planning (2) v,a, relative_time in ng following car in TrajectoryPoint a future period (3) x,y,z,theta,kappa in PathPoint of time Output The developer optionally outputs:
/apollo/predict respective trajectories in PredictionObstacle, Prediction obstacles and which may be used for displaying ion their predicted predicted trajectories of trajectories respective obstacles Output The developer optionally outputs:
decisions with MainDecision and respect to /apollo/decisio ObjectDecisions, which may be Decision various used for displaying the main obstacles and decisions and the decisions with the main respect to respective obstacles decisions 100431 Step 2: capturing the following-car driver's behavior features in car-following state, computing a desired distance of the driver through a dynamics equation based on the driving data of the following-car driver, and fitting out a reaction time distribution function of the driver under the influence of velocity difference and relative distance using a polynomial regression method, specifically including steps of:
[0044] Step 2.1 computing the desired distance:
[0045] Let the maximum threshold spacing for the following-car driver to receive the stimulus from the leading car be Hmax , the desired following spacing of the following car within the spacing H max be kW. The desired spacing should guarantee that when the leading car abruptly stops with the maximum deceleration, the following-car driver's post-reaction braking can safely avoid collision; the safe spacing under the brake condition is schematically shown in Fig.2; the condition for collision avoidance is:
XI 2(t) ,/ 2 he(t) xn' +1(t) = r + L + k + n+1 A'n 2an+1 2an+1 where he (t) denotes safe spacing, n+1 (t) is the velocity of the following car at moment t, r denotes the driver's reaction time, L denotes the vehicle length, k denotes the allowed buffer spacing between the head of the following car and the tail of the leading car after stop, k is a constant, au and an+1 are maximum decelerations of the leading and following cars in the car-following behavior;
[0046] It is seen from the equation above that the safe spacing he(1) is dynamically correlated with the velocities of the leading and following cars; the driver desired spacing is:
i n2(t) , L +k) D(t)= max(x X+12 (t) X
,c+1(t)=r+L+k+ n 2aõ 1 2an+1 The larger one of the two desired spacings in the equation as derived using the max function is the current driver desired distance;
[0047] Step 2.2: computing the reaction time(fitting out a reaction time distribution function of the driver under the influence of velocity difference and relative distance using a polynomial regression method):
[0048] first, computing the reaction time r based on the time sequence data of acceleration variations of the leading car and the following car; owing to different reaction time for different individuals, the corresponding reaction time may be inferred;
[0049] then, each reaction time corresponds to a set of data (velocity difference, relative distance (Ay' Ax)); within the same reaction time, the leading car and the following car are paired; the velocity difference in the data set include: velocity change and relative distance within the reaction time of each vehicle, wherein the velocity change refers to the velocity difference of each vehicle, while the relative distance is obtained through a relative distance equation based on the velocity difference and the acceleration difference;
[0050] Finally, fitting out, by polynomial regression, the driver's reaction time distribution function under the influence of velocity difference and relative distance, wherein the reaction time distribution function has different function expressions for individual following-car drivers, e.g., exponential function, Sigmoid function.
[0051] As shown in Figs. 3, 4, and 5, Step 3 as following: first, performing fuzzification processing to the captured behavior feature data of the following-car driver,i.e. accurate data, using an improved fuzzy inference vehicle model; second, selecting a membership function ,i.e.data source,based on analysis of the following-car driver behavior features, and formulating a fuzzy rule library; next, performing fuzzy inference using the Mamdani model,i.e.Fuzzy reasoning Computing Center; finally, improving defuzzification using heuristic learning to enhance solution efficiency, specifically including steps of:
[0052] Step 3.1 defining input parameters and output parameters:
[0053] Let the computation equation of the velocity difference be:
Avõ = võ ¨v,1 the offset difference refers to the difference between the inter-vehicle distance AL n(t) at moment t and the following-car desired spacing D(t) , the equation being:
6 n(t) = ALn(t) D n(t) with the velocity difference and the offset difference as input parameters and the angular velocity of the following car as the output parameter, the velocity difference and offset difference of the following car in the data set is computed based on the road traffic driving data set, and the velocity difference, the offset difference and the range of acceleration are found by statistics;
[0054] Step 3.2 Fuzzification [0055] With the velocity difference and the offset difference as the input parameters in the fuzzy inference system and the acceleration of the following car as the output parameter, each input parameter and each output parameter have 7 levels, which are represented as N3, N2, Ni, ZE, PI, P2, and P3, respectively, as shown in Table2. For the velocity difference in the input parameters, the level P3 represents that the value of the velocity difference is positive and largest; levels P2 and PI represent that the value of the velocity difference is positive but gradually smaller; level ZE represents that the value of velocity difference is 0, while levels N1, N2, and N3 represent that the values of velocity difference are negative and gradually smaller; for the same reason, the seven levels of another input parameter offset value and output parameter acceleration are identical to the above.
[0056] Table 2 Fuzzy Rule Correspondence Table Avn en N3 N2 Ni ZE PI P2 P3 N3 N3 N3 N2 N2 Ni ZE ZE
N2 N3 N2 N2 Ni ZE ZE P1 Ni N2 N2 Ni Ni ZE P1 P1 PI N2 Ni ZE ZE PI P2 P2 P2 Ni ZE ZE P1 P2 P2 P3 [0057] Step 3.3 Selecting a Membership Function:
[0058] Let x*be an accurate value and 4* represent a converted fuzzy set; then the trigonometric membership function is:
= {(1 ¨ IX - X* ) x - xl s a cj A
O
where, as shown in Fig. 4, it is seen from the trigonometric membership function distribution diagram that, a > 0; when Ix ¨ x I > o-, the trigonometric membership function fuzzy set becomes a fuzzy single value; the larger a, the less the influence of variation of x* on i.e. When a is large enough, the method offers a strong enough anti-interference
configuring paths to specific modules based on the table of Module Output Interface Standards provided by the simulated environment, and performing, by respective modules in the standard, environment construction with reference to the traffic flow and the simulated environment resulting from understanding of the scene.
.. [0013] Preferably, wherein Step 2 specifically comprises:
[0014] Step 2.1 computing the desired distance: let the maximum threshold spacing for the following-car driver to receive the stimulus of the leading car be H., and the desired following spacing of the following car within the spacing Hmax be h,(t) ; the desired spacing should guarantee that when the leading car abruptly stops with the maximum deceleration, the following-car driver's post-reaction braking can safely avoid collision; the condition for preventing collision is:
he(t) +1(t) =r+L+k+x n+12 (t) x2 n (t) 2an+1 2an+1 where he (1) denotes safe spacing,x+1(t) is the velocity of the following car at moment t , denotes the driver' s reaction time, L denotes the vehicle length, k denotes the allowed buffer spacing between the head of the following car and the tail of the leading car (i.e., followed car) after stop, k is a constant, an and an+1 are maximum decelerations of the leading and following cars in the car-following behavior; it is seen from the equation above , that the safe spacing h(1) is dynamically correlated with the velocities of the leading and following cars; the driver desired spacing is:
/r n2 (t) ,L + k) D(t)= max(x1(t) = x12 (t) x + L + k + ________________________ n+
2an+1 2an+1 the larger one of the two distances as derived using the max function is the current driver desired distance;
100151 Step 2.2: computing the reaction time: first, computing the reaction time r based on the time sequence data of variations of the leading car acceleration and the following car acceleration; owing to different reaction time for different individuals, the corresponding reaction time may be inferred; then, each reaction time corresponds to a set of data (velocity difference, relative distance (Ay' Ax)); within the same reaction time, the leading car and the following car are paired; this velocity differences in the set of data include: velocity change and relative distance within the reaction time of each vehicle, wherein the velocity change refers to the velocity difference of each vehicle, while the relative distance is obtained through a relative distance equation based on the velocity difference and the acceleration difference;
finally, fitting out, by polynomial regression, the driver's reaction time distribution function under the influence of velocity difference and relative distance, wherein the reaction time distribution function has different function expressions for individual following-car drivers, e.g., exponential function, Sigmoid function.
100161 Preferably, wherein Step 3 specifically comprises:
[0017] Step 3.1 defining input parameters and output parameters: let the computation equation of the velocity difference be:
Avn = vn ¨ vn+, n the offset difference refers to the difference between the inter-vehicle distance AL(t)at moment t and the following-car desired spacing D(t) , the equation being as follows:
6 = ALn (i) ¨ D n (t) with the velocity difference and the offset difference as the input parameters and the angular velocity of the following car as the output parameter, the velocity difference and offset difference of the following car in the data set are computed based on the road traffic driving data set, and the velocity difference, offset difference and the range of acceleration are found by statistics;
[0018] Step 3.2 Fuzzification: with the velocity difference and the offset difference as the input parameters in the fuzzy inference system and the acceleration of the following car as the output parameter, each input parameter and each output parameter have 7 levels, which are represented as N3, N2, Ni, ZE, PI, P2, and P3, respectively; for the velocity difference in the input parameter, the level P3 represents that the value of the velocity difference is positive and largest; levels P2 and PI represent that the value of the velocity difference is positive but gradually smaller; level ZE represents that the value of velocity difference is 0, while levels Ni, N2, and N3 represent that the values of velocity difference are negative and gradually smaller; for the same reason, the seven levels of another input parameter offset value and output parameter acceleration are identical to the above.
[0019] Step 3.3 Selecting a Membership Function: let x*be an accurate value and A*
represent a converted fuzzy set; then the trigonometric membership function is:
Ix_ RA ) {(1 X* 1) 0a - x* s x - x >
where, it is seen from the trigonometric membership function distribution diagram that, a > ; when Ix 1> , the trigonometric membership function fuzzy set becomes a fuzzy single value; the larger a, the less the influence of variation of x* to PA*(x), i.e. when a is large enough, the method offers a strong enough anti-interference capability;
[0020] Step 3.4 establishing a fuzzy rule library:
the fuzzy relationship is ¨ ¨
wherein: A x: = 74(x) A lAY),in the equation, [A x B]T denotes a dimensional vector formed AxB ¨
by the matrix , i.e., if A b and ' , then therefore, when inputting h ', then r -C =[A X B.}
the conversion relationships of 7 levels of the velocity differences, offset differences, and accelerations of the two vehicles in car-following state are expressed with a conditional statement, thereby establishing 49 fuzzy rules; when the velocity differences and offset differences of the vehicles are all at level N3, it indicates that the following car is in a very safe driving state, the velocity of the leading car is greater than the velocity of the following car, and the actual inter-vehicle spacing is greater than the desired inter-vehicle spacing; in order to maintain a car-following state with respect to the leading car, the following car driver needs to accelerate to reduce the distance from the leading car so as to achieve a desired distance; therefore, with constant reduction of the offset difference, the driver's acceleration decreases;
[0021] Step 3.5: Fuzzy inference: with the Mamdani model as the fuzzy inference model, by resolving the smaller one of Cartisan products of fuzzy sets A and B in the Mamdani model, it is derived that:
(xs Y) = fiA (x) A PB (Y) [0022] Step 3.6: Defuzzification.
[0023] Step 3.6.1: obtaining the initial solution a of the acceleration of the following car by the center-of-gravity method, i.e., the following car acceleration a; then, letting the taboo table H be empty, i.e., H 0 .
.. [0024] Step 3.6.2: if a termination condition is satisfied, jumping to step 3.6.4; otherwise, n selecting a candidate set Can _ N(a")satisfying the taboo requirement from the neighboring domain N(H , an") of the initial solution an" , and then jumping to step 3.6.3;
wherein the taboo requirement is: neighboring domain a"n , satisfying a"" e N(H,a"")and a"" H ;the termination condition is: when the local optimal solutions resulting from twice iterations do not change any more, or the difference between the evaluation functions of the twice optimal solutions is not large, stopping iteration.
[0025] Step 3.6.3: selecting a solution anew with the best evaluation value from the now candidate set, updating the taboo table H = H L..)Can _N(a) , and setting it as the currently ext optimal solution anom ¨ an ; then shifting to step 2.
[0026] Step 3.6.4: output the computation result anou , and stopping searching.
[0027] Preferably, wherein the fuzzy rule in Step 3.4 is constructed as follows: the value ranges of the velocity difference Avn , offset difference s", and acceleration a,,1 are divided evenly into 7 levels: P3, P2, Pl, ZE, N1, N2, N3, wherein P3 denotes the positive maximum value; for the velocity difference Avn , when the velocity difference Avn is P3, it indicates that the velocity difference between the leading car and the following car is very large; P2 and P3 denote that the values of the velocity difference Avn , the offset difference En, and the acceleration an+1 decrease gradually; ZE denotes 0, while Ni, N2, and N3 indicate that the velocity difference Avn , the offset difference En, and the acceleration a0+1 are negative values that decrease gradually; therefore, the fuzzy rule with the velocity difference Avn and the offset difference En between the two vehicles is established as such:
let the set Q=11\13, N2, Ni, ZE, PI, P2, P31, Avnnsr, > a+I Avn e Q EnEQ a,, e Q
n given that Avn and n each have 7 states, 49 acceleration states are obtained.
[0028] Preferably, wherein in Step 4, the feedback adjustment is controlled based on a feedback adjustment equation below:
xn'+,2(t) 2(t),L + k) k(o=a=max(xn+,=r +L+k+
2aõ, 2an+, where, a denotes the feedback adjustment equation, based on which the feedback adjustment may be controlled.
[0029] The method for predicting a car-following behavior under the Apollo platform according to present disclosure offers the following beneficial effects: (1) by testing and validating algorithm feasibility with the Apollo simulation platform and applying a learning mechanism, model accuracy is improved; (2) by adding reaction time varied dependent on the driver's different stages into the desired distance equation and introducing a parameter factor and tuning the driver's desired distance, the simulation result of the model is more approximate to the car-following behavior of a real driver, empowering the model to adaptively adjust the feedback control; (3) the defuzzification process of the car-following behavior model is improved using a heuristic search algorithm, thereby finding the optimal solution for safety- and-comfort-based acceleration.
BRIEF DESCRIPTION OF THE DRAWINGS
[0030] In order to make more likely to be clearly understood that the content of the present invention, the following specific embodiment according to the present invention with the accompanying drawings which, for the present invention as described in further detail, wherein :
[0031] Fig. 1 shows the method for predicting a car-following behavior under the Apollo platform flow diagram;
[0032] Fig. 2 shows a schematic diagram of a safe spacing for emergent braking;
[0033] Fig. 3 shows a flow diagram of a fuzzy inference system;
[0034] Fig. 4 shows a trigonometric membership function diagram;
[0035] Fig. 5 shows a fussy inference diagram of the Mamdani model; and [0036] Fig. 6 shows a Dreamview interface for environment simulation.
DETAILED DESCRIPTION OF EMBODIMENTS
[0037] Hereinafter, the present disclosure will be described in detail through preferred embodiments with reference to the accompanying drawings.
[0038] The present disclosure provides a method for predicting a car-following behavior under the Apollo platform, wherein a structured description of a scene is first formed based on understanding of the scene to give restrictions in motion geometry and physical dimensions of the vehicle; by inputting environment information such as traffic signs, index lines, and the relationships with surrounding vehicles into the Dreamview of the Apollo platform, a real road scene is constructed; then, the driving behaviors of the following-car driver are divided into 4 stages: perception stage, inference stage, decision stage, and execution stage. During the perception stage, the obtained ambient information is processed to compute the desired spacing of the following-car driver and the velocity difference at the current moment; during the inference stage, the following-car driver's action plan is inferred based on a fuzzy interference rule; during the decision stage, the leading-car driver's safety-and-comfort-based optimal acceleration is obtained by defuzzifying the following-car driver's action plan; and during the executing stage, the following-car driver implements vehicle velocity change from in-brain decision to hand manipulation. Finally, the predicting method is tested and validated using the Apollo simulation platform to ensure accuracy and utility of the predicting method.
[0039] The present disclose provides a method for predicting a car-following behavior under the Apollo platform, a flow diagram of which is shown in Fig. 1, specifically comprising:
[0040] Step 1: differentiating scene information in an autonomous driving process of a vehicle into static information and dynamic information, and importing the static information and the dynamic information into Dreamview of the Apollo platform to construct a road scene, specifically including: obtaining three-dimensional information of a traffic scene and motion information, wherein the three-dimensional information of the traffic scene refers to static information in the corresponding scene information and the motion information of the traffic scene refers to dynamic information in the scene information; preliminarily constructing a topological structure of the scene, wherein the topological information of the scene includes information such as the number of surrounding vehicles, the lanes occupied by surrounding vehicles, and the distance from a road edge; inputting such information into Dreamview via a corresponding interface of Apollo; configuring paths to specific modules based on the Table of Module Output Interface Standards ( Table 3)provided by the simulation environment, and performing, by respective modules in the Standard, environment construction with reference to the traffic flow and simulated environment information resulting from understanding of the scene, as shown in Table 1 below:
[0041] Table 1 Table of Module Output Interface Standards Provided by Simulation Output data the simulation will provide:
such as the position, orientation, linear¨ velocity /apollo/localizati position and Localization linearacceleration, angular velocity in on/pose orientation of _ _ the following pose car Output data such as the simulation will provide:
positions, id,position, theta,velocity, length,width, /apollo/percepti orientations, on/obstacles velocities, height,type, polygon_point in Perception shapes, and etc. PerceptionObstacle of respective obstacles /apollo/percepti Output traffic the simulation will provide:
on/traffic_light light signals color,id and tracking_time in TrafficLight, Output data such as the /apollo/canbus/c velocity and the simulation will provide:
CAN bus hassis drive mode of speed_mps the following car the simulation will provide:
Output the /apollo/routing_ Routing Response, including the planned Router navigation response result navigation route from the current position to the destination 100421 Table 3 Table of Decision Planning Module Interface Standards Module Topic Description Fields Output the The developer must provide:
planned (1) timestamp_sec in Header /apollo/planni trajectory of the Planning (2) v,a, relative_time in ng following car in TrajectoryPoint a future period (3) x,y,z,theta,kappa in PathPoint of time Output The developer optionally outputs:
/apollo/predict respective trajectories in PredictionObstacle, Prediction obstacles and which may be used for displaying ion their predicted predicted trajectories of trajectories respective obstacles Output The developer optionally outputs:
decisions with MainDecision and respect to /apollo/decisio ObjectDecisions, which may be Decision various used for displaying the main obstacles and decisions and the decisions with the main respect to respective obstacles decisions 100431 Step 2: capturing the following-car driver's behavior features in car-following state, computing a desired distance of the driver through a dynamics equation based on the driving data of the following-car driver, and fitting out a reaction time distribution function of the driver under the influence of velocity difference and relative distance using a polynomial regression method, specifically including steps of:
[0044] Step 2.1 computing the desired distance:
[0045] Let the maximum threshold spacing for the following-car driver to receive the stimulus from the leading car be Hmax , the desired following spacing of the following car within the spacing H max be kW. The desired spacing should guarantee that when the leading car abruptly stops with the maximum deceleration, the following-car driver's post-reaction braking can safely avoid collision; the safe spacing under the brake condition is schematically shown in Fig.2; the condition for collision avoidance is:
XI 2(t) ,/ 2 he(t) xn' +1(t) = r + L + k + n+1 A'n 2an+1 2an+1 where he (t) denotes safe spacing, n+1 (t) is the velocity of the following car at moment t, r denotes the driver's reaction time, L denotes the vehicle length, k denotes the allowed buffer spacing between the head of the following car and the tail of the leading car after stop, k is a constant, au and an+1 are maximum decelerations of the leading and following cars in the car-following behavior;
[0046] It is seen from the equation above that the safe spacing he(1) is dynamically correlated with the velocities of the leading and following cars; the driver desired spacing is:
i n2(t) , L +k) D(t)= max(x X+12 (t) X
,c+1(t)=r+L+k+ n 2aõ 1 2an+1 The larger one of the two desired spacings in the equation as derived using the max function is the current driver desired distance;
[0047] Step 2.2: computing the reaction time(fitting out a reaction time distribution function of the driver under the influence of velocity difference and relative distance using a polynomial regression method):
[0048] first, computing the reaction time r based on the time sequence data of acceleration variations of the leading car and the following car; owing to different reaction time for different individuals, the corresponding reaction time may be inferred;
[0049] then, each reaction time corresponds to a set of data (velocity difference, relative distance (Ay' Ax)); within the same reaction time, the leading car and the following car are paired; the velocity difference in the data set include: velocity change and relative distance within the reaction time of each vehicle, wherein the velocity change refers to the velocity difference of each vehicle, while the relative distance is obtained through a relative distance equation based on the velocity difference and the acceleration difference;
[0050] Finally, fitting out, by polynomial regression, the driver's reaction time distribution function under the influence of velocity difference and relative distance, wherein the reaction time distribution function has different function expressions for individual following-car drivers, e.g., exponential function, Sigmoid function.
[0051] As shown in Figs. 3, 4, and 5, Step 3 as following: first, performing fuzzification processing to the captured behavior feature data of the following-car driver,i.e. accurate data, using an improved fuzzy inference vehicle model; second, selecting a membership function ,i.e.data source,based on analysis of the following-car driver behavior features, and formulating a fuzzy rule library; next, performing fuzzy inference using the Mamdani model,i.e.Fuzzy reasoning Computing Center; finally, improving defuzzification using heuristic learning to enhance solution efficiency, specifically including steps of:
[0052] Step 3.1 defining input parameters and output parameters:
[0053] Let the computation equation of the velocity difference be:
Avõ = võ ¨v,1 the offset difference refers to the difference between the inter-vehicle distance AL n(t) at moment t and the following-car desired spacing D(t) , the equation being:
6 n(t) = ALn(t) D n(t) with the velocity difference and the offset difference as input parameters and the angular velocity of the following car as the output parameter, the velocity difference and offset difference of the following car in the data set is computed based on the road traffic driving data set, and the velocity difference, the offset difference and the range of acceleration are found by statistics;
[0054] Step 3.2 Fuzzification [0055] With the velocity difference and the offset difference as the input parameters in the fuzzy inference system and the acceleration of the following car as the output parameter, each input parameter and each output parameter have 7 levels, which are represented as N3, N2, Ni, ZE, PI, P2, and P3, respectively, as shown in Table2. For the velocity difference in the input parameters, the level P3 represents that the value of the velocity difference is positive and largest; levels P2 and PI represent that the value of the velocity difference is positive but gradually smaller; level ZE represents that the value of velocity difference is 0, while levels N1, N2, and N3 represent that the values of velocity difference are negative and gradually smaller; for the same reason, the seven levels of another input parameter offset value and output parameter acceleration are identical to the above.
[0056] Table 2 Fuzzy Rule Correspondence Table Avn en N3 N2 Ni ZE PI P2 P3 N3 N3 N3 N2 N2 Ni ZE ZE
N2 N3 N2 N2 Ni ZE ZE P1 Ni N2 N2 Ni Ni ZE P1 P1 PI N2 Ni ZE ZE PI P2 P2 P2 Ni ZE ZE P1 P2 P2 P3 [0057] Step 3.3 Selecting a Membership Function:
[0058] Let x*be an accurate value and 4* represent a converted fuzzy set; then the trigonometric membership function is:
= {(1 ¨ IX - X* ) x - xl s a cj A
O
where, as shown in Fig. 4, it is seen from the trigonometric membership function distribution diagram that, a > 0; when Ix ¨ x I > o-, the trigonometric membership function fuzzy set becomes a fuzzy single value; the larger a, the less the influence of variation of x* on i.e. When a is large enough, the method offers a strong enough anti-interference
13 capability;
[0059] Step 3.4 establishing a fuzzy rule library:
[0060] The fuzzy relationship is wherein: x = A(x) A B(y) , .
in the equation, [A x BIT denotes a dimensional vector formed by the matrix "m t , .e., if A and B, then e'; therefore, when inputting A, :6, then e = [A x B
]r the conversion relationships of the 7 levels of velocity difference, offset difference, and acceleration of each of the two vehicles in the car-following state are expressed with a .. conditional statement, thereby establishing 49 fuzzy rules, as shown in Table 2; when the velocity differences and offset differences of the two vehicles are all at level N3, it indicates that the following car is in a very safe driving state, the velocity of the leading car is greater than the velocity of the following car, and the actual inter-vehicle spacing is greater than the desired inter-vehicle spacing; in order to maintain a state of following the leading car, the following car driver needs to accelerate to reduce the distance from the leading car so as to achieve a desired distance; therefore, with constant reduction of the offset difference, the driver's acceleration decreases;
[0061] Step 3.5: Fuzzy Inference:
[0062] With the Mamdani model as the fuzzy inference model, the inputs and outputs of the Mamdani model are all fuzzy amounts; de-fuzzification is needed after the fuzzy inference;
when the Max-Min operator is adopted, the fuzzy inference is shown in Fig. 5, wherein by resolving the smaller one of Cartisan products of fuzzy sets A and B in the Mamdani model, it is derived that:
I'RC'Y) = (x) A pB(y) [0063] Step 3.6: Defuzzification:
[0064] Step 3.6.1: obtaining the initial solution an" of the acceleration of the following car
[0059] Step 3.4 establishing a fuzzy rule library:
[0060] The fuzzy relationship is wherein: x = A(x) A B(y) , .
in the equation, [A x BIT denotes a dimensional vector formed by the matrix "m t , .e., if A and B, then e'; therefore, when inputting A, :6, then e = [A x B
]r the conversion relationships of the 7 levels of velocity difference, offset difference, and acceleration of each of the two vehicles in the car-following state are expressed with a .. conditional statement, thereby establishing 49 fuzzy rules, as shown in Table 2; when the velocity differences and offset differences of the two vehicles are all at level N3, it indicates that the following car is in a very safe driving state, the velocity of the leading car is greater than the velocity of the following car, and the actual inter-vehicle spacing is greater than the desired inter-vehicle spacing; in order to maintain a state of following the leading car, the following car driver needs to accelerate to reduce the distance from the leading car so as to achieve a desired distance; therefore, with constant reduction of the offset difference, the driver's acceleration decreases;
[0061] Step 3.5: Fuzzy Inference:
[0062] With the Mamdani model as the fuzzy inference model, the inputs and outputs of the Mamdani model are all fuzzy amounts; de-fuzzification is needed after the fuzzy inference;
when the Max-Min operator is adopted, the fuzzy inference is shown in Fig. 5, wherein by resolving the smaller one of Cartisan products of fuzzy sets A and B in the Mamdani model, it is derived that:
I'RC'Y) = (x) A pB(y) [0063] Step 3.6: Defuzzification:
[0064] Step 3.6.1: obtaining the initial solution an" of the acceleration of the following car
14 by the center-of-gravity method, i.e., the following car acceleration a; then, letting the taboo table H be empty, i.e., H = 0 ;
[0065] The center-of-gravity method is a method of taking, as the clear value, the element corresponding to the center-of-gravity of the area enclosed by the fuzzy set membership function curve and the basic variable axis, which is a commonly used defuzzification method.
[0066] The basic idea of the taboo search algorithm is to use a taboo table to record trapping into local optimal solutions through searching historical records, such that in the next search, repeated selection of searching local extremum points is forbidden using the information in the taboo table, so as to jump out of the local optimal points, which facilitates obtaining a global optimal solution.
[0067] Step 3.6.2: if a termination condition is satisfied, jumping to step 3.6.4; otherwise, now Can _N(a ) selecting a candidate set satisfying the taboo requirement from the N( H "}") now neighboring domain ,a of the initial solution a , and then jumping to step 3.6.3;
wherein the taboo requirement is that the neighboring domain acan should satisfy a' e N(H, )and a' 0 H .
, the termination condition is: when the local optimal solutions resulting from twice iterations do not change any more, or the difference between the evaluation functions of the two optimal solutions is not large, stopping iteration;
[0068] Step 3.6.3: selecting a solution amy' with the best evaluation value from the candidate set, updating the taboo table H = H uCan _N(an'') , and setting it as the currently optimal solution anon ¨ anext ; then shifting to step 2;
[0069] Step 3.6.4: outputting the computation result a , and stopping searching.
[0070] The evaluation value is obtained by dividing the time headway (TH) by the predicted acceleration. In equivalent driving conditions, the larger the time headway, the smaller the possibility of collision with the leading car. Further, in consideration of comfort, the absolute value of the acceleration generally shall not be too large; a too large acceleration would cause discomfort of passengers in the following car. Therefore, the acceleration is in reverse proportion to the time headway.
[0071] In step 3.4, the fuzzy rule is established in the following manner:
[0072] The value ranges of the velocity difference Avn , offset difference en , and acceleration an+, are divided evenly into 7 levels: P3, P2, Pl, ZE, Ni, N2, N3, wherein P3 denotes the positive maximum value; for the velocity difference Ayn , when the velocity difference Avn is P3, it indicates that the velocity difference between the leading car and the following car is very large; P2 and P3 denote that the values of the velocity difference Avn, the offset difference , and the acceleration an+, decrease gradually; ZE denotes 0, while Ni, N2, and N3 indicate that the velocity difference Ayn, the offset difference , and the acceleration an+, are negative values that decrease gradually; therefore, the fuzzy rule for the velocity difference Ayn and the offset difference en between the two vehicles is established as such:
Av n an+, n [0073] Let the set Q={N3, N2, NI, ZE, PI, P2, P3}, n AvEQ sEQ
aõ,, E Q; given that Avn and en each have 7 states, 49 acceleration states are obtained.
[0074] Step 4: predicting the following-car acceleration a using the improved vehicle inference model, i.e., the predicted acceleration value; substituting the computed predicted acceleration value a and the real acceleration a into the desired safe distance equation, computing the ratio a between the desired distance D' of the predicted acceleration value U and the desired distance D of the real acceleration a, and substituting a as the parameter factor into the desired safe distance equation to control feedback adjustment, wherein the feedback adjustment equation is specified below:
xn'+12(t) 2(t), L + k) he(t) = a=max(xn.,=r + L + k +
2a1 2an+1 where, a denotes the feedback adjustment equation, through which the feedback adjustment may be controlled.
[0075] During the construction process as disclosed herein, the dynamic information and the static information during vehicle driving are obtained through understanding of the scene; the desired distance and reaction time are obtained by capturing the driver's behavior features, and the defuzzification process of the following-car model is improved using the heuristic search algorithm, and based on the fuzzy inference model, computing the safety-and-comfort-based optimal solution of the following-car acceleration range.
Meanwhile, the model is tested and validated using the Apollo simulation platform to ensure accuracy and utility of the model.
[0076] The method for predicting a car-following behavior under the Apollo platform as disclosed is tested using the Apollo simulation platform. After the Apollo software environment is configured, the output interface of the Apollo platform is docked with the method. After successfully predicting information such as the following-car acceleration using the fuzzy inference behavior control policy model method, the method is docked with the decision planning module Planning of Apollo; finally, the Apollo software implements testing and validation of the fuzzy inference behavior control policy model;
during multiple times of simulation process, the parameters are constantly adjusted, and the algorithm is optimized over the Apollo visual platform, specifically:
[0077] Step a: deploying the environment (e.g., Docker environment), and pulling the container mirror of Apollo;
[0078] Step b: entering the Apollo container, and compiling the simulation environment (e.g., Dreamview simulation environment);
[0079] Step c: running the simulation environment after successful compilation;
[0080] Step d: testing and validating the efficacy of the model using the corresponding simulation environment; the testing and validating interface refers to the simulation environment interface, wherein the interface is shown in Fig. 6, specifically:
[0081] First, docking the traffic flow and environment information outputted by Apollo with the input of the model; then, converting the predicted acceleration value obtained using the model into the Planning input simulation platform, wherein the specific docking path is shown in Table 3; next, a vehicle under the simulation platform may adjust the velocity based on the input plan, and the final predicted acceleration value and the actual value are used to optimize the driver's desired car-following distance through feedback optimization.
[0082] Testing and validating the model in the Apollo simulation environment:
[0083] Docking the traffic flow and environment information outputted by Apollo with the input of the model; then, converting the predicted acceleration value obtained through the model into the Planning input simulation platform, wherein the specific docking path is shown in Fig. 6.
[0084] A vehicle under the simulation platform may adjust the velocity based on the input plan, and the final predicted acceleration value and the actual value are used to optimize the driver's desired car-following distance through feedback optimization.
.. [0085] The above embodiment the present invention are expressed only one of several embodiments, a more specific and detailed description thereof, but it is not is thus able to be construed as limiting the scope of the present invention patent. It should be noted, one of ordinary skill in the art, without departing from the concept of the present invention, numerous variations and modifications may be made, which are within the scope of the present invention. Thus, the scope of which shall be defined by the present invention as the standard.
[0065] The center-of-gravity method is a method of taking, as the clear value, the element corresponding to the center-of-gravity of the area enclosed by the fuzzy set membership function curve and the basic variable axis, which is a commonly used defuzzification method.
[0066] The basic idea of the taboo search algorithm is to use a taboo table to record trapping into local optimal solutions through searching historical records, such that in the next search, repeated selection of searching local extremum points is forbidden using the information in the taboo table, so as to jump out of the local optimal points, which facilitates obtaining a global optimal solution.
[0067] Step 3.6.2: if a termination condition is satisfied, jumping to step 3.6.4; otherwise, now Can _N(a ) selecting a candidate set satisfying the taboo requirement from the N( H "}") now neighboring domain ,a of the initial solution a , and then jumping to step 3.6.3;
wherein the taboo requirement is that the neighboring domain acan should satisfy a' e N(H, )and a' 0 H .
, the termination condition is: when the local optimal solutions resulting from twice iterations do not change any more, or the difference between the evaluation functions of the two optimal solutions is not large, stopping iteration;
[0068] Step 3.6.3: selecting a solution amy' with the best evaluation value from the candidate set, updating the taboo table H = H uCan _N(an'') , and setting it as the currently optimal solution anon ¨ anext ; then shifting to step 2;
[0069] Step 3.6.4: outputting the computation result a , and stopping searching.
[0070] The evaluation value is obtained by dividing the time headway (TH) by the predicted acceleration. In equivalent driving conditions, the larger the time headway, the smaller the possibility of collision with the leading car. Further, in consideration of comfort, the absolute value of the acceleration generally shall not be too large; a too large acceleration would cause discomfort of passengers in the following car. Therefore, the acceleration is in reverse proportion to the time headway.
[0071] In step 3.4, the fuzzy rule is established in the following manner:
[0072] The value ranges of the velocity difference Avn , offset difference en , and acceleration an+, are divided evenly into 7 levels: P3, P2, Pl, ZE, Ni, N2, N3, wherein P3 denotes the positive maximum value; for the velocity difference Ayn , when the velocity difference Avn is P3, it indicates that the velocity difference between the leading car and the following car is very large; P2 and P3 denote that the values of the velocity difference Avn, the offset difference , and the acceleration an+, decrease gradually; ZE denotes 0, while Ni, N2, and N3 indicate that the velocity difference Ayn, the offset difference , and the acceleration an+, are negative values that decrease gradually; therefore, the fuzzy rule for the velocity difference Ayn and the offset difference en between the two vehicles is established as such:
Av n an+, n [0073] Let the set Q={N3, N2, NI, ZE, PI, P2, P3}, n AvEQ sEQ
aõ,, E Q; given that Avn and en each have 7 states, 49 acceleration states are obtained.
[0074] Step 4: predicting the following-car acceleration a using the improved vehicle inference model, i.e., the predicted acceleration value; substituting the computed predicted acceleration value a and the real acceleration a into the desired safe distance equation, computing the ratio a between the desired distance D' of the predicted acceleration value U and the desired distance D of the real acceleration a, and substituting a as the parameter factor into the desired safe distance equation to control feedback adjustment, wherein the feedback adjustment equation is specified below:
xn'+12(t) 2(t), L + k) he(t) = a=max(xn.,=r + L + k +
2a1 2an+1 where, a denotes the feedback adjustment equation, through which the feedback adjustment may be controlled.
[0075] During the construction process as disclosed herein, the dynamic information and the static information during vehicle driving are obtained through understanding of the scene; the desired distance and reaction time are obtained by capturing the driver's behavior features, and the defuzzification process of the following-car model is improved using the heuristic search algorithm, and based on the fuzzy inference model, computing the safety-and-comfort-based optimal solution of the following-car acceleration range.
Meanwhile, the model is tested and validated using the Apollo simulation platform to ensure accuracy and utility of the model.
[0076] The method for predicting a car-following behavior under the Apollo platform as disclosed is tested using the Apollo simulation platform. After the Apollo software environment is configured, the output interface of the Apollo platform is docked with the method. After successfully predicting information such as the following-car acceleration using the fuzzy inference behavior control policy model method, the method is docked with the decision planning module Planning of Apollo; finally, the Apollo software implements testing and validation of the fuzzy inference behavior control policy model;
during multiple times of simulation process, the parameters are constantly adjusted, and the algorithm is optimized over the Apollo visual platform, specifically:
[0077] Step a: deploying the environment (e.g., Docker environment), and pulling the container mirror of Apollo;
[0078] Step b: entering the Apollo container, and compiling the simulation environment (e.g., Dreamview simulation environment);
[0079] Step c: running the simulation environment after successful compilation;
[0080] Step d: testing and validating the efficacy of the model using the corresponding simulation environment; the testing and validating interface refers to the simulation environment interface, wherein the interface is shown in Fig. 6, specifically:
[0081] First, docking the traffic flow and environment information outputted by Apollo with the input of the model; then, converting the predicted acceleration value obtained using the model into the Planning input simulation platform, wherein the specific docking path is shown in Table 3; next, a vehicle under the simulation platform may adjust the velocity based on the input plan, and the final predicted acceleration value and the actual value are used to optimize the driver's desired car-following distance through feedback optimization.
[0082] Testing and validating the model in the Apollo simulation environment:
[0083] Docking the traffic flow and environment information outputted by Apollo with the input of the model; then, converting the predicted acceleration value obtained through the model into the Planning input simulation platform, wherein the specific docking path is shown in Fig. 6.
[0084] A vehicle under the simulation platform may adjust the velocity based on the input plan, and the final predicted acceleration value and the actual value are used to optimize the driver's desired car-following distance through feedback optimization.
.. [0085] The above embodiment the present invention are expressed only one of several embodiments, a more specific and detailed description thereof, but it is not is thus able to be construed as limiting the scope of the present invention patent. It should be noted, one of ordinary skill in the art, without departing from the concept of the present invention, numerous variations and modifications may be made, which are within the scope of the present invention. Thus, the scope of which shall be defined by the present invention as the standard.
Claims (6)
1. A method for predicting a car-following behavior under the Apollo platform, specifically comprising:
Step 1: differentiating scene information in an autonomous driving process of a vehicle into static information and dynamic information, and importing the static information and the dynamic information into Dreamview of the Apollo platform to construct a road scene;
Step 2: capturing the following-car driver's behavior features in the car-following state, computing a desired distance through a dynamics equation based on the driving data of the following-car driver, and fitting out a reaction time distribution function of the driver under the influence of velocity difference and relative distance using a polynomial regression method;
Step 3: first, performing fuzzification processing to the captured behavior feature data of the following-car driver using an improved fuzzy inference vehicle model;
second, selecting a membership function based on analysis of the following-car driver behavior features, and formulating a fuzzy rule library; third, performing fuzzy inference using the Mamdani model; finally, improving defuzzification using heuristic learning to enhance solution efficiency; and Step 4: predicting the following-car acceleration a using the improved vehicle inference model, i.e., the predicted acceleration value; introducing the computed predicted acceleration value a and the real acceleration a into the desired safe distance equation, the ratio a between the desired distance D' of the predicted acceleration value a and the desired distance D of the real acceleration a , and substituting a as the parameter factor into the desired safe distance equation to control feedback adjustment.
Step 1: differentiating scene information in an autonomous driving process of a vehicle into static information and dynamic information, and importing the static information and the dynamic information into Dreamview of the Apollo platform to construct a road scene;
Step 2: capturing the following-car driver's behavior features in the car-following state, computing a desired distance through a dynamics equation based on the driving data of the following-car driver, and fitting out a reaction time distribution function of the driver under the influence of velocity difference and relative distance using a polynomial regression method;
Step 3: first, performing fuzzification processing to the captured behavior feature data of the following-car driver using an improved fuzzy inference vehicle model;
second, selecting a membership function based on analysis of the following-car driver behavior features, and formulating a fuzzy rule library; third, performing fuzzy inference using the Mamdani model; finally, improving defuzzification using heuristic learning to enhance solution efficiency; and Step 4: predicting the following-car acceleration a using the improved vehicle inference model, i.e., the predicted acceleration value; introducing the computed predicted acceleration value a and the real acceleration a into the desired safe distance equation, the ratio a between the desired distance D' of the predicted acceleration value a and the desired distance D of the real acceleration a , and substituting a as the parameter factor into the desired safe distance equation to control feedback adjustment.
2. The method for predicting a car-following behavior under the Apollo platform according to claim 1, wherein Step 1 specifically comprises:
obtaining three-dimensional information and motion information of a traffic scene, wherein the three-dimensional information of the traffic scene refers to static information in the corresponding scene information and the motion information of the traffic scene refers to dynamic information in the scene information; preliminarily constructing a topological structure of the scene, wherein the topological information of the scene includes information such as the number of surrounding vehicles, the lanes occupied by surrounding vehicles, and the distance from road edge; inputting such information into Dreamview via a corresponding interface of Apollo; configuring paths to specific modules based on the table of Module Output Interface Standards provided by the simulated environment, and performing, by respective modules in the standard, environment construction with reference to the traffic flow and the simulated environment resulting from understanding of the scene.
obtaining three-dimensional information and motion information of a traffic scene, wherein the three-dimensional information of the traffic scene refers to static information in the corresponding scene information and the motion information of the traffic scene refers to dynamic information in the scene information; preliminarily constructing a topological structure of the scene, wherein the topological information of the scene includes information such as the number of surrounding vehicles, the lanes occupied by surrounding vehicles, and the distance from road edge; inputting such information into Dreamview via a corresponding interface of Apollo; configuring paths to specific modules based on the table of Module Output Interface Standards provided by the simulated environment, and performing, by respective modules in the standard, environment construction with reference to the traffic flow and the simulated environment resulting from understanding of the scene.
3. The method for predicting a car-following behavior under the Apollo platform according to claim 1, wherein Step 2 specifically comprises:
Step 2.1 computing the desired distance:
let the maximum threshold spacing for the following-car driver to receive the stimulus of the leading car be H max , and the desired following spacing of the following car within the spacing H max be h e(t) , the desired spacing should guarantee that when the leading car abruptly stops with the maximum deceleration, the following-car driver's post-reaction braking can safely avoid collision; the condition for preventing collision is:
where h e(t) denotes safe spacing, x'I(t) is the velocity of the following car at moment t, r denotes the driver's reaction time, L denotes the vehicle length, k denotes the allowed buffer spacing between the head of the following car and the tail of the leading car (i.e., followed car) after stop, k is a constant, an and a n+1 are maximum decelerations of the leading and following cars in the car-following behavior;
it is seen from the equation above that the safe spacing e h (t) is dynamically correlated with the velocities of the leading and following cars; the driver desired spacing is:
the larger one of the two distances as derived using the max function is the current driver desired distance;
Step 2.2: computing the reaction time:
first, computing the reaction time r based on the time sequence data of variations of the leading car acceleration and the following car acceleration; owing to different reaction time for different individuals, the corresponding reaction time may be inferred;
then, each reaction time corresponds to a set of data (velocity difference, relative distance (.DELTA.v, .DELTA.x) ); within the same reaction time, the leading car and the following car are paired; this velocity differences in the set of data include: velocity change and relative distance within the reaction time of each vehicle, wherein the velocity change refers to the velocity difference of each vehicle, while the relative distance is obtained through a relative distance equation based on the velocity difference and the acceleration difference;
finally, fitting out, by polynomial regression, the driver's reaction time distribution function under the influence of velocity difference and relative distance, wherein the reaction time distribution function has different function expressions for individual following-car drivers, e.g., exponential function, Sigmoid function.
Step 2.1 computing the desired distance:
let the maximum threshold spacing for the following-car driver to receive the stimulus of the leading car be H max , and the desired following spacing of the following car within the spacing H max be h e(t) , the desired spacing should guarantee that when the leading car abruptly stops with the maximum deceleration, the following-car driver's post-reaction braking can safely avoid collision; the condition for preventing collision is:
where h e(t) denotes safe spacing, x'I(t) is the velocity of the following car at moment t, r denotes the driver's reaction time, L denotes the vehicle length, k denotes the allowed buffer spacing between the head of the following car and the tail of the leading car (i.e., followed car) after stop, k is a constant, an and a n+1 are maximum decelerations of the leading and following cars in the car-following behavior;
it is seen from the equation above that the safe spacing e h (t) is dynamically correlated with the velocities of the leading and following cars; the driver desired spacing is:
the larger one of the two distances as derived using the max function is the current driver desired distance;
Step 2.2: computing the reaction time:
first, computing the reaction time r based on the time sequence data of variations of the leading car acceleration and the following car acceleration; owing to different reaction time for different individuals, the corresponding reaction time may be inferred;
then, each reaction time corresponds to a set of data (velocity difference, relative distance (.DELTA.v, .DELTA.x) ); within the same reaction time, the leading car and the following car are paired; this velocity differences in the set of data include: velocity change and relative distance within the reaction time of each vehicle, wherein the velocity change refers to the velocity difference of each vehicle, while the relative distance is obtained through a relative distance equation based on the velocity difference and the acceleration difference;
finally, fitting out, by polynomial regression, the driver's reaction time distribution function under the influence of velocity difference and relative distance, wherein the reaction time distribution function has different function expressions for individual following-car drivers, e.g., exponential function, Sigmoid function.
4. The method for predicting a car-following behavior under the Apollo platform according to claim 3, wherein Step 3 specifically comprises:
Step 3.1 defining input parameters and output parameters:
let the computation equation of the velocity difference be:
.DELTA.v n = v n - v n+1 the offset difference refers to the difference between the inter-vehicle distance .DELTA.L n(t) at moment t and the following-car desired spacing D n(t), the equation being as follows:
.epsilon.n (t) = .DELTA.L n (t) - D n(t) with the velocity difference and the offset difference as the input parameters and the angular velocity of the following car as the output parameter, the velocity difference and offset difference of the following car in the data set are computed based on the road traffic driving data set, and the velocity difference, offset difference and the range of acceleration are found by statistics;
Step 3.2 Fuzzification with the velocity difference and the offset difference as the input parameters in the fuzzy inference system and the acceleration of the following car as the output parameter, each input parameter and each output parameter have 7 levels, which are represented as N3, N2, N1, ZE, P1, P2, and P3, respectively; for the velocity difference in the input parameter, the level P3 represents that the value of the velocity difference is positive and largest; levels P2 and P1 represent that the value of the velocity difference is positive but gradually smaller;
level ZE represents that the value of velocity difference is 0, while levels N1, N2, and N3 represent that the values of velocity difference are negative and gradually smaller; for the same reason, the seven levels of another input parameter offset value and output parameter acceleration are identical to the above.
Step 3.3 Selecting a Membership Function:
let x* be an accurate value and ~* represent a converted fuzzy set; then the trigonometric membership function is:
where, it is seen from the trigonometric membership function distribution diagram that, .sigma. > 0 ; when |x - x*| > .sigma., the trigonometric membership function fuzzy set becomes a fuzzy single value; the larger .sigma. , the less the influence of variation of x*
to µA*(x), i.e. when .sigma.
is large enough, the method offers a strong enough anti-interference capability;
Step 3.4 establishing a fuzzy rule library:
the fuzzy relationship is wherein: in the equation, denotes a dimensional vector formed by the matrix i.e., if ~ and ~ then ~; therefore, when inputting ~, ~, then the conversion relationships of 7 levels of the velocity differences, offset differences, and accelerations of the two vehicles in car-following state are expressed with a conditional statement, thereby establishing 49 fuzzy rules; when the velocity differences and offset differences of the vehicles are all at level N3, it indicates that the following car is in a very safe driving state, the velocity of the leading car is greater than the velocity of the following car, and the actual inter-vehicle spacing is greater than the desired inter-vehicle spacing; in order to maintain a car-following state with respect to the leading car, the following car driver needs to accelerate to reduce the distance from the leading car so as to achieve a desired distance; therefore, with constant reduction of the offset difference, the driver's acceleration decreases;
Step 3.5: Fuzzy inference:
with the Mamdani model as the fuzzy inference model, by resolving the smaller one of Cartisan products of fuzzy sets A and B in the Mamdani model, it is derived that:
R, (X y) = lj 4(X) A i (Y) Step 3.6: Defuzzification:
Step 3.6.1: obtaining the initial solution an" of the acceleration of the following car by the center-of-gravity method, i.e., the following car acceleration a ;
then, letting the taboo table H be empty, i.e., H ;
Step 3.6.2: if a termination condition is satisfied, jumping to step 3.6.4;
otherwise, ""
Can _ N(a ) selecting a candidate set satisfying the taboo requirement from the ""
neighboring domain of the initial solution awl' N(H,a ) , and then jumping to step 3.6.3;
can wherein the taboo requirement is: neighboring domain a satisfying a' e N(H ,a"")and iFt H ;
the termination condition is: when the local optimal solutions resulting from twice iterations do not change any more, or the difference between the evaluation functions of the twice optimal solutions is not large, stopping iteration;
ew Step 3.6.3: selecting a solution am with the best evaluation value from the candidate Can _ N(a"") ;
set, updating the taboo table H = H u and setting it as the currently optimal anow = anev solution then shifting to step 2;
Step 3.6.4: output the computation result , and stopping searching.
Step 3.1 defining input parameters and output parameters:
let the computation equation of the velocity difference be:
.DELTA.v n = v n - v n+1 the offset difference refers to the difference between the inter-vehicle distance .DELTA.L n(t) at moment t and the following-car desired spacing D n(t), the equation being as follows:
.epsilon.n (t) = .DELTA.L n (t) - D n(t) with the velocity difference and the offset difference as the input parameters and the angular velocity of the following car as the output parameter, the velocity difference and offset difference of the following car in the data set are computed based on the road traffic driving data set, and the velocity difference, offset difference and the range of acceleration are found by statistics;
Step 3.2 Fuzzification with the velocity difference and the offset difference as the input parameters in the fuzzy inference system and the acceleration of the following car as the output parameter, each input parameter and each output parameter have 7 levels, which are represented as N3, N2, N1, ZE, P1, P2, and P3, respectively; for the velocity difference in the input parameter, the level P3 represents that the value of the velocity difference is positive and largest; levels P2 and P1 represent that the value of the velocity difference is positive but gradually smaller;
level ZE represents that the value of velocity difference is 0, while levels N1, N2, and N3 represent that the values of velocity difference are negative and gradually smaller; for the same reason, the seven levels of another input parameter offset value and output parameter acceleration are identical to the above.
Step 3.3 Selecting a Membership Function:
let x* be an accurate value and ~* represent a converted fuzzy set; then the trigonometric membership function is:
where, it is seen from the trigonometric membership function distribution diagram that, .sigma. > 0 ; when |x - x*| > .sigma., the trigonometric membership function fuzzy set becomes a fuzzy single value; the larger .sigma. , the less the influence of variation of x*
to µA*(x), i.e. when .sigma.
is large enough, the method offers a strong enough anti-interference capability;
Step 3.4 establishing a fuzzy rule library:
the fuzzy relationship is wherein: in the equation, denotes a dimensional vector formed by the matrix i.e., if ~ and ~ then ~; therefore, when inputting ~, ~, then the conversion relationships of 7 levels of the velocity differences, offset differences, and accelerations of the two vehicles in car-following state are expressed with a conditional statement, thereby establishing 49 fuzzy rules; when the velocity differences and offset differences of the vehicles are all at level N3, it indicates that the following car is in a very safe driving state, the velocity of the leading car is greater than the velocity of the following car, and the actual inter-vehicle spacing is greater than the desired inter-vehicle spacing; in order to maintain a car-following state with respect to the leading car, the following car driver needs to accelerate to reduce the distance from the leading car so as to achieve a desired distance; therefore, with constant reduction of the offset difference, the driver's acceleration decreases;
Step 3.5: Fuzzy inference:
with the Mamdani model as the fuzzy inference model, by resolving the smaller one of Cartisan products of fuzzy sets A and B in the Mamdani model, it is derived that:
R, (X y) = lj 4(X) A i (Y) Step 3.6: Defuzzification:
Step 3.6.1: obtaining the initial solution an" of the acceleration of the following car by the center-of-gravity method, i.e., the following car acceleration a ;
then, letting the taboo table H be empty, i.e., H ;
Step 3.6.2: if a termination condition is satisfied, jumping to step 3.6.4;
otherwise, ""
Can _ N(a ) selecting a candidate set satisfying the taboo requirement from the ""
neighboring domain of the initial solution awl' N(H,a ) , and then jumping to step 3.6.3;
can wherein the taboo requirement is: neighboring domain a satisfying a' e N(H ,a"")and iFt H ;
the termination condition is: when the local optimal solutions resulting from twice iterations do not change any more, or the difference between the evaluation functions of the twice optimal solutions is not large, stopping iteration;
ew Step 3.6.3: selecting a solution am with the best evaluation value from the candidate Can _ N(a"") ;
set, updating the taboo table H = H u and setting it as the currently optimal anow = anev solution then shifting to step 2;
Step 3.6.4: output the computation result , and stopping searching.
5. The method for predicting a car-following behavior under the Apollo platform according to claim 4, wherein the fuzzy rule in Step 3.4 is constructed as follows:
the value ranges of the velocity difference Avn, offset difference en, and acceleration a, 1 1~ are divided evenly into 7 levels: P3, P2, P 1, ZE, N1, N2, N3, wherein P3 denotes the positive maximum value; for the velocity difference Avn when the velocity difference Avn is P3, it indicates that the velocity difference between the leading car and the following car is very large; P2 and P3 denote that the values of the velocity difference Ayn, the offset difference 611 , and the acceleration an~1 decrease gradually; ZE denotes 0, while N1, N2, and N3 indicate that the velocity difference Avn , the offset difference g" , and the acceleration 61'7+1 are negative values that decrease gradually; therefore, the fuzzy rule with the velocity difference Ayn and the offset difference 6" between the two vehicles is established as such:
let the set Q={N3, N2, N1, ZE, PI, P2, P3}, Alin n en ¨> an+1 Avn E Q, 6n 6 Q
' a 6 Q . g , given that Av. and n each have 7 states, 49 accelerati n+I on states are obtained.
the value ranges of the velocity difference Avn, offset difference en, and acceleration a, 1 1~ are divided evenly into 7 levels: P3, P2, P 1, ZE, N1, N2, N3, wherein P3 denotes the positive maximum value; for the velocity difference Avn when the velocity difference Avn is P3, it indicates that the velocity difference between the leading car and the following car is very large; P2 and P3 denote that the values of the velocity difference Ayn, the offset difference 611 , and the acceleration an~1 decrease gradually; ZE denotes 0, while N1, N2, and N3 indicate that the velocity difference Avn , the offset difference g" , and the acceleration 61'7+1 are negative values that decrease gradually; therefore, the fuzzy rule with the velocity difference Ayn and the offset difference 6" between the two vehicles is established as such:
let the set Q={N3, N2, N1, ZE, PI, P2, P3}, Alin n en ¨> an+1 Avn E Q, 6n 6 Q
' a 6 Q . g , given that Av. and n each have 7 states, 49 accelerati n+I on states are obtained.
6. The method for predicting a car-following behavior under the Apollo platform according to claim 4, wherein in Step 4, the feedback adjustment is controlled based on a feedback adjustment equation below:
where, a denotes the feedback adjustment equation, based on which the feedback adjustment may be controlled.
where, a denotes the feedback adjustment equation, based on which the feedback adjustment may be controlled.
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| CN110750877A (en) | 2020-02-04 |
| CN110750877B (en) | 2024-05-03 |
| CA3065617C (en) | 2022-07-19 |
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