CN205396080U

CN205396080U - Car initiative collision avoidance system

Info

Publication number: CN205396080U
Application number: CN201620037056.9U
Authority: CN
Inventors: 徐志江; 赵万忠; 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: 2016-07-27
Anticipated expiration: 2026-01-14

Abstract

The utility model discloses an active collision avoidance system for automobiles, which comprises a forward-looking radar, a camera, a vehicle speed sensor, a yaw angular velocity sensor, a center of mass side slip angle sensor, a signal processing module, an electronic control power supply ECU, a throttle controller, and a steering controller , Brake controller. During the driving process of the car, the electronic control unit collects the signals from various sensors through the signal processing module in real time, and judges the current road conditions and vehicle conditions of the car in real time. If a dangerous situation may occur at this time, the ECU executes the internal preset The predetermined trajectory planning program generates a continuous non-collision executable trajectory, and outputs relevant signals to the accelerator controller, steering controller and brake controller for corresponding operations to avoid dangerous situations. The utility model can assist the driver to operate the car in an emergency, and can improve the driving active safety performance.

Description

A vehicle active collision avoidance system

技术领域technical field

本实用新型涉及汽车辅助驾驶领域，尤其涉及一种汽车主动避撞系统。The utility model relates to the field of automobile auxiliary driving, in particular to an automobile active collision avoidance system.

背景技术Background technique

随着智能交通在全球范围的兴起，汽车辅助驾驶技术受到了越来越多的关注，其研究的主要目的在于降低日趋严重的交通事故发生率，提高现有道路交通效率。国际上众多的研究机构，工业设计单位对其研发过程正投入大量的人力、物力、财力来进行相关关键技术的研发。With the rise of intelligent transportation on a global scale, automobile assisted driving technology has received more and more attention. The main purpose of its research is to reduce the incidence of increasingly serious traffic accidents and improve the efficiency of existing road traffic. Numerous research institutions and industrial design units in the world are investing a lot of manpower, material resources and financial resources in the research and development of related key technologies.

主动避撞系统作为汽车辅助驾驶技术的一项重要研究内容，其研究的主要目的是提高车辆驾驶的安全性能，其主要利用现代信息技术、传感技术来扩展驾驶人员的感知能力，将外界信息(如车速、障碍物距离、速度、方向等)传递给驾驶人员的同时综合利用车况与路况信息，判断汽车当前运行状况的安全程度，在紧急情况下能自动的采取措施控制汽车，使得汽车主动地避开危险，保证汽车安全行驶或最大可能的减小事故的伤害程度。汽车只有具备了这样的主动安全性能，才可能从根本上减少交通事故，提高交通安全。Active collision avoidance system is an important research content of automobile assisted driving technology. The main purpose of its research is to improve the safety performance of vehicle driving. It mainly uses modern information technology and sensor technology to expand the driver's perception ability and integrate external information (such as vehicle speed, obstacle distance, speed, direction, etc.) are transmitted to the driver while comprehensively utilizing the vehicle condition and road condition information to judge the safety level of the current operating condition of the vehicle, and automatically take measures to control the vehicle in an emergency, so that the vehicle takes the initiative Avoid the danger as much as possible, ensure the safe driving of the car or reduce the degree of injury of the accident to the greatest possible extent. Only when the car has such active safety performance can it be possible to fundamentally reduce traffic accidents and improve traffic safety.

轨迹规划技术是主动避撞系统中的一项关键技术，要想实现对车辆的智能控制，其前提条件是就要生成可行的参考轨迹，并将轨迹的参数提供给跟踪控制器，以便于控制器能够控制汽车按照所规划的轨迹行驶，因此，如何在紧急情况下规划一条可行的无碰轨迹显得尤为重要。Trajectory planning technology is a key technology in the active collision avoidance system. In order to realize the intelligent control of the vehicle, the prerequisite is to generate a feasible reference trajectory and provide the parameters of the trajectory to the tracking controller to facilitate the control. The controller can control the car to drive according to the planned trajectory. Therefore, how to plan a feasible collision-free trajectory in an emergency is particularly important.

实用新型内容Utility model content

本实用新型所要解决的技术问题是针对背景技术中所涉及到的缺陷，提供一种汽车主动避撞系统，解决了在紧急情况下主动避撞系统的轨迹规划问题，通过软硬件相结合的方式在保证汽车操纵稳定性的同时有效的避开障碍物，避免交通事故的发生，实现了汽车的主动安全功能。The technical problem to be solved by the utility model is to provide an active collision avoidance system for automobiles in view of the defects involved in the background technology, which solves the trajectory planning problem of the active collision avoidance system in emergency situations, through the combination of software and hardware While ensuring the stability of the car's handling, it can effectively avoid obstacles, avoid traffic accidents, and realize the active safety function of the car.

本实用新型为解决上述技术问题采用以下技术方案：The utility model adopts the following technical solutions for solving the above-mentioned technical problems:

一种汽车主动避撞系统，包含前视雷达、摄像头、信号处理模块、车速传感器、横摆角速度传感器、质心侧偏角传感器、方向盘转角传感器、电子控制电源ECU、油门控制器、转向控制器和制动控制器；An active collision avoidance system for a car, including a forward-looking radar, a camera, a signal processing module, a vehicle speed sensor, a yaw rate sensor, a center-of-mass side slip angle sensor, a steering wheel angle sensor, an electronic control power supply ECU, a throttle controller, a steering controller and brake controller;

所述前视雷达、摄像头通过信号处理模块和所述电子控制电源ECU相连；所述电子控制电源ECU分别和、车速传感器、横摆角速度传感器、质心侧偏角传感器、方向盘转角传感器、油门控制器、转向控制器、制动控制器相连；The forward-looking radar and the camera are connected to the electronic control power supply ECU through a signal processing module; , the steering controller and the brake controller are connected;

所述前视雷达与摄像头均安装在汽车前方，用于检测汽车前方的道路情况，并将所测得的信号经信号处理模块处理后传递给所述电子控制单元ECU；Both the forward-looking radar and the camera are installed in front of the car to detect the road conditions in front of the car, and the measured signal is processed by the signal processing module and then transmitted to the electronic control unit ECU;

所述车速传感器、横摆角速度传感器、质心侧偏角传感器、方向盘转角传感器分别用于感应汽车的速度、横摆角速度、质心侧偏角和前轮转向角，并将采集到的信号经处理后送入到电子控制单元ECU；The vehicle speed sensor, the yaw rate sensor, the center of mass side slip angle sensor, and the steering wheel angle sensor are respectively used to sense the speed, yaw rate, center of mass side slip angle and front wheel steering angle of the vehicle, and the collected signals are processed Send to the electronic control unit ECU;

所述电子控制单元ECU用于根据接收到的信号输出相应的信号到油门控制器、转向控制器、制动控制器，进行相应加速、减速、制动操作，以保证行车安全。The electronic control unit ECU is used to output corresponding signals to the throttle controller, steering controller, and brake controller according to the received signals, and perform corresponding acceleration, deceleration, and braking operations to ensure driving safety.

本实用新型还公开了一种基于以上汽车主动避撞系统的轨迹规划方法，包含以下步骤：The utility model also discloses a trajectory planning method based on the above vehicle active collision avoidance system, which includes the following steps:

步骤1)，通过前视雷达与摄像头获取汽车前方障碍物的距离、速度、加速度和宽度，并将前方障碍物的与汽车之间的距离与预先设定的安全距离阈值对比，如果其小于预设的安全距离阈值，则执行步骤2)；Step 1), obtain the distance, speed, acceleration and width of the obstacle in front of the car through the forward-looking radar and the camera, and compare the distance between the obstacle in front and the car with the preset safety distance threshold, if it is less than the preset Set the safety distance threshold, then perform step 2);

步骤2)，通过车速传感器、横摆角速度传感器、质心侧偏角传感器、方向盘转角传感器获取汽车的速度、横摆角速度、质心侧偏角和前轮转向角；Step 2), obtain the speed of the car, the yaw rate, the side slip angle of the center of mass and the front wheel steering angle through the vehicle speed sensor, the yaw rate sensor, the center of mass side slip angle sensor, and the steering wheel angle sensor;

步骤3)，根据汽车的横摆角、前轮转向角、纵向速度、前轴与后轴之间的间距、以及后轴中点的纵向坐标和侧向坐标建立汽车三自由度运动学模型；Step 3), according to the yaw angle of the car, the steering angle of the front wheels, the longitudinal speed, the distance between the front axle and the rear axle, and the longitudinal and lateral coordinates of the midpoint of the rear axle, the three-degree-of-freedom kinematics model of the car is established;

步骤4)，用七次多项式参数化待生成轨迹；Step 4), parameterize the trajectory to be generated with a polynomial of degree seven;

步骤5)，根据汽车三自由度运动学模型和参数化的待生成轨迹设置轨迹优化模型约束条件、设定目标函数以及优化变量，并根据汽车的纵向速度、横摆角速度、质心侧偏角、前轮转向角、以及汽车前方障碍物距离、速度、加速度对其进行求解，得到轨迹优化模型；Step 5), according to the vehicle three-degree-of-freedom kinematics model and the parameterized trajectory to be generated, set the trajectory optimization model constraints, set the objective function and optimization variables, and according to the vehicle's longitudinal velocity, yaw rate, center of mass sideslip angle, The front wheel steering angle, and the distance, speed, and acceleration of obstacles in front of the car are solved to obtain the trajectory optimization model;

步骤6)，基于动态粒子群优化算法，对所建立的轨迹优化模型进行求解，得到规划轨迹。Step 6), based on the dynamic particle swarm optimization algorithm, the established trajectory optimization model is solved to obtain the planned trajectory.

作为该汽车主动避撞系统的轨迹规划方法的进一步优化方案，根据以下公式建立步骤3)中所述的汽车三自由度运动学模型：As a further optimization scheme of the trajectory planning method of the vehicle active collision avoidance system, the vehicle three-degree-of-freedom kinematics model described in step 3) is established according to the following formula:

$\{\begin{matrix} \overset{\cdot &Center Dot;}{x x} ((t t)) = = v v cos cos ((θ θ)) \\ \overset{\cdot \cdot}{y the y} ((t t)) = = v v sin sin ((θ θ)) \\ \overset{\cdot \cdot}{θ θ} ((t t)) = = v v tan the tan δ δ ((t t)) / / l l \end{matrix}$

其中，x和y分别是汽车后轴中点的纵向坐标和侧向坐标，θ是汽车的横摆角，δ是汽车前轮转向角，v是汽车的纵向速度，l是汽车前轴与后轴之间的间距，t是轨迹规划的当前时间。Among them, x and y are the longitudinal coordinates and lateral coordinates of the midpoint of the rear axle of the car respectively, θ is the yaw angle of the car, δ is the steering angle of the front wheels of the car, v is the longitudinal speed of the car, and l is the distance between the front axle and the rear axle of the car. The spacing between the axes, t is the current time of trajectory planning.

作为该汽车主动避撞系统的轨迹规划方法的进一步优化方案，步骤5)中所述的七次多项式参数化的轨迹方程为：As a further optimization scheme of the trajectory planning method of this automobile active collision avoidance system, the trajectory equation of the seventh degree polynomial parameterization described in step 5) is:

$\{\begin{matrix} {x x}_{d d} ((t t)) = = {x x}_{d d 00} + + {x x}_{d d 11} t t + + {x x}_{d d 22} {t t}^{22} + + {x x}_{d d 33} {t t}^{33} + + {x x}_{d d 44} {t t}^{44} + + {x x}_{d d 55} {t t}^{55} + + {x x}_{d d 66} {t t}^{66} + + {x x}_{d d 77} {t t}^{77} \\ {y the y}_{d d} ((t t)) = = {y the y}_{d d 00} + + {y the y}_{d d 11} t t + + {y the y}_{d d 22} {t t}^{22} + + {y the y}_{d d 33} {t t}^{33} + + {y the y}_{d d 44} {t t}^{44} + + {y the y}_{d d 55} {t t}^{55} + + {y the y}_{d d 66} {t t}^{66} + + {y the y}_{d d 77} {t t}^{77} \end{matrix}$

其中，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_d7是多项式的待定系数，(x_d(t),y_d(t))为待生成轨迹。Among them, 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 _d7 are Undetermined coefficients of the polynomial, (x _d (t), y _d (t)) is the trajectory to be generated.

作为该汽车主动避撞系统的轨迹规划方法的进一步优化方案，步骤6所述的轨迹优化模型的约束条件为：As a further optimization scheme of the trajectory planning method of the vehicle active collision avoidance system, the constraints of the trajectory optimization model described in step 6 are:

$\{\begin{matrix} {x x}_{d d} (({t t}_{00})) = = {x x}_{00} \\ {\overset{\cdot &Center Dot;}{x x}}_{d d} (({t t}_{00})) = = {v v}_{00} {cosθ cosθ}_{00} \\ {\overset{\cdot\cdot \cdot\cdot}{x x}}_{d d} (({t t}_{00})) = = {\overset{\cdot &Center Dot;}{v v}}_{00} {cosθ cosθ}_{00} - - {v v}_{00}^{22} {tanδ tanδ}_{00} {sinθ sinθ}_{00} / / l l \\ {x x}_{d d} (({t t}_{f f})) = = {x x}_{f f} \\ {\overset{\cdot \cdot}{x x}}_{d d} (({t t}_{f f})) = = {v v}_{f f} {cosθ cosθ}_{f f} \\ {\overset{\cdot\cdot \cdot\cdot}{x x}}_{d d} (({t t}_{f f})) = = {\overset{\cdot \cdot}{v v}}_{f f} {cosθ cosθ}_{f f} - - {v v}_{f f}^{22} {tanδ tanδ}_{f f} {sinθ sinθ}_{f f} / / l l \\ {y the y}_{d d} (({t t}_{00})) = = {y the y}_{00} \\ {\overset{\cdot \cdot}{y the y}}_{d d} (({t t}_{00})) = = {v v}_{00} {sinθ sinθ}_{00} \\ {\overset{\cdot\cdot \cdot\cdot}{x x}}_{d d} (({t t}_{00})) = = {\overset{\cdot \cdot}{v v}}_{00} {sinθ sinθ}_{00} + + {v v}_{00}^{22} {tanδ tanδ}_{00} {cosθ cosθ}_{00} / / l l \\ {y the y}_{d d} (({t t}_{f f})) = = {y the y}_{f f} \\ {\overset{\cdot &Center Dot;}{y the y}}_{d d} (({t t}_{f f})) = = {v v}_{f f} {sinθ sinθ}_{f f} \\ {\overset{\cdot\cdot \cdot\cdot}{x x}}_{d d} (({t t}_{f f})) = = {\overset{\cdot \cdot}{v v}}_{f f} {sinθ sinθ}_{f f} + + {v v}_{f f}^{22} {tanδ tanδ}_{f f} {cosθ cosθ}_{f f} / / l l \end{matrix}$

(R₀+R₁)²≤[PL^-1(H₁-Mx_d6-Nx_d7)+x_d6t⁶+x_d7t⁷-x₀-v_x(t-t₀)]² (R ₀ +R ₁ ) ² ≤[PL ^-1 (H ₁ -Mx _d6 -Nx _d7 )+x _d6 t ⁶ +x _d7 t ⁷ -x ₀ -v _x (tt ₀ )] ²

+[PL^-1(H₂-My_d6-Ny_d7)+y_d6t⁶+y_d7t⁷-y₀-v_y(t-t₀)]²；+[PL ^-1 (H ₂ -My _d6 -Ny _d7 )+y _d6 t ⁶ +y _d7 t ⁷ -y ₀ -v _y (tt ₀ )] ² ;

其中， $\{\begin{matrix} \overset{&RightArrow;}{x} = {[x_{d 0}, x_{d 1}, x_{d 2}, x_{d 3}, x_{d 4}, x_{d 5}]}^{T} \\ \overset{&RightArrow;}{y} = {[y_{d 0}, y_{d 1}, y_{d 2}, y_{d 3}, y_{d 4}, y_{d 5}]}^{T} \\ H_{1} = {[x_{d} (t_{0}), {\overset{\cdot}{x}}_{d} (t_{0}), {\overset{\cdot\cdot}{x}}_{d} (t_{0}), x_{d} (t_{f}), {\overset{\cdot}{x}}_{d} (t_{f}), {\overset{\cdot\cdot}{x}}_{d} (t_{f})]}^{T} \\ H_{2} = {[y_{d} (t_{0}), {\overset{\cdot}{y}}_{d} (t_{0}), {\overset{\cdot\cdot}{y}}_{d} (t_{0}), y_{d} (t_{f}), {\overset{\cdot}{y}}_{d} (t_{f}), {\overset{\cdot\cdot}{y}}_{d} (t_{f})]}^{T} \\ M = {[t_{0}^{6}, 6 t_{0}^{5}, 30 t_{0}^{4}, t_{f}^{6}, 6 t_{f}^{5}, 30 t_{f}^{4}]}^{T} \\ N = {[t_{0}^{7}, 7 t_{0}^{6}, 42 t_{0}^{5}, t_{f}^{7}, 7 t_{f}^{6}, 42 t_{f}^{5}]}^{T} \end{matrix},$ in, $\{\begin{matrix} \overset{&Right Arrow;}{x} = {[x_{d 0}, x_{d 1}, x_{d 2}, x_{d 3}, x_{d 4}, x_{d 5}]}^{T} \\ \overset{&Right Arrow;}{the y} = {[{the y}_{d 0}, {the y}_{d 1}, {the y}_{d 2}, {the y}_{d 3}, {the y}_{d 4}, {the y}_{d 5}]}^{T} \\ h_{1} = {[x_{d} (t_{0}), {\overset{\cdot}{x}}_{d} (t_{0}), {\overset{\cdot\cdot}{x}}_{d} (t_{0}), x_{d} (t_{f}), {\overset{\cdot}{x}}_{d} (t_{f}), {\overset{\cdot\cdot}{x}}_{d} (t_{f})]}^{T} \\ h_{2} = {[{the y}_{d} (t_{0}), {\overset{&Center Dot;}{the y}}_{d} (t_{0}), {\overset{\cdot\cdot}{the y}}_{d} (t_{0}), {the y}_{d} (t_{f}), {\overset{&Center Dot;}{the y}}_{d} (t_{f}), {\overset{\cdot\cdot}{the y}}_{d} (t_{f})]}^{T} \\ m = {[t_{0}^{6}, 6 t_{0}^{5}, 30 t_{0}^{4}, t_{f}^{6}, 6 t_{f}^{5}, 30 t_{f}^{4}]}^{T} \\ N = {[t_{0}^{7}, 7 t_{0}^{6}, 42 t_{0}^{5}, t_{f}^{7}, 7 t_{f}^{6}, 42 t_{f}^{5}]}^{T} \end{matrix},$

P＝[1tt²t³t⁴t⁵]，P=[1tt ² t ³ t ⁴ t ⁵ ],

$L L = = [\begin{matrix} 11 & {t t}_{00} & {t t}_{00}^{22} & {t t}_{00}^{33} & {t t}_{00}^{44} & {t t}_{00}^{55} \\ 00 & 11 & 22 {t t}_{00} & 33 {t t}_{00}^{22} & 44 {t t}_{00}^{33} & 55 {t t}_{00}^{44} \\ 00 & 00 & 22 & 66 {t t}_{00} & 1212 {t t}_{00}^{22} & 2020 {t t}_{00}^{33} \\ 11 & {t t}_{f f} & {t t}_{f f}^{22} & {t t}_{f f}^{33} & {t t}_{f f}^{44} & {t t}_{f f}^{55} \\ 00 & 11 & 22 {t t}_{f f} & 33 {t t}_{f f}^{22} & 44 {t t}_{f f}^{33} & 55 {t t}_{f f}^{44} \\ 00 & 00 & 22 & 66 {t t}_{f f} & 1212 {t t}_{f f}^{22} & 2020 {t t}_{f f}^{33} \end{matrix}],,$

t₀为轨迹规划初始时刻,t_f为轨迹规划终了时刻，为初始时刻t₀汽车的状态，为终了时刻t_f汽车的状态；t ₀ is the initial time of trajectory planning, t _f is the end time of trajectory planning, is the state of the car at the initial time t ₀ , is the state of the car at the final moment t _f ;

R₀为汽车长度的一半，R₁为与障碍物宽度的一半；R ₀ is half the length of the car, R ₁ is half the width of the obstacle;

目标函数为 $J (x_{d}, y_{d}) = {&Integral;}_{t_{0}}^{t_{f}} {w_{1} [{(x_{d} - x^{'})}^{2} + {(y_{d} - y^{'})}^{2}] + w_{2} a_{y}^{2}} d t,$ 其中，w₁和w₂是权重系数，且w₁+w₂＝1；a_y是汽车侧向加速度；The objective function is $J (x_{d}, {the y}_{d}) = {&Integral;}_{t_{0}}^{t_{f}} {w_{1} [{(x_{d} - x^{'})}^{2} + {({the y}_{d} - {the y}^{'})}^{2}] + w_{2} a_{the y}^{2}} d t,$ Among them, w ₁ and w ₂ are weight coefficients, and w ₁ +w ₂ =1; a _y is the lateral acceleration of the vehicle;

$\{\begin{matrix} {x x}^{' '} = = \frac{{x x}_{f f} - - {x x}_{00}}{{t t}_{f f} - - {t t}_{00}} ((t t - - {t t}_{00})) \\ {y the y}^{' '} = = \frac{{y the y}_{f f} - - {y the y}_{00}}{{t t}_{f f} - - {t t}_{00}} ((t t - - {t t}_{00})) \end{matrix}$

优化的变量为x_d6、x_d7、y_d6、y_d7。The optimized variables are x _d6 , x _d7 , y _d6 , y _d7 .

本实用新型采用以上技术方案与现有技术相比，具有以下技术效果：Compared with the prior art by adopting the above technical scheme, the utility model has the following technical effects:

1.本实用新型所述的轨迹规划方法所生成的轨迹满足各种非完整约束和执行机构约束；1. The trajectory generated by the trajectory planning method described in the utility model satisfies various non-holonomic constraints and actuator constraints;

2.本实用新型所述的轨迹规划方法所生成的轨迹轨迹曲率具有连续性，具有动态实时性，能够适应动态变化的道路环境；2. The trajectory trajectory curvature generated by the trajectory planning method described in the utility model has continuity, has dynamic real-time performance, and can adapt to the dynamically changing road environment;

3.通过跟踪本实用新型所述的轨迹规划方法所生成的轨迹能使汽车有效地避开障碍物，防止交通事故的发生。3. By tracking the trajectory generated by the trajectory planning method described in the utility model, the vehicle can effectively avoid obstacles and prevent traffic accidents.

附图说明Description of drawings

图1是本实用新型主动避撞系统结构示意图；Fig. 1 is a schematic structural diagram of the active collision avoidance system of the present invention;

图2是本实用新型主动避撞过程示意图；Fig. 2 is a schematic diagram of the active collision avoidance process of the utility model;

图3是本实用新型的汽车三自由度运动学模型。Fig. 3 is the three-degree-of-freedom kinematics model of the automobile of the present invention.

具体实施方式detailed description

下面结合附图对本实用新型的技术方案做进一步的详细说明：Below in conjunction with accompanying drawing, the technical scheme of the utility model is described in further detail:

如图1所示，本实用新型公开了一种汽车主动避撞系统，包含前视雷达、摄像头、信号处理模块、车速传感器、横摆角速度传感器、质心侧偏角传感器、方向盘转角传感器、电子控制单元ECU、油门控制器、转向控制器和制动控制器；As shown in Figure 1, the utility model discloses an active collision avoidance system for automobiles, which includes a forward-looking radar, a camera, a signal processing module, a vehicle speed sensor, a yaw rate sensor, a center-of-mass side slip angle sensor, a steering wheel angle sensor, an electronic control Unit ECU, accelerator controller, steering controller and brake controller;

本实用新型还公开了一种基于该汽车主动避撞系统的轨迹规划方法，包含以下具体步骤：The utility model also discloses a trajectory planning method based on the automobile active collision avoidance system, which includes the following specific steps:

步骤1、通过前视雷达与摄像头获取汽车前方障碍物的距离、速度、加速度和宽度，并将前方障碍物的与汽车之间的距离与预先设定的安全距离阈值对比，如果其小于预设的安全距离阈值，则执行步骤2。Step 1. Obtain the distance, speed, acceleration and width of the obstacle in front of the car through the forward-looking radar and camera, and compare the distance between the obstacle in front of the car and the preset safety distance threshold, if it is less than the preset If the safety distance threshold is set, go to step 2.

步骤2、通过车速传感器、横摆角速度传感器、质心侧偏角传感器、方向盘转角传感器获取汽车的纵向速度、横摆角速度、质心侧偏角和前轮转向角。Step 2. Obtain the longitudinal speed, yaw rate, side slip angle and front wheel steering angle of the car through the vehicle speed sensor, yaw rate sensor, mass center slip angle sensor, and steering wheel angle sensor.

步骤3、建立汽车三自由度运动学模型，如图3所示：Step 3. Establish a three-degree-of-freedom kinematics model of the car, as shown in Figure 3:

$\{\begin{matrix} \overset{\cdot &Center Dot;}{x x} ((t t)) = = v v cos cos ((θ θ)) \\ \overset{\cdot &Center Dot;}{y the y} ((t t)) = = v v sin sin ((θ θ)) \\ \overset{\cdot \cdot}{θ θ} ((t t)) = = v v tan the tan δ δ ((t t)) / / l l \end{matrix} - - - - - - ((11))$

步骤4、进入循环。Step 4, enter the cycle.

步骤5、设轨迹规划初始时刻为t₀,轨迹规划终了时刻为t_f，用七次多项式参数化待生成轨迹：Step 5. Set the initial time of trajectory planning as t ₀ , and the end time of trajectory planning as t _f , and parameterize the trajectory to be generated with a polynomial of degree seven:

$\{\begin{matrix} {x x}_{d d} ((t t)) = = {x x}_{d d 00} + + {x x}_{d d 11} t t + + {x x}_{d d 22} {t t}^{22} + + {x x}_{d d 33} {t t}^{33} + + {x x}_{d d 44} {t t}^{44} + + {x x}_{d d 55} {t t}^{55} + + {x x}_{d d 66} {t t}^{66} + + {x x}_{d d 77} {t t}^{77} \\ {y the y}_{d d} ((t t)) = = {y the y}_{d d 00} + + {y the y}_{d d 11} t t + + {y the y}_{d d 22} {t t}^{22} + + {y the y}_{d d 33} {t t}^{33} + + {y the y}_{d d 44} {t t}^{44} + + {y the y}_{d d 55} {t t}^{55} + + {y the y}_{d d 66} {t t}^{66} + + {y the y}_{d d 77} {t t}^{77} \end{matrix} - - - - - - ((22))$

其中，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_d7是多项式的待定系数。Among them, 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 _d7 are The undetermined coefficients of the polynomial.

步骤6、根据汽车三自由度运动学模型和参数化的待生成轨迹设置轨迹优化模型约束条件、设定目标函数以及优化变量，并根据汽车的纵向速度、横摆角速度、质心侧偏角、前轮转向角，以及汽车前方障碍物距离、速度、加速度对其进行求解，得到轨迹优化模型：Step 6. Set trajectory optimization model constraint conditions, objective function and optimization variables according to the vehicle three-degree-of-freedom kinematics model and the parameterized trajectory to be generated, and according to the vehicle's longitudinal velocity, yaw rate, center of mass sideslip angle, front The wheel steering angle, and the distance, speed, and acceleration of obstacles in front of the car are solved to obtain the trajectory optimization model:

1)约束条件：1) Constraints:

设在初始时刻t₀车辆A的状态为在终了时刻t_f车辆A的状态为并且所设计轨迹为(x_d(t),y_d(t))。然后，根据车辆运动学模型(1)，施加在所设计轨迹上的等式约束条件如下：Suppose the state of vehicle A at the initial time _t0 is The state of vehicle A at the final moment t _f is And the designed trajectory is (x _d (t), y _d (t)). Then, according to the vehicle kinematics model (1), the equation constraints imposed on the designed trajectory are as follows:

$\{\begin{matrix} {x x}_{d d} (({t t}_{00})) = = {x x}_{00} \\ {\overset{\cdot &Center Dot;}{x x}}_{d d} (({t t}_{00})) = = {v v}_{00} {cosθ cosθ}_{00} \\ {\overset{\cdot\cdot \cdot\cdot}{x x}}_{d d} (({t t}_{00})) = = {\overset{\cdot \cdot}{v v}}_{00} {cosθ cosθ}_{00} - - {v v}_{00}^{22} {tanδ tanδ}_{00} {sinθ sinθ}_{00} / / l l \\ {x x}_{d d} (({t t}_{f f})) = = {x x}_{f f} \\ {\overset{\cdot \cdot}{x x}}_{d d} (({t t}_{f f})) = = {v v}_{f f} {cosθ cosθ}_{f f} \\ {\overset{\cdot\cdot \cdot\cdot}{x x}}_{d d} (({t t}_{f f})) = = {\overset{\cdot &Center Dot;}{v v}}_{f f} {cosθ cosθ}_{f f} - - {v v}_{f f}^{22} {tanδ tanδ}_{f f} {sinθ sinθ}_{f f} / / l l \\ {y the y}_{d d} (({t t}_{00})) = = {y the y}_{00} \\ {\overset{\cdot &Center Dot;}{y the y}}_{d d} (({t t}_{00})) = = {v v}_{00} {sinθ sinθ}_{00} \\ {\overset{\cdot\cdot \cdot\cdot}{x x}}_{d d} (({t t}_{00})) = = {\overset{\cdot &Center Dot;}{v v}}_{00} {sinθ sinθ}_{00} + + {v v}_{00}^{22} {tanδ tanδ}_{00} {cosθ cosθ}_{00} / / l l \\ {y the y}_{d d} (({t t}_{f f})) = = {y the y}_{f f} \\ {\overset{\cdot &Center Dot;}{y the y}}_{d d} (({t t}_{f f})) = = {v v}_{f f} {sinθ sinθ}_{f f} \\ {\overset{\cdot\cdot \cdot\cdot}{x x}}_{d d} (({t t}_{f f})) = = {\overset{\cdot \cdot}{v v}}_{f f} {sinθ sinθ}_{f f} + + {v v}_{f f}^{22} {tanδ tanδ}_{f f} {cosθ cosθ}_{f f} / / l l \end{matrix} - - - - - - ((33))$

将轨迹方程(2)代入到等式约束条件(3)中，并将其化为矩阵形式，其系数可以由以下方程来确定：Substituting the trajectory equation (2) into the equality constraint (3) and transforming it into a matrix form, its coefficients can be determined by the following equations:

$\{\begin{matrix} \overset{&RightArrow; &Right Arrow;}{x x} = = {L L}^{- - 11} (({H h}_{11} - - {Mx Mx}_{d d 66} - - {Nx nx}_{d d 77})) \\ \overset{&RightArrow; &Right Arrow;}{y the y} = = {L L}^{- - 11} (({H h}_{22} - - {My My}_{d d 66} - - {Ny Ny}_{d d 77})) \end{matrix} - - - - - - ((44))$

其中， $\{\begin{matrix} \overset{&RightArrow;}{x} = {[x_{d 0}, x_{d 1}, x_{d 2}, x_{d 3}, x_{d 4}, x_{d 5}]}^{T} \\ \overset{&RightArrow;}{y} = {[y_{d 0}, y_{d 1}, y_{d 2}, y_{d 3}, y_{d 4}, y_{d 5}]}^{T} \\ H_{1} = {[x_{d} (t_{0}), {\overset{\cdot}{x}}_{d} (t_{0}), {\overset{\cdot\cdot}{x}}_{d} (t_{0}), x_{d} (t_{f}), {\overset{\cdot}{x}}_{d} (t_{f}), {\overset{\cdot\cdot}{x}}_{d} (t_{f})]}^{T} \\ H_{2} = {[y_{d} (t_{0}), {\overset{\cdot}{y}}_{d} (t_{0}), {\overset{\cdot\cdot}{y}}_{d} (t_{0}), y_{d} (t_{f}), {\overset{\cdot}{y}}_{d} (t_{f}), {\overset{\cdot\cdot}{y}}_{d} (t_{f})]}^{T} \\ M = {[t_{0}^{6}, 6 t_{0}^{5}, 30 t_{0}^{4}, t_{f}^{6}, 6 t_{f}^{5}, 30 t_{f}^{4}]}^{T} \\ N = {[t_{0}^{7}, 7 t_{0}^{6}, 42 t_{0}^{5}, t_{f}^{7}, 7 t_{f}^{6}, 42 t_{f}^{5}]}^{T} \end{matrix};$ in, $\{\begin{matrix} \overset{&Right Arrow;}{x} = {[x_{d 0}, x_{d 1}, x_{d 2}, x_{d 3}, x_{d 4}, x_{d 5}]}^{T} \\ \overset{&Right Arrow;}{the y} = {[{the y}_{d 0}, {the y}_{d 1}, {the y}_{d 2}, {the y}_{d 3}, {the y}_{d 4}, {the y}_{d 5}]}^{T} \\ h_{1} = {[x_{d} (t_{0}), {\overset{\cdot}{x}}_{d} (t_{0}), {\overset{\cdot\cdot}{x}}_{d} (t_{0}), x_{d} (t_{f}), {\overset{&Center Dot;}{x}}_{d} (t_{f}), {\overset{\cdot\cdot}{x}}_{d} (t_{f})]}^{T} \\ h_{2} = {[{the y}_{d} (t_{0}), {\overset{\cdot}{the y}}_{d} (t_{0}), {\overset{\cdot\cdot}{the y}}_{d} (t_{0}), {the y}_{d} (t_{f}), {\overset{\cdot}{the y}}_{d} (t_{f}), {\overset{\cdot\cdot}{the y}}_{d} (t_{f})]}^{T} \\ m = {[t_{0}^{6}, 6 t_{0}^{5}, 30 t_{0}^{4}, t_{f}^{6}, 6 t_{f}^{5}, 30 t_{f}^{4}]}^{T} \\ N = {[t_{0}^{7}, 7 t_{0}^{6}, 42 t_{0}^{5}, t_{f}^{7}, 7 t_{f}^{6}, 42 t_{f}^{5}]}^{T} \end{matrix};$

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

将方程(4)代入到轨迹方程(2)可以得到轨迹方程的进一步表达式：Substituting equation (4) into trajectory equation (2) can obtain a further expression of trajectory equation:

$\{\begin{matrix} {x x}_{d d} ((t t)) = = {PL PL}^{- - 11} (({H h}_{11} - - {Mx Mx}_{d d 66} - - {Nx nx}_{d d 77})) + + {x x}_{d d 66} {t t}^{66} + + {x x}_{d d 77} {t t}^{77} \\ {y the y}_{d d} ((t t)) = = {PL PL}^{- - 11} (({H h}_{22} - - {My My}_{d d 66} - - {Ny Ny}_{d d 77})) + + {y the y}_{d d 66} {t t}^{66} + + {y the y}_{d d 77} {t t}^{77} \end{matrix} - - - - - - ((55))$

其中P＝[1tt²t³t⁴t⁵]。where P=[1tt ² t ³ t ⁴ t ⁵ ].

为了实现避碰的要求，还需要满足一些不等式约束条件：In order to achieve the requirements of collision avoidance, some inequality constraints need to be satisfied:

(R₀+R₁)²≤[x_d(t)-x₀-v_x(t-t₀)]²+[y_d(t)-y₀-v_y(t-t₀)]² (R ₀ +R ₁ ) ² ≤[x _d (t)-x ₀ -v _x (tt ₀ )] ² +[y _d (t)-y ₀ -v _y (tt ₀ )] ²

(6)(6)

其中，R₀为汽车长度的一半，R₁为与障碍物宽度的一半；Among them, R ₀ is half of the length of the car, and R ₁ is half of the width of the obstacle;

将方程(5)代入到(6)中可以得到进一步的表达式：Substituting equation (5) into (6) yields a further expression:

+[PL^-1(H₂-My_d6-Ny_d7)+y_d6t⁶+y_d7t⁷-y₀-v_y(t-t₀)]²(7)+[PL ^-1 (H ₂ -My _d6 -Ny _d7 )+y _d6 t ⁶ +y _d7 t ⁷ -y ₀ -v _y (tt ₀ )] ² (7)

2)目标函数，一般来说，在智能汽车主动避撞的过程中，所规划轨迹必须满足一些条件，例如，在保证车辆稳定性的同时有效地避开障碍物，基于这样的考虑，选择以下函数作为优化的目标函数：2) The objective function, generally speaking, in the process of active collision avoidance of smart cars, the planned trajectory must meet some conditions, for example, to effectively avoid obstacles while ensuring the stability of the vehicle, based on this consideration, choose the following function as the objective function for optimization:

$J J (({x x}_{d d},, {y the y}_{d d})) = = {&Integral; &Integral;}_{{t t}_{00}}^{{t t}_{f f}} {{{w w}_{I I} [[{(({x x}_{d d} - - {x x}^{' '}))}^{22} + + {(({y the y}_{d d} - - {y the y}^{' '}))}^{22}]] + + {w w}_{22} {a a}_{y the y}^{22}}} d d t t - - - - - - ((88))$

其中，w₁和w₂是权重系数，且w₁+w₂＝1；a_y是汽车侧向加速度；；Among them, w ₁ and w ₂ are weight coefficients, and w ₁ +w ₂ =1; a _y is the lateral acceleration of the vehicle;

3)优化变量，从方程(5)中很容易看到，待优化的变量为：x_d6、x_d7、y_d6、y_d7。3) Optimizing variables, it is easy to see from equation (5), the variables to be optimized are: x _d6 , x _d7 , y _d6 , y _d7 .

步骤7、基于动态粒子群优化算法，对所建立的轨迹优化模型进行求解，以得到所需轨迹：Step 7. Based on the dynamic particle swarm optimization algorithm, solve the established trajectory optimization model to obtain the required trajectory:

粒子群优化算法，又称微粒群算法，其基于粒子构成的群体，针对每个优化问题的解都是寻找其可行空间中的一个粒子。为了让粒子能在全局范围内搜索并保持粒子的多样性，本实用新型采用动态粒子群算法(dynamicParticleSwarmOptimization,DPSO)对轨迹优化模型进行优化。Particle swarm optimization algorithm, also known as particle swarm optimization algorithm, is based on a group of particles, and the solution to each optimization problem is to find a particle in its feasible space. In order to allow particles to search globally and maintain particle diversity, the utility model uses dynamic particle swarm optimization (dynamic ParticleSwarm Optimization, DPSO) to optimize the trajectory optimization model.

如果粒子群的D维空间位置向量为x_i＝(x_i1,x_i2,...,x_iD)，每个x_i表示解空间中一个潜在的可行解,可根据目标函数计算出的适应值来判断其是否为最优解。第i个粒子的D维空间速度向量为v_i＝(v_i1,v_i2,...,v_iD)，第i个粒子个体最优位置P_i＝(P_i1,P_i2,...,P_iD)，粒子群群体最优位置L_i＝(L_i1,L_i2,...,L_iD)，粒子群群体全局最优位置G＝(G₁,G₂,...,G_D)迭代公式如下：If the D-dimensional space position vector of the particle swarm is x _i =(x _i1 , x _i2 ,..., x _iD ), each x _i represents a potential feasible solution in the solution space, which can be calculated according to the objective function value to judge whether it is the optimal solution. The D-dimensional space velocity vector of the i-th particle is v _i =(v _i1 ,v _i2 ,...,v _iD ), the individual optimal position of the i-th particle P _i =(P _i1 ,P _i2 ,... ,P _iD ), the optimal position of particle swarm swarm L _i =(L _i1 ,L _i2 ,...,L _iD ), the global optimal position of particle swarm swarm G=(G ₁ ,G ₂ ,...,G _D ) The iteration formula is as follows:

v_i(t+1)＝ωv_it+b₁r₁(p_i(t)-x_i(t))+b₂r₂(L_i(t)-x_i(t))+b₂r₃(G(t)-x_i(t))(9)v _i (t+1)＝ωv _i t+b ₁ r ₁ (p _i (t)-x _i (t))+b ₂ r ₂ (L _i (t)-x _i (t))+b ₂ r ₃ (G(t) _-xi (t))(9)

式中：b₁、b₂、b₃为正常数；r₁、r₂、r₃为[0,1]内的随机数；参数ω为惯性因子。In the formula: b ₁ , b ₂ , b ₃ are normal numbers; r ₁ , r ₂ , r ₃ are random numbers in [0,1]; parameter ω is the inertia factor.

设ω依据循环次数从ω_s线性递减至ω_e，最大循环次数为I_max，循环的当前次数为I_c，则ω的值可以通过下式得出：Assuming that ω decreases linearly from ω _s to ω _e according to the number of cycles, the maximum number of cycles is I _max , and the current number of cycles is I _c , then the value of ω can be obtained by the following formula:

$ω ω = = {ω ω}_{s the s} - - (({ω ω}_{s the s} - - {ω ω}_{e e})) \frac{{I I}_{c c}}{{I I}_{m m a a x x}} - - - - - - ((1010))$

式中：ω_s为优化最初的惯性因子；ω_e为优化结束的惯性因子。In the formula: ω _s is the inertia factor at the beginning of optimization; ω _e is the inertia factor at the end of optimization.

粒子群中粒子在t+1时刻的位置通过下式求出：The position of the particle in the particle swarm at time t+1 is obtained by the following formula:

x_i(t+1)＝x_i(t)+v_i(t+1)(11)x _i (t+1) = x _i (t) + v _i (t+1) (11)

如果粒子群更新为之后超越了定义域界限，则需要重新调整粒子的位置，使其落在决策空间内，新位置可按照下式计算：If the particle swarm is updated beyond the boundary of the domain of definition, the position of the particle needs to be readjusted so that it falls within the decision space, and the new position can be calculated according to the following formula:

x_i(t+1)＝x_i(t)+λv_i(t+1)(12)x _i (t+1)= _xi (t)+λv _i (t+1)(12)

λ＝2/(γ²+2)(13)λ=2/(γ ² +2)(13)

式中：λ为速度调整系数，其介于(0,1)之间；γ为调整次数，当γ大于3时，粒子速度变为反向。In the formula: λ is the speed adjustment coefficient, which is between (0,1); γ is the number of adjustments, and when γ is greater than 3, the particle speed becomes reversed.

粒子i和k之间的距离||x_i-x_k||可以通过下式求得：The distance between particles i and k ||x _i -x _k || can be obtained by the following formula:

$| | | | {x x}_{i i} - - {x x}_{k k} | | | | = = \sqrt{(({Σ Σ}_{i i = = 11}^{D D.} {(({x x}_{i i L L} - - {x x}_{k k L L}))}^{22})) / / d d} - - - - - - ((1414))$

式中：d为决策变量的维数。In the formula: d is the dimension of the decision variable.

动态粒子群的生成：如果已生成m个粒子群，假设与粒子群a最近的粒子群为b，如果他们之间的距离大于D_max，则需要生成一个粒子群x_m+1，群内第i个粒子第k维分量可以由下式求得：Generation of dynamic particle swarms: If m particle swarms have been generated, assuming that the nearest particle swarm to particle swarm a is b, if the distance between them is greater than D _max , then a particle swarm x _m+1 needs to be generated, the first in the group The k-th dimension component of the i particle It can be obtained by the following formula:

${x x}_{m m + + 11}^{i i k k} = = (({x x}_{a a}^{i i k k} + + {x x}_{b b}^{i i k k})) / / 22 + + {c c}_{11} {((- - 11))}^{r r o o u u n no d d ((0.5 0.5 + + {c c}_{22}))} | | {x x}_{a a}^{i i k k} - - {x x}_{b b}^{i i k k} | | / / 22 - - - - - - ((1515))$

式中：C₁、C₂为[0,1]内的随机数；round(·)为取整函数，因此，round(0.5+C₂)为0或1。In the formula: C ₁ and C ₂ are random numbers within [0,1]; round(·) is a rounding function, therefore, round(0.5+C ₂ ) is 0 or 1.

步骤8、ECU将所生成轨迹的相关参数输出到油门控制器、转向控制器、制动控制器，并执行相应操作，以精确跟踪所生成轨迹。Step 8. The ECU outputs relevant parameters of the generated trajectory to the accelerator controller, steering controller, and brake controller, and performs corresponding operations to accurately track the generated trajectory.

步骤9、跳转到步骤4，进行下一时刻的轨迹求解与跟踪，如此循环，直至规划结束，完成整个避撞过程，如图2所示。Step 9. Jump to step 4 to solve and track the trajectory at the next moment. This cycle continues until the planning is completed and the entire collision avoidance process is completed, as shown in Figure 2.

本技术领域技术人员可以理解的是，除非另外定义，这里使用的所有术语(包括技术术语和科学术语)具有与本实用新型所属领域中的普通技术人员的一般理解相同的意义。还应该理解的是，诸如通用字典中定义的那些术语应该被理解为具有与现有技术的上下文中的意义一致的意义，并且除非像这里一样定义，不会用理想化或过于正式的含义来解释。Those skilled in the art can understand that, unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meanings as commonly understood by those of ordinary skill in the art to which the present invention belongs. It should also be understood that terms such as those defined in commonly used dictionaries should be understood to have a meaning consistent with the meaning in the context of the prior art, and unless defined as herein, are not to be interpreted in an idealized or overly formal sense Explanation.

以上所述的具体实施方式，对本实用新型的目的、技术方案和有益效果进行了进一步详细说明，所应理解的是，以上所述仅为本实用新型的具体实施方式而已，并不用于限制本实用新型，凡在本实用新型的精神和原则之内，所做的任何修改、等同替换、改进等，均应包含在本实用新型的保护范围之内。The specific embodiments described above have further described the purpose, technical solutions and beneficial effects of the present utility model in detail. It should be understood that the above descriptions are only specific embodiments of the present utility model and are not intended to limit the present invention. For the utility model, any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the utility model shall be included in the protection scope of the utility model.

Claims

1. An automobile active collision avoidance system is characterized by comprising a forward looking radar, a camera, a signal processing module, a vehicle speed sensor, a yaw angular velocity sensor, a mass center side deviation angle sensor, a steering wheel corner sensor, an electronic control power supply ECU, an accelerator controller, a steering controller and a brake controller;

the forward looking radar and the camera are connected with the electronic control power supply ECU through a signal processing module; the electronic control power supply ECU is respectively connected with a vehicle speed sensor, a yaw rate sensor, a mass center slip angle sensor, a steering wheel corner sensor, an accelerator controller, a steering controller and a brake controller;

the forward looking radar and the camera are both arranged in front of the automobile and used for detecting the road condition in front of the automobile, and transmitting the detected signals to the electronic control unit ECU after being processed by the signal processing module;

the vehicle speed sensor, the yaw rate sensor, the mass center slip angle sensor and the steering wheel angle sensor are respectively used for sensing the speed, the yaw rate, the mass center slip angle and the front wheel steering angle of the vehicle, and collected signals are processed and then sent to the electronic control unit ECU;

and the electronic control unit ECU is used for outputting corresponding signals to the accelerator controller, the steering controller and the brake controller according to the received signals, and carrying out corresponding acceleration, deceleration and braking operations so as to ensure the driving safety.