RECONSTRUCTION METHOD FOR SECURE ENVIRONMENT ENVELOPE OF SMART VEHICLE BASED ON DRIVING BEHAVIOR OF VEHICLE IN FRONT

Abstract

A reconstruction method for a secure environment envelope of a smart vehicle based on the driving behavior of a vehicle in front, starting from the simulation of the behavior of a real driver pre-estimating the potential collision risk of the drive area in front, introducing a prediction regarding the driving behavior of the vehicle in front to the environment sensing link of the smart vehicle, reconstructing, on the basis of the prediction result regarding the driving behavior of the vehicle in front, a secure environment envelope of the smart vehicle. The method uses a signal as an observed value, such as the trajectory point sequence of the vehicle in front, the indicators of the vehicle in front, the smart vehicle speed, the relative longitudinal speed of the smart vehicle and the vehicle in front, etc., and predicts the driving behavior of the vehicle in front by means of a hidden markov model (HMM); the method corrects, on the basis of the prediction result about the driving behavior of the vehicle in front, the transverse spacing and the longitudinal spacing between the smart vehicle and the vehicle in front, realizes the reconstruction of a secure environment envelope of a smart vehicle, and further realizes the pre-estimation regarding the potential collision risk of the smart vehicle in the safe drive area, and improves the security of the smart vehicle.

Claims

1. A reconstruction method of intelligent vehicle safety environment envelope based on forward vehicle driving behavior, comprising forward vehicle driving behavior prediction model and intelligent vehicle safety environment envelope reconstruction algorithm, forward vehicle driving behavior prediction model is responsible for the prediction of forward vehicle driving behavior, and intelligent vehicle safety environment envelope reconstruction algorithm is responsible for the reconstruction of safety environment envelope based on the prediction results.

2. According to the reconstruction method of intelligent vehicle safety environment envelope based on forward vehicle driving behavior described in claim 1, the invention is characterized in that the forward vehicle driving behavior prediction model described in the invention is a HMM prediction model λ=(N, M, π, A, B), which including. the driving behavior states of forward vehicle is S: S=(S.sub.1, S.sub.2, . . . S.sub.N), the state at a given time t is q.sub.t, and q.sub.t∈S; status number of the invention N=4 where S.sub.1 is represent for uniform driving behavior; S.sub.2 is the emergency braking driving behavior; S.sub.3 is the driving behavior on steering left; S.sub.4 is the driving behavior on steering right; the observation sequence is V: V=(v.sub.1, v.sub.2, . . . v.sub.M); observing events is O.sub.t at a given time t, the observations number of the invention: M=7 where v.sub.1 is the observation value of polar diameter changing of adjacent trajectory point sequences of forward vehicle; v.sub.2 is the observation value of the polar angle changing of the sequence of adjacent trajectory point sequences of forward vehicle; v.sub.3 is intelligent vehicle speed: v.sub.4 is the longitudinal relative speed of the intelligent vehicle and the forward vehicle; v.sub.5 is the turn signal to the left of the forward vehicle; v.sub.6 is the turn signal to the right of the forward vehicle; v.sub.7 is the brake signal of the forward vehicle; π is the probability vector of initial state of forward vehicle driving behavior; π=(π.sub.1, π.sub.2, . . . π.sub.N), where π.sub.i=P(q.sub.1=S.sub.i); A is the state transition matrix, that is, state transition matrix of forward vehicle driving behavior; A={a.sub.ij}.sub.N×N, where a.sub.ij=P(q.sub.t+1=S.sub.j|q.sub.tS.sub.i), 1≤i, j≤N; B is the probability distribution matrix of observed events; namely, probability of generating observation v.sub.k at state S.sub.j: B={b.sub.jk}.sub.N×M, where b.sub.jk=P[O.sub.t=v.sub.k|q.sub.t=S.sub.j], 1≤j≤N, 1≤k≤M.

3. According to the reconstruction method of intelligent vehicle safety environment envelope based on forward vehicle driving behavior described in claim 2, the invention is characterized in that forward vehicle driving behavior prediction model is implemented as follows: establishment of forward vehicle driving behavior prediction model: the driving behavior prediction model established for forward vehicle including: uniform driving behavior prediction model (US_HMM), emergency brake driving behavior prediction model (EB_HMM), left-turn driving behavior prediction model (LT_HMM) and Right turn driving behavior prediction model (RT_HMM); off-line training of four forward vehicle driving behavior prediction models; prediction of forward vehicle driving behavior based on four forward vehicle driving behavior prediction models.

4. According to the reconstruction method of intelligent vehicle safety environment envelope based on forward vehicle driving behavior described in claim 3, the invention is characterized in that the off-line training process of the forward vehicle driving behavior prediction model includes: (1) model parameter initialization, mainly initialize parameters of HMM model, such as π, A, and B; (2) the forward-backward algorithm is selected to calculate the forward frequency α.sub.t(i) and backward probability β.sub.t(j) with the current sample; (3) baum-Welch algorithm was applied to calculate estimated value {circumflex over (λ)}=(90 , A, B) of the current new model; (4) calculate the likelihood probability P=(O/{circumflex over (λ)}); (5) P=(O/{circumflex over (λ)}) is increasing continually, the next time, the new estimated value calculated by step (3) will be re-estimated for the sample, and returned to step (2), it is iterated step by step until P=(O/{circumflex over (λ)}) is no longer significantly increased i.e., converges, at this time, the model {circumflex over (λ)} is the model in requirement.

5. According to the reconstruction method of intelligent vehicle safety environment envelope based on forward vehicle driving behavior described in claim 3, the invention is characterized in that Forward vehicle driving behavior prediction process includes: the original parameters are extracted to form a set of observation sequences O; the forward-backward algorithm is applied to calculate the probability P(O/λ) of each model generating the current observation sequence, and the driving behavior corresponding to model with the largest probability is the predicted result of driving behavior of forward vehicle.

6. According to the reconstruction method of intelligent vehicle safety environment envelope based on forward vehicle driving behavior described in claim 1, the invention is characterized in that the intelligent vehicle safety environment envelope reconstruction algorithm is as follows: according to the sensor and dynamic model, the relative position information of the intelligent vehicle and the forward vehicle is established, as shown below: $\left[\begin{array}{c}\Delta p_{x,j}\left(t\right)\\ \Delta p_{y,j}\left(t\right)\end{array}\right]=\left[\begin{array}{cc}\cos \left(-e_{\psi }\left(t\right)\right)& -\sin \left(-e_{\psi }\left(t\right)\right)\\ \sin \left(-e_{\psi }\left(t\right)\right)& \cos \left(-e_{\psi }\left(t\right)\right)\end{array}\right]\left[\begin{array}{c}{p}_{x,j}\left(t\right)-p_{x,\mathrm{sub}}\left(t\right)\\ p_{y,j}\left(t\right)-p_{y,\mathrm{sub}}\left(t\right)\end{array}\right]$ where p.sub.x,j(t) is the longitudinal coordinates of the jth forward vehicle; p.sub.x,sub(t) is the longitudinal coordinates of the intelligent vehicle: e.sub.Ψ(t) is the position error between vehicle and road surface; p.sub.y,j(t) is the lateral coordinates of the jth forward vehicle; p.sub.y,sub(t) is the lateral coordinates of the intelligent vehicle: Δp.sub.x,j(t) is the longitudinal relative distance between the smart vehicle and the jth forward vehicle; Δp.sub.y,j(t) is the lateral relative distance between the smart vehicle and the jth forward vehicle: the distance between intelligent vehicle and forward vehicle can be obtained by transformation, as shown below: $\left[\begin{array}{c}{C}_{x,j}\left(t\right)\\ C_{y,j}\left(t\right)\end{array}\right]=\left[\begin{array}{c}\Delta p_{x,j}\left(t\right)\\ \Delta p_{y,j}\left(t\right)\end{array}\right]-\left[\begin{array}{c}\mathrm{sgn}\left(\Delta p_{x,j}\left(t\right)\right).Math.L_v\\ \mathrm{sgn}\left(\Delta p_{y,j}\left(t\right)\right)W_v\end{array}\right]$ Where: L.sub.v is the length of the forward vehicle; W.sub.v is the width of the forward vehicle; C.sub.x,j(t) is the longitudinal distance between intelligent vehicle and forward vehicle; C.sub.y,j(t) is the lateral distance between intelligent vehicle and forward vehicle; based on the predicted results the longitudinal and lateral distance between the intelligent vehicle and the forward vehicle are modified to realize the reconstruction for safety environment envelope of intelligent vehicle, as shown below: $\left[\begin{array}{c}{C}_{x,j}^{\prime }\left(t\right)\\ C_{y,j}^{\prime }\left(t\right)\end{array}\right]=\left[\begin{array}{cc}{\omega }_x& 0\\ 0& {\omega }_y\end{array}\right].Math.\left[\begin{array}{c}{C}_{x,j}\left(t\right)\\ C_{y,j}\left(t\right)\end{array}\right]$ where parameter ω.sub.x is the longitudinal correction factor, and represents the variations in scale of longitudinal distance; parameter ω.sub.y is the lateral correction factor and represents the variations in scale of lateral distance; the probability value of the result predicted by HMM model is applied to determine the value of ω.sub.x and ω.sub.y.

7. According to the reconstruction method of intelligent vehicle safety environment envelope based on forward vehicle driving behavior described in claim 6, the invention is characterized in that the value range of ω.sub.x is between 0 and 1, the value range of ω.sub.y is between 0 and 1 when the lateral spacing gets smaller, while the lateral distance gets larger, the value range of ω.sub.y is greater than 1.

Description

DESCRIPTION OF THE DRAWINGS

[0021] FIG. 1 is the system block diagram of the invention.

[0022] FIG. 2 is the off-line training flow chart of the forward vehicle driving behavior prediction model.

[0023] FIG. 3 is the predicting flow chart of driving behavior of the forward vehicle.

[0024] FIG. 4 is a schematic diagram of the variation of lateral spacing when the forward vehicle has left-turn driving behavior.

[0025] Where, figure (a) shows the current lateral distance between the intelligent vehicle and the forward vehicle, and figure (b) shows the lateral distance between the intelligent vehicle and the forward vehicle when the forward vehicle has left-turn driving behavior.

[0026] FIG. 5 is a schematic diagram of longitudinal spacing varying when the forward vehicle has emergency braking driving behavior.

[0027] Where, figure (a) shows the current longitudinal distance between intelligent the vehicle and the forward vehicle, figure (b) shows the longitudinal distance between the intelligent vehicle and the forward vehicle when the forward vehicle has emergency braking driving behavior.

[0028] Parameters in the figures: {circle around (1)}: intelligent vehicle; {circle around (2)}: the forward vehicle; C.sub.x,j(t): the longitudinal distance between intelligent vehicle and forward vehicle; C′.sub.x,j(t): the reconstructed longitudinal distance between intelligent vehicle and forward vehicle considering driving behavior of forward vehicle; C.sub.y,j(t): the lateral distance between intelligent vehicle and forward vehicle: C′.sub.y,j(t): the reconstructed lateral distance between intelligent vehicle and forward vehicle considering driving behavior of forward vehicle.

SPECIFIC IMPLEMENTATIONS

[0029] Following is a clear and complete description of the concept and specific working process of the invention with reference to the drawings and examples. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, other embodiments acquired by skilled personnel in the field without any creative effort belong to the scope of protection of the present invention.

[0030] As shown in FIG. 1, a reconstruction method of intelligent vehicle safely environment envelope based on forward vehicle driving behavior is composed of forward vehicle driving behavior prediction model and intelligent vehicle safety environment envelope reconstruction algorithm. 1. The realization process of the forward vehicle driving behavior prediction model is as follows:

[0031] Establishment of forward vehicle driving behavior prediction model: The driving behavior prediction model established for forward vehicle including: Uniform driving behavior prediction model (US_HMM), Emergency brake driving behavior prediction model (EB_HMM), Left-turn driving behavior prediction model (LT_HMM) and Right turn driving behavior prediction model (RT_HMM).

[0032] Off-line training of forward vehicle driving behavior prediction model: As shown in FIG. 2, the off-line training flow chart of the invention includes the following steps:

[0033] (1) Model parameter initialization, mainly initialize parameters of HMM model, such as π, A, and B.

[0034] (2) The forward-backward algorithm is selected to calculate the forward frequency α.sub.t(i) and backward probability β.sub.t(j) with the current sample.

[0035] (3) Baum-Welch algorithm was applied to calculate the estimated value {circumflex over (λ)}=(π, A, B) of the current new model.

[0036] (4) Calculate the likelihood probability P=(O/{circumflex over (λ)}).

[0037] (5) If P=(O/{circumflex over (λ)}) is increasing continually the next time, the new estimated value calculated by step (3) will be re-estimated for the sample, and returned to step (2), it is iterated step by step until P=(O/{circumflex over (λ)})is no longer significantly increased, i.e., converges. At this time, the model {circumflex over (λ)} is the model in requirement.

[0038] The training process of the present invention is illustrated by an example of a left-turn driving behavior prediction model (LT_HMM).

(1) Selection of Training Samples

[0039] For left-turn driving behavior prediction model, the invention will consider observed sequence including seven parameters: the observed value of the pole diameter changes of the forward vehicle adjacent trajectory points sequence, the pole angle changes of the forward vehicle adjacent trajectory points sequence, intelligent vehicle speed, longitudinal relative velocity between intelligent vehicle and the forward vehicle, left turn signal, right turn signal, and brake lamp of the forward vehicle respectively. The observation sequence is described as a vector, as shown in equation (4).

O(f)={v.sub.1 v.sub.2 v.sub.3 v.sub.4 v.sub.5 v.sub.6 v.sub.7}  (4)

[0040] Where v.sub.1 is the observed value of the pole diameter changes of the forward vehicle adjacent trajectory points sequence, v.sub.2 is the observed value of pole angle changes of the forward vehicle adjacent trajectory points sequence; v.sub.3 is the observed value of intelligent vehicle speed; v.sub.4 is the observed valve of longitudinal relative velocity between intelligent vehicle and the forward vehicle; v.sub.5, v.sub.6, v.sub.7 is left turn signal, right turn signal, and brake lamp of the forward vehicle respectively. Note: The number of samples is 100.

(2) Model Parameter Initialization

[0041] The invention adopts the mean value method to obtain initial value of π, and A:

[00004]$\pi =\left[0.250.250.250.25\right],A=\left[\begin{array}{cccc}0.25& 0.25& 0.25& 0.25\\ 0.25& 0.25& 0.25& 0.25\\ 0.25& 0.25& 0.25& 0.25\\ 0.25& 0.25& 0.25& 0.25\end{array}\right].$

[0042] The invention determines the initial probability distribution of the output probability matrix B based an the prior characteristics of different trajectory patterns:

[00005]$B=\left[\begin{array}{ccccccc}0.2& 0.2& 0.1& 0.2& 0.1& 0.1& 0.1\\ 0.3& 0.08& 0.08& 0.08& 0.08& 0.08& 0.3\\ 0.08& 0.3& 0.08& 0.08& 0.3& 0.08& 0.08\\ 0.08& 0.3& 0.08& 0.08& 0.08& 0.3& 0.08\end{array}\right].$

(3) Training Left-Turn Driving Behavior Prediction Model

[0043] According, to the off-line training process shown in FIG. 2 the left-turn driving behavior training samples are fed into the initial left-turn driving behavior prediction model for training, and finally the left-turn driving behavior prediction model is obtained.

[00006]$\hat{\pi }=\left[0.02460.03240.12570.8173\right]\text{;}$$\hat{A}=\left[\begin{array}{cccc}0.1132& 0.1271& 0.2119& 0.5478\\ 0.0243& 0.3471& 0.2694& 0.3592\\ 0.1432& 0.0221& 0.5034& 0.3213\\ 0.4318& 0.1349& 0.2392& 0.1941\end{array}\right]\text{;}$$\hat{B}=\left[\begin{array}{ccccccc}0.1912& 0.2136& 0.0981& 0.952& 0.1104& 0.0896& 0.1019\\ 0.2851& 0.0745& 0.0847& 0.0832& 0.0791& 0.0785& 0.3149\\ 0.0785& 0.2832& 0.0788& 0.0847& 0.3168& 0.0791& 0.0789\\ 0.0791& 0.2841& 0.0831& 0.0789& 0.0806& 0.3159& 0.0783\end{array}\right]$

2. Prediction Process of Driving Behavior of Forward Vehicle

[0044] The prediction process is shown in FIG. 3. The original parameters are extracted to form a set of observation sequences O. The forward-backward algorithm is applied to calculate the probability P(O/λ) of each model generating the current observation sequence, and the driving behavior corresponding to model with the largest probability is the predicted result of driving behavior of forward vehicle.

3. Reconstruction of Safety Environment Envelope Based on Forward Vehicle Driving Behavior Prediction

[0045] The prediction result is considered on left-turning driving behavior of forward vehicle as an example to illustrate the lateral safe distance reconstruction method of the invention:

[0046] As shown in FIG. 4, when considering only the current position of forward vehicle {circle around (2)}, the lateral distance C.sub.y,j(t) between intelligent vehicle {circle around (1)} and or and vehicle {circle around (2)} is shown as in FIG. 4(a). When considering that forward vehicle {circle around (2)} has left-turn driving behavior, the lateral distance C′.sub.y,j(t) between intelligent vehicle {circle around (1)} and forward vehicle {circle around (2)} is show as FIG. 4(b). Comparing FIG. 4(a) and FIG. 4(b), we can see that the lateral spacing between the intelligent vehicle {circle around (1)} and the forward vehicle {circle around (2)} gets smaller. Based on the prediction result, lateral safety distance is reconstructed to achieve new lateral secure model C′.sub.y,j(t)=ω.sub.yC.sub.y,j(t) , where ω.sub.y is lateral correction factor, represents the variations in scale of lateral distance, and its value depend on the predicted maximum likelihood probability of the left-turning driving behavior of the forward vehicle driving behavior prediction model. It can be seen that when considering the left-turn driving behavior of vehicles in front, intelligent vehicles predict the left-turn driving behavior of forward vehicle, and reduce the risk of lateral collision by reconstructing the lateral safe distance.

[0047] The prediction result is considered on emergency braking driving behavior of forward vehicle as an example to illustrate the longitudinal safe distance reconstruction method of the invention:

[0048] As shown in FIG. 5, when considering only the current position of forward vehicle {circle around (2)}, the longitudinal distance C.sub.x,j(t) between intelligent vehicle {circle around (1)} and forward vehicle {circle around (2)} is shown as in FIG. 5(a). When considering that forward vehicle {circle around (2)} has emergency braking driving behavior, the longitudinal distance C′.sub.x,j(t) between intelligent vehicle {circle around (1)} and forward vehicle {circle around (2)} is shown as FIG. 5(b). Comparing FIG. 5(a) and FIG. 5(b), we can see that the longitudinal spacing between the intelligent vehicle {circle around (1)} and the forward vehicle {circle around (2)} gets smaller. Based on the prediction result, longitudinal safe distance is reconstructed to achieve new longitudinal safe model C′.sub.x,j(t)=ω.sub.xC.sub.x,j(t), where ω.sub.x is longitudinal correction factor, represents the variations in scale of longitudinal distance, and its value depend on the predicted maximum likelihood probability of the emergency braking driving behavior of the forward vehicle driving behavior prediction model. It can be seen that when considering the emergency braking driving behavior of forward vehicle, intelligent vehicle predict the emergency braking driving behavior of forward vehicle, and reduce the risk of longitudinal collision by, reconstructing the longitudinal safe distance.

[0049] The series of detailed explanations listed above are only specific explanations of the feasible embodiments of the invention, and they are not intended to limit the scope of protection of the invention. Any equivalent implementation or modification without departing from the spirit of the present invention shall be included in the scope of protection of the present invention.