CN105691388A

CN105691388A - Vehicle collision avoidance system and track planning method thereof

Info

Publication number: CN105691388A
Application number: CN201610023691.6A
Authority: CN
Inventors: 赵万忠; 徐志江; 王春燕; 崔滔文
Original assignee: Nanjing University of Aeronautics and Astronautics
Current assignee: Nanjing University of Aeronautics and Astronautics
Priority date: 2016-01-14
Filing date: 2016-01-14
Publication date: 2016-06-22
Anticipated expiration: 2036-01-14
Also published as: CN105691388B

Abstract

The invention discloses a vehicle collision avoidance system and a track planning method thereof. The system comprises a forward looking radar, a camera, a vehicle speed sensor, a yaw angular velocity sensor, a sideslip angle sensor, a signal processing module, an electronic control unit (ECU), a throttle controller, a steering controller and a braking controller. When a vehicle is driven, signals which are transmitted by all sensors through the signal processing module are collected in real time by the electronic control unit, the road condition and the vehicle condition of the vehicle at the moment are judged in real time, if hazardous conditions possibly occur, a continuous collision avoidance executable track is generated by the ECU according to a track planning program preset in the ECU, and the related signals are output to the throttle controller, the steering controller and the braking controller for corresponding operations, so that the hazardous conditions are avoided. According to the vehicle collision avoidance system, a driver can be assisted to operate the vehicle in case of an emergency, so that the active driving safety performance can be increased.

Description

A kind of Automotive active anti-collision system and method for planning track thereof

Technical field

The present invention relates to automobile assistant driving field, particularly relate to a kind of Automotive active anti-collision system and method for planning track thereof。

Background technology

Along with intelligent transportation rise in the world, automobile assistant driving technology is of increased attention, and the main purpose of its research is in that to reduce the vehicle accident incidence rate being on the rise, and improves existing road traffic efficiency。Research institutions numerous in the world, its R&D process is just put into substantial amounts of human and material resources, financial resources to carry out the research and development of related key technical by industrial design unit。

Active collision avoidance system is as an important research content of automobile assistant driving technology, the main purpose of its research is to improve the security performance of vehicle drive, it mainly utilizes modern information technologies, sensing technology extends the perception of human pilot, by external information (such as speed, obstacle distance, speed, direction etc.) pass to comprehensive utilization vehicle condition and traffic information while human pilot, judge the safe coefficient of automobile current operating conditions, in case of emergency can take measures automatically to control automobile, automobile is averted danger on one's own initiative, ensure the extent of injury of the reduction accident of vehicle safety travel or maximum possible。Automobile has only possessed such active safety performance, is only possible to and fundamentally reduces vehicle accident, improves traffic safety。

Trajectory planning techniques is a key technology in Active collision avoidance system, want to realize the Based Intelligent Control to vehicle, its precondition is to generate feasible reference locus, and the parameter of track is supplied to tracking control unit, so that controller can control automobile and travel according to the track planned, therefore, the nothing that how in case of emergency planning one is feasible is touched track and is particularly important。

Summary of the invention

The technical problem to be solved is for defect involved in background technology, a kind of Automotive active anti-collision system and method for planning track thereof are provided, solve the trajectory planning problem of in case of emergency Active collision avoidance system, by mode effective avoiding obstacles while ensureing vehicle handling stability that software and hardware combines, the generation avoided traffic accident, it is achieved that the active safety function of automobile。

The present invention solves above-mentioned technical problem by the following technical solutions:

A kind of Automotive active anti-collision system, comprises forward-looking radar, photographic head, signal processing module, vehicle speed sensor, yaw-rate sensor, side slip angle sensor, steering wheel angle sensor, Electronic Control power ECU, throttle control, steering controller and brake monitor；

Described forward-looking radar, photographic head are connected with described Electronic Control power ECU by signal processing module；Described Electronic Control power ECU respectively with, vehicle speed sensor, yaw-rate sensor, side slip angle sensor, steering wheel angle sensor, throttle control, steering controller, brake monitor be connected；

Described forward-looking radar and photographic head are installed in vehicle front, for detecting the road conditions of vehicle front, and after signal processing module processes, measured signal are passed to described electronic control unit ECU；

Described vehicle speed sensor, yaw-rate sensor, side slip angle sensor, steering wheel angle sensor are respectively used to the sensing speed of automobile, yaw velocity, side slip angle and front wheel steering angle, and the signal collected is sent to electronic control unit ECU after treatment；

Described electronic control unit ECU, for exporting corresponding signal to throttle control, steering controller, brake monitor according to the signal received, carries out acceleration accordingly, deceleration, brake operating, to ensure traffic safety。

The invention also discloses a kind of method for planning track based on above Automotive active anti-collision system, comprise the steps of

Step 1), the distance of vehicle front barrier, speed, acceleration and width is obtained with photographic head by forward-looking radar, and by preceding object thing and distance between automobile and safe distance threshold comparison set in advance, if less than default safe distance threshold value, then perform step 2)；

Step 2), obtain the speed of automobile, yaw velocity, side slip angle and front wheel steering angle by vehicle speed sensor, yaw-rate sensor, side slip angle sensor, steering wheel angle sensor；

Step 3), set up automobile three-degree-of-freedom motion model according to the longitudinal coordinate at the spacing between the yaw angle of automobile, front wheel steering angle, longitudinal velocity, front axle and rear axle and rear axle midpoint and lateral coordinate；

Step 4), with seven order polynomial parametrization tracks to be generated；

Step 5), according to automobile three-degree-of-freedom motion model and parameterized track to be generated, track optimizing model constraints, target setting function and optimized variable are set, and according to the longitudinal velocity of automobile, yaw velocity, side slip angle, front wheel steering angle and vehicle front obstacle distance, speed, acceleration, it is solved, obtain track optimizing model；

Step 6), based on dynamic particles colony optimization algorithm, the track optimizing model set up is solved, obtains planned trajectory。

As the further prioritization scheme of the method for planning track of this Automotive active anti-collision system, according to below equation establishment step 3) described in automobile three-degree-of-freedom motion model:

\{\begin{matrix} \overset{\cdot}{x} (t) = v \cos (θ) \\ \overset{\cdot}{y} (t) = v \sin (θ) \\ \overset{\cdot}{θ} (t) = v \tan δ (t) / l \end{matrix}

Wherein, x and y is the longitudinal coordinate at automobile hind axle midpoint and lateral coordinate respectively, and θ is the yaw angle of automobile, and δ is vehicle front steering angle, and v is the longitudinal velocity of automobile, and l is the spacing between automobile front axle and rear axle, and t is the current time of trajectory planning。

As the further prioritization scheme of the method for planning track of this Automotive active anti-collision system, step 5) described in the seven parameterized equation of locus of order polynomial be:

\{\begin{matrix} x_{d} (t) = x_{d 0} + x_{d 1} t + x_{d 2} t^{2} + x_{d 3} t^{3} + x_{d 4} t^{4} + x_{d 5} t^{5} + x_{d 6} t^{6} + x_{d 7} t^{7} \\ y_{d} (t) = y_{d 0} + y_{d 1} t + y_{d 2} t^{2} + y_{d 3} t^{3} + y_{d 4} t^{4} + y_{d 5} t^{5} + y_{d 6} t^{6} + y_{d 7} t^{7} \end{matrix}

Wherein, x_d0、x_d1、x_d2、x_d3、x_d4、x_d5、x_d6、x_d7、y_d0、y_d1、y_d2、y_d3、y_d4、y_d5、y_d6、y_d7It is polynomial undetermined coefficient, (x_d(t),y_d(t)) for track to be generated。

As the further prioritization scheme of the method for planning track of this Automotive active anti-collision system, the constraints of the track optimizing model described in step 6 is:

\{\begin{matrix} x_{d} (t_{0}) = x_{0} \\ {\overset{\cdot}{x}}_{d} (t_{0}) = v_{0} {cosθ}_{0} \\ {\overset{\cdot\cdot}{x}}_{d} (t_{0}) = {\overset{\cdot}{v}}_{0} {cosθ}_{0} - v_{0}^{2} {tanδ}_{0} {sinθ}_{0} / l \\ x_{d} (t_{f}) = x_{f} \\ {\overset{\cdot}{x}}_{d} (t_{f}) = v_{f} {cosθ}_{f} \\ {\overset{\cdot\cdot}{x}}_{d} (t_{f}) = {\overset{\cdot}{v}}_{f} {cosθ}_{f} - v_{f}^{2} {tanδ}_{f} {sinθ}_{f} / l \\ y_{d} (t_{0}) = y_{0} \\ {\overset{\cdot}{y}}_{d} (t_{0}) = v_{0} {sinθ}_{0} \\ {\overset{\cdot\cdot}{y}}_{d} (t_{0}) = {\overset{\cdot}{v}}_{0} {sinθ}_{0} + v_{0}^{2} {tanδ}_{0} {cosθ}_{0} / l \\ y_{d} (t_{f}) = y_{f} \\ {\overset{\cdot}{y}}_{d} (t_{f}) = v_{f} \sin θ f \\ {\overset{\cdot\cdot}{y}}_{d} (t_{f}) = {\overset{\cdot}{v}}_{f} {sinθ}_{f} + v_{f}^{2} {tanδ}_{f} {cosθ}_{f} / l \end{matrix}

(R₀+R₁)²≤[PL^-1(H₁-Mx_d6-Nx_d7)+x_d6t⁶+x_d7t⁷-x₀-v_x(t-t₀)]²

+[PL^-1(H₂-My_d6-Ny_d7)+y_d6t⁶+y_d7t⁷-y₀-v_y(t-t₀)]²；

Wherein,

\{\begin{matrix} \overset{&RightArrow;}{x} = {[x_{d 0}, x_{d 1}, x_{d 2}, x_{d 3}, x_{d 4}, x_{d 5}]}^{T} \\ \overset{&RightArrow;}{y} = {[y_{d 0}, y_{d 1}, y_{d 2}, y_{d 3}, y_{d 4}, y_{d 5}]}^{T} \\ H_{1} = {[x_{d} (t_{0}), {\overset{\cdot}{x}}_{d} (t_{0}), {\overset{\cdot\cdot}{x}}_{d} (t_{0}), x_{d} (t_{f}), {\overset{\cdot}{x}}_{d} (t_{f}), {\overset{\cdot\cdot}{x}}_{d} (f_{f})]}^{T} \\ H_{2} = {[y_{d} (t_{0}), {\overset{\cdot}{y}}_{d} (t_{0}), {\overset{\cdot\cdot}{y}}_{d} (t_{0}), y_{d} (t_{f}), {\overset{\cdot}{y}}_{d} (t_{f}), {\overset{\cdot\cdot}{y}}_{d} (f_{f})]}^{T} \\ M = {[t_{0}^{6}, 6 t_{0}^{5}, 30 t_{0}^{4}, t_{f}^{6}, 6 t_{f}^{5}, 30 t_{f}^{4}]}^{T} \\ N = {[t_{0}^{7}, 7 t_{0}^{6}, 42 t_{0}^{5}, t_{f}^{7}, 7 t_{f}^{6}, 42 t_{f}^{5}]}^{T} \end{matrix},

P=[1tt²t³t⁴t⁵],

L = [\begin{matrix} 1 & t_{0} & t_{0}^{2} & t_{0}^{3} & t_{0}^{4} & t_{0}^{5} \\ 0 & 1 & 2 t_{0} & 3 t_{0}^{2} & 4 t_{0}^{3} & 5 t_{0}^{4} \\ 0 & 0 & 2 & 6 t_{0} & 12 t_{0}^{2} & 20 t_{0}^{3} \\ 1 & t_{f} & t_{f}^{2} & t_{f}^{3} & t_{f}^{4} & t_{f}^{5} \\ 0 & 1 & 2 t_{f} & 3 t_{f}^{2} & 4 t_{f}^{3} & 5 t_{f}^{4} \\ 0 & 0 & 2 & 6 t_{f} & 12 t_{f}^{2} & 20 t_{f}^{3} \end{matrix}],

t₀For trajectory planning initial time, t_fEnd the moment for trajectory planning,For initial time t₀The state of automobile,For moment t of ending_fThe state of automobile；

R₀For the half of motor vehicle length, R₁For the half with barrier width；

Object function is

J (x_{d}, y_{d}) = {&Integral;}_{t_{0}}^{t_{f}} {w_{1} [{(x_{d} - x^{'})}^{2} + {(y_{d} - y^{'})}^{2}] + w_{2} a_{y}^{2}} d t,

Wherein, w₁And w₂It is weight coefficient, and w₁+w₂=1；A_yIt it is automobile side angle acceleration；

\{\begin{matrix} x^{'} = \frac{x_{f} - x_{0}}{t_{f} - t_{0}} (t - t_{0}) \\ y^{'} = \frac{y_{f} - y_{0}}{t_{f} - t_{0}} (t - t_{0}) \end{matrix}

The variable optimized is x_d6、x_d7、y_d6、y_d7。

The present invention adopts above technical scheme compared with prior art, has following technical effect that

1. the track that method for planning track of the present invention generates meets various nonholonomic constraint and actuator constraint；

2. the track trajectory tortuosity that method for planning track of the present invention generates has seriality, has dynamic real-time, it is possible to adapt to the road environment of dynamically change；

3. the track generated by following the tracks of method for planning track of the present invention can make automobile be effectively shielded from barrier, it is prevented that the generation of vehicle accident。

Accompanying drawing explanation

Fig. 1 is Active collision avoidance system structural representation of the present invention；

Fig. 2 is the present invention actively collision avoidance process schematic；

Fig. 3 is the automobile three-degree-of-freedom motion model of the present invention。

Detailed description of the invention

Below in conjunction with accompanying drawing, technical scheme is described in further detail:

As shown in Figure 1, the invention discloses a kind of Automotive active anti-collision system, comprise forward-looking radar, photographic head, signal processing module, vehicle speed sensor, yaw-rate sensor, side slip angle sensor, steering wheel angle sensor, electronic control unit ECU, throttle control, steering controller and brake monitor；

The invention also discloses a kind of method for planning track based on this Automotive active anti-collision system, comprise step in detail below:

Step 1, obtain the distance of vehicle front barrier, speed, acceleration and width by forward-looking radar and photographic head, and by preceding object thing and distance between automobile and safe distance threshold comparison set in advance, if less than default safe distance threshold value, then perform step 2。

Step 2, obtain the longitudinal velocity of automobile, yaw velocity, side slip angle and front wheel steering angle by vehicle speed sensor, yaw-rate sensor, side slip angle sensor, steering wheel angle sensor。

Step 3, set up automobile three-degree-of-freedom motion model, as shown in Figure 3:

\{\begin{matrix} \overset{\cdot}{x} (t) = v \cos (θ) \\ \overset{\cdot}{y} (t) = v \sin (θ) \\ \overset{\cdot}{θ} (t) = v \tan δ (t) / l \end{matrix} - - - (1)

Step 4, entrance circulation。

Step 5, set trajectory planning initial time as t₀, the trajectory planning end of a period moment is t_f, with seven order polynomial parametrization tracks to be generated:

\{\begin{matrix} x_{d} (t) = x_{d 0} + x_{d 1} t + x_{d 2} t^{2} + x_{d 3} t^{3} + x_{d 4} t^{4} + x_{d 5} t^{5} + x_{d 6} t^{6} + x_{d 7} t^{7} \\ y_{d} (t) = y_{d 0} + y_{d 1} t + y_{d 2} t^{2} + y_{d 3} t^{3} + y_{d 4} t^{4} + y_{d 5} t^{5} + y_{d 6} t^{6} + y_{d 7} t^{7} \end{matrix} - - - (2)

Wherein, x_d0、x_d1、x_d2、x_d3、x_d4、x_d5、x_d6、x_d7、y_d0、y_d1、y_d2、y_d3、y_d4、y_d5、y_d6、y_d7It it is polynomial undetermined coefficient。

Step 6, track optimizing model constraints, target setting function and optimized variable are set according to automobile three-degree-of-freedom motion model and parameterized track to be generated, and according to the longitudinal velocity of automobile, yaw velocity, side slip angle, front wheel steering angle, and it is solved by vehicle front obstacle distance, speed, acceleration, obtain track optimizing model:

1) constraints:

It is located at initial time t₀The state of vehicle A isAt moment t of ending_fThe state of vehicle A isAnd designed track is (x_d(t),y_d(t))。Then, according to vehicle kinematics model (1), the equality constraint being applied on designed track is as follows:

\{\begin{matrix} x_{d} (t_{0}) = x_{0} \\ {\overset{\cdot}{x}}_{d} (t_{0}) = v_{0} {cosθ}_{0} \\ {\overset{\cdot\cdot}{x}}_{d} (t_{0}) = {\overset{\cdot}{v}}_{0} {cosθ}_{0} - v_{0}^{2} {tanδ}_{0} {sinθ}_{0} / l \\ x_{d} (t_{f}) = x_{f} \\ {\overset{\cdot}{x}}_{d} (t_{f}) = v_{f} {cosθ}_{f} \\ {\overset{\cdot\cdot}{x}}_{d} (t_{f}) = {\overset{\cdot}{v}}_{f} {cosθ}_{f} - v_{f}^{2} {tanδ}_{f} {sinθ}_{f} / l \\ y_{d} (t_{0}) = y_{0} \\ {\overset{\cdot}{y}}_{d} (t_{0}) = v_{0} {sinθ}_{0} \\ {\overset{\cdot\cdot}{y}}_{d} (t_{0}) = {\overset{\cdot}{v}}_{0} {sinθ}_{0} + v_{0}^{2} {tanδ}_{0} {cosθ}_{0} / l \\ y_{d} (t_{f}) = y_{f} \\ {\overset{\cdot}{y}}_{d} (t_{f}) = v_{f} \sin θ f \\ {\overset{\cdot\cdot}{y}}_{d} (t_{f}) = {\overset{\cdot}{v}}_{f} {sinθ}_{f} + v_{f}^{2} {tanδ}_{f} {cosθ}_{f} / l \end{matrix} - - - (3)

Being updated in equality constraint (3) by equation of locus (2), and turned to matrix form, its coefficient can be determined by below equation:

\{\begin{matrix} \overset{&RightArrow;}{x} = L^{- 1} (H_{1} - {Mx}_{d 6} - {Nx}_{d 7}) \\ \overset{&RightArrow;}{y} = L^{- 1} (H_{2} - {My}_{d 6} - {Ny}_{d 7}) \end{matrix} - - - (4)

Wherein,

\{\begin{matrix} \overset{&RightArrow;}{x} = {[x_{d 0}, x_{d 1}, x_{d 2}, x_{d 3}, x_{d 4}, x_{d 5}]}^{T} \\ \overset{&RightArrow;}{y} = {[y_{d 0}, y_{d 1}, y_{d 2}, y_{d 3}, y_{d 4}, y_{d 5}]}^{T} \\ H_{1} = {[x_{d} (t_{0}), {\overset{\cdot}{x}}_{d} (t_{0}), {\overset{\cdot\cdot}{x}}_{d} (t_{0}), x_{d} (t_{f}), {\overset{\cdot}{x}}_{d} (t_{f}), {\overset{\cdot\cdot}{x}}_{d} (f_{f})]}^{T} \\ H_{2} = {[y_{d} (t_{0}), {\overset{\cdot}{y}}_{d} (t_{0}), {\overset{\cdot\cdot}{y}}_{d} (t_{0}), y_{d} (t_{f}), {\overset{\cdot}{y}}_{d} (t_{f}), {\overset{\cdot\cdot}{y}}_{d} (f_{f})]}^{T} \\ M = {[t_{0}^{6}, 6 t_{0}^{5}, 30 t_{0}^{4}, t_{f}^{6}, 6 t_{f}^{5}, 30 t_{f}^{4}]}^{T} \\ N = {[t_{0}^{7}, 7 t_{0}^{6}, 42 t_{0}^{5}, t_{f}^{7}, 7 t_{f}^{6}, 42 t_{f}^{5}]}^{T} \end{matrix};

And

L = [\begin{matrix} 1 & t_{0} & t_{0}^{2} & t_{0}^{3} & t_{0}^{4} & t_{0}^{5} \\ 0 & 1 & 2 t_{0} & 3 t_{0}^{2} & 4 t_{0}^{3} & 5 t_{0}^{4} \\ 0 & 0 & 2 & 6 t_{0} & 12 t_{0}^{2} & 20 t_{0}^{3} \\ 1 & t_{f} & t_{f}^{2} & t_{f}^{3} & t_{f}^{4} & t_{f}^{5} \\ 0 & 1 & 2 t_{f} & 3 t_{f}^{2} & 4 t_{f}^{3} & 5 t_{f}^{4} \\ 0 & 0 & 2 & 6 t_{f} & 12 t_{f}^{2} & 20 t_{f}^{3} \end{matrix}] .

Equation (4) is updated to equation of locus (2) the further expression formula of equation of locus can be obtained:

\{\begin{matrix} x_{d} (t) = {PL}^{- 1} (H_{1} - {Mx}_{d 6} - {Nx}_{d 7}) + x_{d 6} t^{6} + x_{d 7} t^{7} \\ y_{d} (t) = {PL}^{- 1} (H_{2} - {My}_{d 6} - {Ny}_{d 7}) + y_{d 6} t^{6} + y_{d 7} t^{7} \end{matrix} - - - (5)

Wherein P=[1tt²t³t⁴t⁵]。

In order to realize the requirement of collision prevention, in addition it is also necessary to meet some inequality constraints conditions:

(R₀+R₁)²≤[x_d(t)-x₀-v_x(t-t₀)]²+[y_d(t)-y₀-v_y(t-t₀)]²

(6)

Wherein, R₀For the half of motor vehicle length, R₁For the half with barrier width；

Equation (5) is updated in (6) further expression formula can be obtained:

(R₀+R₁)²≤[PL^-1(H₁-Mx_d6-Nx_d7)+x_d6t⁶+x_d7t⁷-x₀-v_x(t-t₀)]²

+[PL^-1(H₂-My_d6-Ny_d7)+y_d6t⁶+y_d7t⁷-y₀-v_y(t-t₀)]²(7)

2) object function, in general, in the process of intelligent automobile actively collision avoidance, institute's planned trajectory must is fulfilled for some conditions, for instance, at the simultaneously effective avoiding obstacles ensureing intact stability, based on such consideration, select the object function using minor function as optimization:

J (x_{d}, y_{d}) = {&Integral;}_{t_{0}}^{t_{f}} {w_{1} [{(x_{d} - x^{'})}^{2} + {(y_{d} - y^{'})}^{2}] + w_{2} a_{y}^{2}} d t - - - (8)

Wherein, w₁And w₂It is weight coefficient, and w₁+w₂=1；A_yIt it is automobile side angle acceleration；；

\{\begin{matrix} x^{'} = \frac{x_{f} - x_{0}}{t_{f} - t_{0}} (t - t_{0}) \\ y^{'} = \frac{y_{f} - y_{0}}{t_{f} - t_{0}} (t - t_{0}) \end{matrix}

3) optimized variable, it is easily seen that variable to be optimized is from equation (5): x_d6、x_d7、y_d6、y_d7。

Step 7, based on dynamic particles colony optimization algorithm, the track optimizing model set up is solved, to obtain required track:

Particle swarm optimization algorithm, also known as particle swarm optimization, its colony constituted based on particle, the solution for each optimization problem is to find a particle in its feas ible space。In order to allow particle can search for and keep the multiformity of particle in global scope, the present invention adopts dynamic particles group's algorithm (dynamicParticleSwarmOptimization, DPSO) that track Optimized model is optimized。

If the D dimension space position vector of population is x_i=(x_i1,x_i2,...,x_iD), each x_iRepresent a potential feasible solution in solution space, can judge whether it is optimal solution according to the adaptive value that object function calculates。The D dimension space velocity vector of i-th particle is v_i=(v_i1,v_i2,...,v_iD), i-th particle personal best particle P_i=(P_i1,P_i2,...,P_iD), population colony optimal location L_i=(L_i1,L_i2,...,L_iD), global optimum of population colony position G=(G₁,G₂,...,G_D) iterative formula is as follows:

v_i(t+1)=ω v_it+b₁r₁(p_i(t)-x_i(t))+b₂r₂(L_i(t)-x_i(t))+b₂r₃(G(t)-x_i(t))(9)

In formula: b₁、b₂、b₃For normal number；R₁、r₂、r₃For the random number in [0,1]；Parameter ω is inertial factor。

If ω according to cycle-index from ω_sLinear decrease is to ω_e, maximum cycle is I_max, the current number of times of circulation is I_c, then the value of ω can be drawn by following formula:

ω = ω_{s} - (ω_{s} - ω_{e}) \frac{I_{c}}{I_{m a x}} - - - (10)

In formula: ω_sFor optimizing initial inertial factor；ω_eFor optimizing the inertial factor terminated。

In population, the particle position in the t+1 moment is obtained by following formula:

x_i(t+1)=x_i(t)+v_i(t+1)(11)

If population is updated to has surmounted definition domain border afterwards, then needing to readjust the position of particle so that it is drop in decision space, new position can calculate according to the following formula:

x_i(t+1)=x_i(t)+λv_i(t+1)(12)

λ=2/ (γ²+2)(13)

In formula: λ is speed regulation coefficient, and it is between (0,1)；γ is for adjusting number of times, and when γ is more than 3, particle rapidity becomes reversely。

Distance between particle i and k | | x_i-x_k| | can be tried to achieve by following formula:

| | x_{i} - x_{k} | | = \sqrt{(Σ_{i = 1}^{D} {(x_{i L} - x_{k L})}^{2}) / d} - - - (14)

In formula: d is the dimension of decision variable。

The generation of dynamic particles group: if having generated m population, it is assumed that the population nearest with population a is b, if the distance between them is more than D_max, then need to generate a population x_m+1, i-th particle kth dimension component in groupCan be tried to achieve by following formula:

x_{m + 1}^{i k} = (x_{a}^{i k} + x_{b}^{i k}) / 2 + c_{1} {(- 1)}^{r o u n d (0.5 + c_{2})} | x_{a}^{i k} - x_{b}^{i k} | / 2 - - - (15)

In formula: C₁、C₂For the random number in [0,1]；Round () is bracket function, therefore, and round (0.5+C₂) it is 0 or 1。

The relevant parameter of generated track is exported throttle control, steering controller, brake monitor by step 8, ECU, and performs corresponding operating, with the generated track of accurate tracking。

Step 9, jumping to step 4, the track carrying out subsequent time solves and tracking, so circulates, until planning terminates, completes whole collision avoidance process, as shown in Figure 2。

Those skilled in the art of the present technique it is understood that unless otherwise defined, all terms used herein (include technical term and scientific terminology) and have with the those of ordinary skill in art of the present invention be commonly understood by identical meaning。Should also be understood that in such as general dictionary, those terms of definition should be understood that have the meaning consistent with the meaning in the context of prior art, and unless defined as here, will not explain by idealization or excessively formal implication。

Above-described detailed description of the invention; the purpose of the present invention, technical scheme and beneficial effect have been further described; it is it should be understood that; the foregoing is only the specific embodiment of the present invention; it is not limited to the present invention; all within the spirit and principles in the present invention, any amendment of making, equivalent replacement, improvement etc., should be included within protection scope of the present invention。

Claims

1. an Automotive active anti-collision system, it is characterized in that, comprise forward-looking radar, photographic head, signal processing module, vehicle speed sensor, yaw-rate sensor, side slip angle sensor, steering wheel angle sensor, Electronic Control power ECU, throttle control, steering controller and brake monitor；

2. based on the method for planning track of the Automotive active anti-collision system described in claim 1, it is characterised in that comprise the steps of

Step 4), with seven order polynomial parametrization tracks to be generated；

3. the method for planning track of Automotive active anti-collision system according to claim 2, it is characterised in that according to below equation establishment step 3) described in automobile three-degree-of-freedom motion model:

\{\begin{matrix} \overset{\cdot}{x} (t) = v c o s (θ) \\ \overset{\cdot}{y} (t) = v \sin (θ) \\ \overset{\cdot}{θ} (t) = v \tan δ (t) / l \end{matrix}

4. the method for planning track of Automotive active anti-collision system according to claim 3, it is characterised in that step 5) described in the seven parameterized equation of locus of order polynomial be:

\{\begin{matrix} x_{d} (t) = x_{d 0} + x_{d 1} t + x_{d 2} t^{2} + x_{d 3} t^{3} + x_{d 4} t^{4} + x_{d 5} t^{5} + x_{d 6} t^{6} + x_{d 7} t^{7} \\ y_{d} (t) = y_{d 0} + y_{d 1} t + y_{d 2} t^{2} + y_{d 3} t^{3} + y_{d 4} t^{4} + y_{d 5} t^{5} + y_{d 6} t^{6} + y_{d 7} t^{7} \end{matrix}

Wherein, x_d0、x_d1、x_d2、x_d3、x_d4、x_d5、x_d6、x_d7、y_d0、y_d1、y_d2、y_d3、y_d4、y_d5、y_d6、y_d7It is polynomial undetermined coefficient, (x_d(t),y_d(t)) for the coordinate in length and breadth of track to be generated。

5. the method for planning track of Automotive active anti-collision system according to claim 4, it is characterised in that the constraints of the track optimizing model described in step 6 is:

\{\begin{matrix} x_{d} (t_{0}) = x_{0} \\ {\overset{\cdot}{x}}_{d} (t_{0}) = v_{0} {cosθ}_{0} \\ {\overset{\cdot\cdot}{x}}_{d} (t_{0}) = {\overset{\cdot}{v}}_{0} {cosθ}_{0} - v_{0}^{2} {tanδ}_{0} {sinθ}_{0} / l \\ x_{d} (t_{f}) = x_{f} \\ {\overset{\cdot}{x}}_{d} (t_{f}) = v_{f} {cosθ}_{f} \\ {\overset{\cdot\cdot}{x}}_{d} (t_{f}) = {\overset{\cdot}{v}}_{f} {cosθ}_{f} - v_{f}^{2} {tanδ}_{f} {sinθ}_{f} / l \\ y_{d} (t_{0}) = y_{0} \\ {\overset{\cdot}{y}}_{d} (t_{0}) = v_{0} {sinθ}_{0} \\ {\overset{\cdot\cdot}{y}}_{d} (t_{0}) = {\overset{\cdot}{v}}_{0} {sinθ}_{0} + v_{0}^{2} {tanδ}_{0} {cosθ}_{0} / l \\ y_{d} (t_{f}) = y_{f} \\ {\overset{\cdot}{y}}_{d} (t_{f}) = v_{f} {sinθ}_{f} \\ {\overset{\cdot\cdot}{y}}_{d} (t_{f}) = {\overset{\cdot}{v}}_{f} {sinθ}_{f} + v_{f}^{2} {tanδ}_{f} {cosθ}_{f} / l \end{matrix}

(R₀+R₁)²≤[PL^-1(H₁-Mx_d6-Nx_d7)+x_d6t⁶+x_d7t⁷-x₀-v_x(t-t₀)]²+[PL^-1(H₂-My_d6-Ny_d7)+y_d6t⁶+y_d7t⁷-y₀-v_y(t-t₀)]²；

Wherein,

\{\begin{matrix} \overset{&RightArrow;}{x} = {[x_{d 0}, x_{d 1}, x_{d 2}, x_{d 3}, x_{d 4}, x_{d 5}]}^{T} \\ \overset{&RightArrow;}{y} = {[y_{d 0}, y_{d 1}, y_{d 2}, y_{d 3}, y_{d 4}, y_{d 5}]}^{T} \\ H_{1} = {[x_{d} (t_{0}), {\overset{\cdot}{x}}_{d} (t_{0}), {\overset{\cdot \cdot}{x}}_{d} (t_{0}), x_{d} (t_{f}), {\overset{\cdot}{x}}_{d} (t_{f}), {\overset{\cdot \cdot}{x}}_{d} (t_{f})]}^{T} \\ H_{2} = {[y_{d} (t_{0}), {\overset{\cdot}{y}}_{d} (t_{0}), {\overset{\cdot \cdot}{y}}_{d} (t_{0}), y_{d} (t_{f}), {\overset{\cdot}{y}}_{d} (t_{f}), {\overset{\cdot \cdot}{y}}_{d} (t_{f})]}^{T} \\ M = {[t_{0}^{6}, {6 t}_{0}^{5}, {30 t}_{0}^{4}, t_{f}^{6}, {6 t}_{f}^{5}, {30 t}_{f}^{4}]}^{T} \\ N = {[t_{0}^{7}, {7 t}_{0}^{6}, {42 t}_{0}^{5}, t_{f}^{7}, {7 t}_{f}^{6}, {42 t}_{f}^{5}]}^{T} \end{matrix}

P=[1tt²t³t⁴t⁵],

L = [\begin{matrix} 1 & t_{0} & t_{0}^{2} & t_{0}^{3} & t_{0}^{4} & t_{0}^{5} \\ 0 & 1 & 2 t_{0} & 3 t_{0}^{2} & 4 t_{0}^{3} & 5 t_{0}^{4} \\ 0 & 0 & 2 & 6 t_{0} & 12 t_{0}^{2} & 20 t_{0}^{3} \\ 1 & t_{f} & t_{f}^{2} & t_{f}^{3} & t_{f}^{4} & t_{f}^{5} \\ 0 & 1 & 2 t_{f} & 3 t_{f}^{2} & 4 t_{f}^{3} & 5 t_{f}^{4} \\ 0 & 0 & 2 & 6 t_{f} & 12 t_{f}^{2} & 20 t_{f}^{3} \end{matrix}],

Object function is

J (x_{d}, y_{d}) = {&Integral;}_{t_{0}}^{t_{f}} {w_{1} [{(x_{d} - x^{'})}^{2} + {(y_{d} - y^{'})}^{2}] + w_{2} a_{y}^{2}} dt,

\{\begin{matrix} x^{'} = \frac{x_{f} - x_{0}}{t_{f} - t_{0}} (t - t_{0}) \\ y^{'} = \frac{y_{f} - y_{0}}{t_{f} - t_{0}} (t - t_{0}) \end{matrix}

The variable optimized is x_d6、x_d7、y_d6、y_d7。