WO2020143288A1 - Autonomous vehicle decision-making system under complex operating conditions, and trajectory planning method therefor - Google Patents

Autonomous vehicle decision-making system under complex operating conditions, and trajectory planning method therefor Download PDF

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WO2020143288A1
WO2020143288A1 PCT/CN2019/115628 CN2019115628W WO2020143288A1 WO 2020143288 A1 WO2020143288 A1 WO 2020143288A1 CN 2019115628 W CN2019115628 W CN 2019115628W WO 2020143288 A1 WO2020143288 A1 WO 2020143288A1
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vehicle
trajectory
decision
speed
unit
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PCT/CN2019/115628
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French (fr)
Chinese (zh)
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赵万忠
徐灿
王春燕
陈青云
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南京航空航天大学
南京航空航天大学秦淮创新研究院
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/0088—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
    • G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0255—Control of position or course in two dimensions specially adapted to land vehicles using acoustic signals, e.g. ultra-sonic singals
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0257—Control of position or course in two dimensions specially adapted to land vehicles using a radar
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
    • G05D1/0278—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle using satellite positioning signals, e.g. GPS

Definitions

  • the invention belongs to the technical field of vehicle automatic driving, and in particular relates to an automatic driving vehicle decision system and a trajectory planning method under complex working conditions.
  • the system is the brain of autonomous driving. It mainly plans a safe trajectory for autonomous vehicles based on traffic information sensed by vehicle sensors, such as surrounding vehicle speed, yaw rate, lane line and road boundary information. After the corresponding trajectory is obtained, the corresponding control quantity is obtained through the vehicle dynamics model and passed to the lower-level execution system, so as to control the steering wheel, brakes and throttle to realize the automatic driving of the vehicle.
  • the current research on vehicle decision-making systems is mainly for decision-making under static conditions, that is, traffic conditions determined by the movement of surrounding vehicles. For actual complex traffic, the future movement of surrounding vehicles may change and needs to be predicted. It’s not the same as following the car or changing lanes. In this situation, the decision-making system needs to ensure that the vehicle avoids the surrounding obstacles in real time and improve the driving safety of the vehicle.
  • the trajectory planning technology is a key technology for the decision-making system of autonomous vehicles.
  • the current research mainly focuses on the single planning of speed or path, such as planning only the speed when following the car, and keeping the speed unchanged when changing lanes. path.
  • speed or path In order to realize automatic driving in the true sense, especially in the face of overtaking conditions, it is necessary to plan the speed and path at the same time when planning the trajectory. Therefore, how to plan a collision-free trajectory by controlling the speed and movement direction of vehicles under actual complicated traffic conditions is very important.
  • the object of the present invention is to provide an automatic driving vehicle decision-making system and its trajectory planning method under complex working conditions, in order to solve the existing technology in the multi-lane, high-speed scenes and other complex working conditions
  • a safe and stable trajectory can be planned in real time through the technical solution of the present invention to realize automatic driving of vehicles.
  • the invention discloses a decision-making system for autonomous driving vehicles under complex working conditions, including: an environment perception unit, a real-time decision unit, a trajectory planning unit and a control unit; wherein,
  • Environment awareness unit used to sense the motion information of autonomous vehicles and surrounding vehicles in real time
  • Real-time decision-making unit including the on-board computer on the autonomous driving vehicle and the CAN communication module connected to it; the CAN communication module is connected to the above-mentioned environment sensing unit and receives the above-mentioned motion information; the on-board computer decides the automatic driving vehicle in the current state according to the motion information Best driving behavior;
  • the trajectory planning unit is connected to the above-mentioned real-time decision-making unit, which performs double planning on the path and speed of the vehicle according to the optimal driving behavior of the autonomous vehicle decided by the real-time decision-making unit to obtain the optimal trajectory;
  • the control unit includes the electronic control unit and the active steering motor, brake and accelerator pedal displacement motor electrically connected thereto; the electronic control unit generates the active steering motor, brake and throttle according to the optimal trajectory planned by the above trajectory planning unit The control command of the pedal displacement motor, which in turn controls the driving state of the vehicle.
  • the environment sensing unit includes a GPS, a laser radar, a camera, an ultrasonic radar, and a yaw rate sensor provided on the autonomous driving vehicle.
  • the motion information includes position information, speed information, and yaw angle information.
  • trajectory planning unit considers the safety, efficiency, and stability constraints of the vehicle when planning.
  • the risk constraint That is to ensure that the hazard degree R corresponding to the optimal trajectory is less than the hazard level acceptable to passengers
  • the three-dimensional decision field can be constrained into a two-dimensional plane; the specific solution is as follows:
  • ⁇ is the weighting factor
  • s t represents the state parameters corresponding to the given trajectory at time t, specifically including the horizontal and vertical coordinates, speed, and yaw angle information
  • p t represents the state parameters corresponding to the surrounding vehicles at time t, specifically including the horizontal and vertical Coordinate, speed, yaw angle information
  • F is the risk assessment function
  • Efficiency constraints ensures that the vehicle travels along the optimal speed provided by the traffic road. According to this efficiency constraint, the above two-dimensional plane can be constrained into a curve; where v final is the corresponding speed after a given trajectory for several cycles, The best speed of the corresponding vehicle under current traffic conditions;
  • Stability constraint ensures that the vehicle changes lanes less, thereby improving riding comfort; according to this constraint, the above curve can be constrained to an optimal decision point, and the autonomous driving vehicle can be determined by the decision point The optimal driving trajectory at the next moment; where ⁇ y is the longitudinal deviation of the determined trajectory from the current road.
  • the invention also discloses a trajectory planning method of the automatic driving vehicle decision system under complex working conditions. Based on the above system, it includes the following steps:
  • each target position point represents a trajectory, that is, the traversal trajectory
  • step 1) specifically includes the following steps:
  • the path corresponding to the future traversal trajectory of the vehicle is fitted using a polynomial, using 4 constraints, and the third-order polynomial is used for fitting, as follows:
  • a 0 , a 1 , a 2 , a 3 are the fitting parameters corresponding to the polynomial path, Is the yaw angle corresponding to the vehicle at the current moment, (x k , y k ) is the position coordinate corresponding to the current state of the automatic driving vehicle, (x p , y p ) is the target point reached by the automatic driving vehicle after a given number of cycles ;
  • T is the time corresponding to each cycle
  • v k is the current speed of the vehicle
  • a is the acceleration corresponding to the process
  • v k is the speed corresponding to the vehicle at the current moment
  • v t is the speed corresponding to the vehicle after t cycles
  • the optimal trajectory search method in step 2) specifically includes the following steps:
  • S r is the road safety factor
  • D sf is the standard safety distance
  • b is the limiting factor
  • Des is the actual distance between the vehicle and the surrounding vehicle
  • t is the time when the surrounding vehicle reaches the front of the vehicle
  • t b is the braking time
  • the constraint of the three-dimensional hazard field in step 22) specifically includes:
  • ⁇ is the weighting factor
  • s t represents the state parameters corresponding to the given trajectory at time t, specifically including the horizontal and vertical coordinates, speed, and yaw angle information
  • p t represents the state parameters corresponding to the surrounding vehicles at time t, specifically including the horizontal and vertical Coordinate, speed, yaw angle information
  • F is the risk assessment function
  • Efficiency constraints Constrain the above two-dimensional plane to a curve; where v final is the corresponding velocity after a certain period of a given trajectory, The best speed of the corresponding vehicle under current traffic conditions;
  • the invention can realize the automatic driving of vehicles under the complicated traffic conditions of multiple lanes, and can plan a safe and collision-free trajectory in real time.
  • the decision planning system designed by the present invention can fully consider the kinematic and dynamic constraints of the vehicle's actuator, the generated trajectory has continuity, and can be applied to the real-time changing road environment.
  • the trajectory planning of the vehicle by the present invention is a dual planning of path and speed, that is, planning the path of the vehicle while planning the speed of the vehicle at each point on the path.
  • Figure 1 is an overall block diagram of the decision-making system of the present invention.
  • FIG. 2 is a schematic diagram corresponding to an optimization search method for a given target position point.
  • a decision-making system for an autonomous driving vehicle under complex working conditions of the present invention is characterized by comprising: an environment awareness unit, a real-time decision unit, a trajectory planning unit and a control unit; wherein,
  • Environment awareness unit including GPS, lidar, camera, ultrasonic radar, and yaw rate sensor installed on the self-driving vehicle; used to sense the motion information of the self-driving vehicle and surrounding vehicles in real time; the motion information includes: location information, Speed information and yaw angle information.
  • Real-time decision-making unit including the on-board computer on the autonomous driving vehicle and the CAN communication module connected to it; the CAN communication module is connected to the above-mentioned environment sensing unit and receives the above-mentioned motion information; the on-board computer decides the automatic driving vehicle in the current state according to the motion information Best driving behavior;
  • the trajectory planning unit is connected to the above-mentioned real-time decision-making unit, which double-plans the path and speed of the vehicle according to the best driving behavior of the self-driving vehicle decided by the real-time decision-making unit, and obtains the optimal trajectory; the safety of the vehicle is considered when planning Sexual constraints, high efficiency constraints, stability constraints.
  • ⁇ is the weighting factor
  • s t represents the state parameters corresponding to the given trajectory at time t, specifically including the horizontal and vertical coordinates, speed, and yaw angle information
  • p t represents the state parameters corresponding to the surrounding vehicles at time t, specifically including the horizontal and vertical Coordinate, speed, yaw angle information
  • F is the risk assessment function
  • the control unit includes an electronic control unit (ECU) and an active steering motor, a brake and an accelerator pedal displacement motor electrically connected thereto; the electronic control unit generates an active steering motor, a control system according to the optimal trajectory planned by the above trajectory planning unit Control commands for moving and accelerator pedal displacement motors, which in turn control the vehicle's driving status.
  • ECU electronice control unit
  • active steering motor a brake and an accelerator pedal displacement motor electrically connected thereto
  • the electronic control unit generates an active steering motor, a control system according to the optimal trajectory planned by the above trajectory planning unit Control commands for moving and accelerator pedal displacement motors, which in turn control the vehicle's driving status.
  • the environment awareness unit When working, the environment awareness unit first uses various sensors to obtain the motion information around the autonomous vehicle, and transmits this information to the on-board computer for real-time decision-making.
  • the trajectory contains not only the path information of the self-driving vehicle, but also the speed information, so it is well adapted to the decision planning under the actual complex working conditions.
  • the vehicle's control unit After obtaining the trajectory information, the vehicle's control unit will use the corresponding control algorithm to convert the trajectory information into the control quantity information directly input by the controller, and input these control quantities to the active steering motor, brake, and accelerator pedal displacement motor, thereby Realize the automatic driving of vehicles.
  • the invention also discloses a trajectory planning method of the automatic driving vehicle decision system under complex working conditions. Based on the above system, it includes the following steps:
  • each target position point represents a trajectory, that is, the traversal trajectory
  • the method for obtaining the traversing trajectory in step 1) specifically includes the following steps:
  • the path corresponding to the future traversal trajectory of the vehicle is fitted using a polynomial, and the third-order polynomial is used to fit using 4 constraints, as follows:
  • a 0 , a 1 , a 2 , a 3 are the fitting parameters corresponding to the polynomial path, Is the yaw angle corresponding to the vehicle at the current moment, (x k ,y k ) is the position coordinate corresponding to the current state of the automatic driving vehicle, (x p ,y p ) is the target point reached by the automatic driving vehicle after a given number of cycles ;
  • T is the time corresponding to each cycle
  • v k is the current speed of the vehicle
  • a is the acceleration corresponding to the process
  • v k is the speed corresponding to the vehicle at the current moment
  • v t is the speed corresponding to the vehicle after t cycles
  • the optimal trajectory search method in step 2) specifically includes the following steps:
  • S r is the road safety factor
  • D sf is the standard safety distance
  • b is the limiting factor
  • Des is the actual distance between the vehicle and the surrounding vehicle
  • t is the time when the surrounding vehicle reaches the front of the vehicle
  • t b is the braking time
  • step 22) restricting the three-dimensional hazard field specifically includes:
  • ⁇ is the weighting factor
  • s t represents the state parameter corresponding to the given trajectory at time t, specifically including the horizontal and vertical coordinates, speed, and yaw angle information
  • p t represents the state parameter corresponding to the surrounding vehicles at time t, specifically including the horizontal and vertical coordinates , Speed, yaw angle information
  • F is the risk assessment function
  • Efficiency constraints ensures that the vehicle can travel along the optimal speed provided by the traffic road. According to the efficiency constraint, the above two-dimensional plane is constrained into a curve; where, v final is the corresponding speed after a certain period of the given trajectory, The best speed of the corresponding vehicle under current traffic conditions;
  • the tracking method based on vehicle dynamics used in step 3 includes the following steps:
  • Is the yaw rate, Y and X are the vertical and horizontal coordinates in the earth coordinate system;
  • T is the sampling time of each discretization period
  • I is the identity matrix
  • f is the dynamic equation
  • (u 0 , ⁇ 0 ) is the reference state point.
  • ⁇ is the predicted actual state quantity
  • ⁇ ref is the reference state quantity obtained from the planned trajectory
  • ⁇ U is the control quantity increment
  • the first term of the objective function is the deviation of the predicted trajectory from the reference trajectory, reflecting the trajectory followability
  • the second term of the objective function is the deviation of the control quantity, which reflects stability.
  • Control amount constraint where the control amount is defined as follows:
  • the optimal tracking control quantity sequence of the autonomous vehicle can be solved at every moment, so as to ensure that the vehicle travels along the safest trajectory.

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Abstract

An autonomous vehicle decision-making system under complex operating conditions, and a trajectory planning method therefor. The system mainly comprises an environment sensing unit, a real-time decision-making unit, a trajectory planning unit and a control unit. During a vehicle travelling process, the environment sensing unit transmits sensed motion information of surrounding vehicles to the real-time decision-making unit used to make a decision regarding the best driving behavior of an autonomous vehicle in the current state. After the decision regarding the driving behavior is made, the trajectory planning unit then carries out double planning on a path and the speed of the vehicle by means of a set trajectory planning method to obtain the optimal trajectory in the current state, and inputs, by means of an ECU, a corresponding control signal to an execution mechanism so as to ensure safe and reliable travel of the vehicle. A safe collision-free trajectory is planned for a vehicle in real time under complex operating conditions, namely a plurality of lanes and a plurality of obstacles, of the vehicle, thereby realizing autonomous driving of the vehicle.

Description

一种复杂工况下自动驾驶车辆决策***及其轨迹规划方法Autonomous driving vehicle decision system and trajectory planning method under complex working conditions 技术领域Technical field
本发明属于车辆自动驾驶技术领域，尤其涉及一种复杂工况下自动驾驶车辆决策***及其轨迹规划方法。The invention belongs to the technical field of vehicle automatic driving, and in particular relates to an automatic driving vehicle decision system and a trajectory planning method under complex working conditions.
背景技术Background technique
随着传感器精度的提高，芯片技术的发展、5G通信的到来，对于车辆自动驾驶的研究越来越多，其研究的目的主要在于减少交通事故，缓解交通堵塞，并减轻驾驶员的驾驶负担。目前很多国际化公司，如谷歌，通用，特斯拉都投入了大量精力来研究高水平的自动驾驶车辆。With the improvement of sensor accuracy, the development of chip technology and the advent of 5G communication, there are more and more studies on vehicle automatic driving. The purpose of its research is mainly to reduce traffic accidents, alleviate traffic jams, and reduce the driving burden of drivers. At present, many international companies, such as Google, GM, and Tesla have invested a lot of energy to study high-level autonomous vehicles.
作为车辆自动驾驶技术的核心部分，决策***实时决策出一条能避开周围障碍物的安全可靠的轨迹显得尤其重要。该***是自动驾驶的大脑，主要根据车辆传感器感知到的交通信息，如周围车辆速度，横摆角速度，车道线及道路边界信息，来给自动驾驶车辆规划出一条安全的轨迹。得到相应的轨迹后，通过车辆的动力学模型得到相应的控制量并传给下层执行***，从而控制方向盘、刹车和油门来实现车辆的自动驾驶。目前对于车辆决策***的研究，主要是对静止工况下即周围车辆运动确定的交通工况下的决策规划，而对于实际复杂交通中，周围车辆的未来运动可能发生变化需要进行预测，面对的也不是规则的跟车、换道工况。在该情境下，决策***需要保证车辆实时避开周围的障碍物，提高车辆的行车安全性。As the core part of vehicle autonomous driving technology, it is particularly important for the decision-making system to decide a safe and reliable trajectory that can avoid surrounding obstacles in real time. The system is the brain of autonomous driving. It mainly plans a safe trajectory for autonomous vehicles based on traffic information sensed by vehicle sensors, such as surrounding vehicle speed, yaw rate, lane line and road boundary information. After the corresponding trajectory is obtained, the corresponding control quantity is obtained through the vehicle dynamics model and passed to the lower-level execution system, so as to control the steering wheel, brakes and throttle to realize the automatic driving of the vehicle. The current research on vehicle decision-making systems is mainly for decision-making under static conditions, that is, traffic conditions determined by the movement of surrounding vehicles. For actual complex traffic, the future movement of surrounding vehicles may change and needs to be predicted. It’s not the same as following the car or changing lanes. In this situation, the decision-making system needs to ensure that the vehicle avoids the surrounding obstacles in real time and improve the driving safety of the vehicle.
此外，轨迹规划技术作为自动驾驶车辆决策***的关键技术，目前的研究主要停留在对速度或者对路径的单一规划，如跟车时只对速度进行规划，换道时则保持速度不变只规划路径。而要实现真正意义上的自动驾驶，尤其面对超车工况时，在轨迹规划时需要对速度及路径进行同时规划。因此，如何在实际复杂交通工况下，通过控制车辆的速度及运动方向规划出一条无碰撞的轨迹显得很重要。In addition, the trajectory planning technology is a key technology for the decision-making system of autonomous vehicles. The current research mainly focuses on the single planning of speed or path, such as planning only the speed when following the car, and keeping the speed unchanged when changing lanes. path. In order to realize automatic driving in the true sense, especially in the face of overtaking conditions, it is necessary to plan the speed and path at the same time when planning the trajectory. Therefore, how to plan a collision-free trajectory by controlling the speed and movement direction of vehicles under actual complicated traffic conditions is very important.
发明内容Summary of the invention
针对于上述现有技术的不足，本发明的目的在于提供一种复杂工况下自动驾驶车辆决策***及其轨迹规划方法，以解决现有技术中在多车道、高速场景等复杂工况下的智能决策及轨迹规划问题，通过本发明的技术方案能实时规划出一条安全稳定的轨迹，实现车辆的自动驾驶。In view of the above-mentioned shortcomings of the prior art, the object of the present invention is to provide an automatic driving vehicle decision-making system and its trajectory planning method under complex working conditions, in order to solve the existing technology in the multi-lane, high-speed scenes and other complex working conditions For intelligent decision-making and trajectory planning, a safe and stable trajectory can be planned in real time through the technical solution of the present invention to realize automatic driving of vehicles.
为达到上述目的，本发明采用的技术方案如下：To achieve the above objectives, the technical solutions adopted by the present invention are as follows:
本发明公开了一种复杂工况下自动驾驶车辆决策***，包括：环境感知单元、实时决策单元、轨迹规划单元及控制单元；其中，The invention discloses a decision-making system for autonomous driving vehicles under complex working conditions, including: an environment perception unit, a real-time decision unit, a trajectory planning unit and a control unit; wherein,
环境感知单元，用于实时感知自动驾驶车辆及周围车辆的运动信息；Environment awareness unit, used to sense the motion information of autonomous vehicles and surrounding vehicles in real time;
实时决策单元，包含自动驾驶车辆上的车载计算机及与之相连的CAN通信模块；CAN通信模块与上述环境感知单元连接，并接收上述运动信息；车载计算机根据运动信息决策出当 前状态下自动驾驶车辆最佳的驾驶行为；Real-time decision-making unit, including the on-board computer on the autonomous driving vehicle and the CAN communication module connected to it; the CAN communication module is connected to the above-mentioned environment sensing unit and receives the above-mentioned motion information; the on-board computer decides the automatic driving vehicle in the current state according to the motion information Best driving behavior;
轨迹规划单元，与上述实时决策单元相连接，其根据实时决策单元决策出的自动驾驶车辆最佳的驾驶行为对车辆的路径及速度进行双规划，得到最优轨迹；The trajectory planning unit is connected to the above-mentioned real-time decision-making unit, which performs double planning on the path and speed of the vehicle according to the optimal driving behavior of the autonomous vehicle decided by the real-time decision-making unit to obtain the optimal trajectory;
控制单元，包含电子控制单元及与之电气连接的主动转向电机、制动及油门踏板位移电机；电子控制单元根据上述轨迹规划单元规划出的最优轨迹，产生对主动转向电机、制动及油门踏板位移电机的控制指令，进而控制车辆行驶状态。The control unit includes the electronic control unit and the active steering motor, brake and accelerator pedal displacement motor electrically connected thereto; the electronic control unit generates the active steering motor, brake and throttle according to the optimal trajectory planned by the above trajectory planning unit The control command of the pedal displacement motor, which in turn controls the driving state of the vehicle.
进一步地，所述环境感知单元包含自动驾驶车辆上设有的GPS、激光雷达、摄像头、超声波雷达及横摆角速度传感器。Further, the environment sensing unit includes a GPS, a laser radar, a camera, an ultrasonic radar, and a yaw rate sensor provided on the autonomous driving vehicle.
进一步地，所述运动信息包含：位置信息、速度信息及横摆角信息。Further, the motion information includes position information, speed information, and yaw angle information.
进一步地，所述轨迹规划单元在规划时考虑车辆的安全性约束、高效性约束、平稳性约束。Further, the trajectory planning unit considers the safety, efficiency, and stability constraints of the vehicle when planning.
进一步地，所述的危险性约束：
Figure PCTCN2019115628-appb-000001
即保证最优轨迹对应的危险度R小于乘客可接受的危险等级
Figure PCTCN2019115628-appb-000002
根据该危险性约束，即可将三维决策场约束成一个二维平面；具体求解如下： Further, the risk constraint:
Figure PCTCN2019115628-appb-000001
That is to ensure that the hazard degree R corresponding to the optimal trajectory is less than the hazard level acceptable to passengers
Figure PCTCN2019115628-appb-000002
According to the risk constraint, the three-dimensional decision field can be constrained into a two-dimensional plane; the specific solution is as follows:
Figure PCTCN2019115628-appb-000003
Figure PCTCN2019115628-appb-000003
式中，γ为权重因子；s t表示t时刻给定轨迹对应的状态参数，具体包括横纵坐标、速度、横摆角信息；p t表示t时刻周围车辆对应的状态参数，具体包括横纵坐标、速度、横摆角信息；F为危险度评估函数； In the formula, γ is the weighting factor; s t represents the state parameters corresponding to the given trajectory at time t, specifically including the horizontal and vertical coordinates, speed, and yaw angle information; p t represents the state parameters corresponding to the surrounding vehicles at time t, specifically including the horizontal and vertical Coordinate, speed, yaw angle information; F is the risk assessment function;
高效性约束：
Figure PCTCN2019115628-appb-000004
该约束保证车辆沿着交通道路提供的最佳车速行驶，根据该高效性约束，即可将上述二维平面约束成一条曲线；其中，v final为给定轨迹若干个周期后对应的速度，
Figure PCTCN2019115628-appb-000005
为当前交通状况下对应的车辆最佳车速； Efficiency constraints:
Figure PCTCN2019115628-appb-000004
This constraint ensures that the vehicle travels along the optimal speed provided by the traffic road. According to this efficiency constraint, the above two-dimensional plane can be constrained into a curve; where v final is the corresponding speed after a given trajectory for several cycles,
Figure PCTCN2019115628-appb-000005
The best speed of the corresponding vehicle under current traffic conditions;
平稳性约束：min Δy，该平稳性约束保证车辆少换道，从而提高乘坐舒适性；根据该约束，即可将上述的曲线约束成最优的决策点，通过该决策点来确定自动驾驶车辆下一时刻的最优行驶轨迹；其中，Δy为决策出的轨迹与当前道路的纵向偏差。Stability constraint: minΔy, the stability constraint ensures that the vehicle changes lanes less, thereby improving riding comfort; according to this constraint, the above curve can be constrained to an optimal decision point, and the autonomous driving vehicle can be determined by the decision point The optimal driving trajectory at the next moment; where Δy is the longitudinal deviation of the determined trajectory from the current road.
本发明还公开了一种复杂工况下自动驾驶车辆决策***的轨迹规划方法，基于上述***，包括以下步骤：The invention also discloses a trajectory planning method of the automatic driving vehicle decision system under complex working conditions. Based on the above system, it includes the following steps:
1)获得遍历轨迹：先给定若干周期后车辆可能到达的目标位置点，每个目标位置点代表一条轨迹，即遍历轨迹；1) Obtain the traversal trajectory: given a target position point that the vehicle may reach after several cycles, each target position point represents a trajectory, that is, the traversal trajectory;
2)根据上述遍历轨迹进行最优化搜索，通过车辆面对的危险性、高效性和稳定性约束来优化出当前时刻车辆最优的一条轨迹。2) Perform an optimization search based on the above traversal trajectory, and optimize the vehicle's optimal trajectory at the current moment through the danger, efficiency and stability constraints faced by the vehicle.
进一步地，所述步骤1)中获得遍历轨迹的方法，具体包括如下步骤：Further, the method for obtaining the traversal trajectory in step 1) specifically includes the following steps:
11)根据车辆当前位置及给定的目标位置点，利用多项式拟合出车辆未来的遍历轨迹对 应的路径，利用4个约束，使用3次多项式进行拟合，具体如下：11) According to the current position of the vehicle and the given target position point, the path corresponding to the future traversal trajectory of the vehicle is fitted using a polynomial, using 4 constraints, and the third-order polynomial is used for fitting, as follows:
y＝a 0+a 1x+a 2x 2+a 3x 3 y=a 0 +a 1 x+a 2 x 2 +a 3 x 3
Figure PCTCN2019115628-appb-000006
Figure PCTCN2019115628-appb-000006
其中，a 0,a 1,a 2,a 3分别为多项式路径对应的拟合参数，
Figure PCTCN2019115628-appb-000007
为当前时刻车辆对应的横摆角，(x k,y k)为当前状态自动驾驶车辆对应的位置坐标，(x p,y p)为给定的若干个周期之后自动驾驶车辆到达的目标点； Among them, a 0 , a 1 , a 2 , a 3 are the fitting parameters corresponding to the polynomial path,
Figure PCTCN2019115628-appb-000007
Is the yaw angle corresponding to the vehicle at the current moment, (x k , y k ) is the position coordinate corresponding to the current state of the automatic driving vehicle, (x p , y p ) is the target point reached by the automatic driving vehicle after a given number of cycles ;
12)利用积分求出上述路径对应的长度，将该过程视为匀加速运动过程，得到遍历轨迹对应的速度；12) Use integral to find the length corresponding to the above path, and regard the process as a uniform acceleration motion process to obtain the speed corresponding to the traversal trajectory;
每条决策路径的长度S：The length S of each decision path:
Figure PCTCN2019115628-appb-000008
Figure PCTCN2019115628-appb-000008
其中，y’为多项式路径的斜率；Where y’ is the slope of the polynomial path;
将该过程视为匀加速运动；从而得到每个决策点对应的加速度：Think of this process as a uniform acceleration motion; thus obtaining the acceleration corresponding to each decision point:
Figure PCTCN2019115628-appb-000009
Figure PCTCN2019115628-appb-000009
其中，T为每个周期对应的时间，v k为车辆当前速度，a为该过程对应的加速度； Where T is the time corresponding to each cycle, v k is the current speed of the vehicle, and a is the acceleration corresponding to the process;
该过程对应的速度表示如下：The speed corresponding to this process is expressed as follows:
v t＝v k+a*(t-k) v t = v k +a*(tk)
其中，v k为当前时刻车辆对应的速度，v t表示t个周期后车辆对应的速度； Where v k is the speed corresponding to the vehicle at the current moment, and v t is the speed corresponding to the vehicle after t cycles;
13)根据步骤11)拟合出的路径和步骤12)得到的在该路径上的速度，即得到车辆遍历轨迹的参数信息(路径+速度)。13) According to the path fitted in step 11) and the speed on the path obtained in step 12), the parameter information (path + speed) of the trajectory of the vehicle is obtained.
进一步的，所述步骤2)中的最优轨迹搜索方法，具体包括如下步骤：Further, the optimal trajectory search method in step 2) specifically includes the following steps:
21)获取每条轨迹对应的危险度，该危险度即可形成一个车辆在未来时刻的三维危险场，其中危险度评估函数F建立如下：21) Obtain the hazard degree corresponding to each trajectory, and the hazard degree can form a three-dimensional hazard field of the vehicle at the future moment, in which the hazard assessment function F is established as follows:
Figure PCTCN2019115628-appb-000010
Figure PCTCN2019115628-appb-000010
其中，S r为道路安全系数；D sf为标准安全距离；b为限幅系数；D es为自车与周围车辆实际距 离，t为周围车辆到达自车前方的时间，t b为刹车时间； Among them, S r is the road safety factor; D sf is the standard safety distance; b is the limiting factor; Des is the actual distance between the vehicle and the surrounding vehicle, t is the time when the surrounding vehicle reaches the front of the vehicle, and t b is the braking time;
22)对上述三维危险场进行约束，得到一条最优轨迹。22) Constrain the above three-dimensional hazard field to obtain an optimal trajectory.
进一步地，所述步骤22)中对三维危险场进行约束具体包括：Further, the constraint of the three-dimensional hazard field in step 22) specifically includes:
221)危险性约束：
Figure PCTCN2019115628-appb-000011
即保证最优轨迹对应的危险度R小于乘客可接受的危险等级
Figure PCTCN2019115628-appb-000012
根据该约束，即可将三维决策场约束成一个二维平面；具体求解如下： 221) Dangerous constraints:
Figure PCTCN2019115628-appb-000011
That is to ensure that the hazard degree R corresponding to the optimal trajectory is less than the hazard level acceptable to passengers
Figure PCTCN2019115628-appb-000012
According to the constraint, the three-dimensional decision field can be constrained into a two-dimensional plane; the specific solution is as follows:
Figure PCTCN2019115628-appb-000013
Figure PCTCN2019115628-appb-000013
式中，γ为权重因子；s t表示t时刻给定轨迹对应的状态参数，具体包括横纵坐标、速度、横摆角信息；p t表示t时刻周围车辆对应的状态参数，具体包括横纵坐标、速度、横摆角信息；F为危险度评估函数； In the formula, γ is the weighting factor; s t represents the state parameters corresponding to the given trajectory at time t, specifically including the horizontal and vertical coordinates, speed, and yaw angle information; p t represents the state parameters corresponding to the surrounding vehicles at time t, specifically including the horizontal and vertical Coordinate, speed, yaw angle information; F is the risk assessment function;
222)高效性约束：
Figure PCTCN2019115628-appb-000014
将上述二维平面约束成一条曲线；其中，v final为给定轨迹若干个周期后对应的速度，
Figure PCTCN2019115628-appb-000015
为当前交通状况下对应的车辆最佳车速； 222) Efficiency constraints:
Figure PCTCN2019115628-appb-000014
Constrain the above two-dimensional plane to a curve; where v final is the corresponding velocity after a certain period of a given trajectory,
Figure PCTCN2019115628-appb-000015
The best speed of the corresponding vehicle under current traffic conditions;
223)平稳性约束：min Δy，将上述的曲线约束成最优的决策点，通过该决策点来确定自动驾驶车辆t+1时刻的最优行驶轨迹；其中，Δy为决策出的轨迹与当前道路的纵向偏差。223) Stationarity constraint: minΔy, constrain the above curve to the optimal decision point, and determine the optimal driving trajectory at time t+1 of the self-driving vehicle through this decision point; where Δy is the determined trajectory and the current The longitudinal deviation of the road.
本发明的有益效果：The beneficial effects of the invention:
1、本发明能实现车辆在多车道复杂交通工况下的自动驾驶，能实时规划出一条安全，无碰撞的轨迹。1. The invention can realize the automatic driving of vehicles under the complicated traffic conditions of multiple lanes, and can plan a safe and collision-free trajectory in real time.
2、本发明所设计的决策规划***能充分考虑车辆执行机构的运动学和动力学约束，所生成的轨迹具有连续性，能适用实时变化的道路环境。2. The decision planning system designed by the present invention can fully consider the kinematic and dynamic constraints of the vehicle's actuator, the generated trajectory has continuity, and can be applied to the real-time changing road environment.
3、本发明对车辆进行的轨迹规划，是路径与速度的双规划，即在给车辆规划路径的同时规划处车辆在该路径上各点的速度。3. The trajectory planning of the vehicle by the present invention is a dual planning of path and speed, that is, planning the path of the vehicle while planning the speed of the vehicle at each point on the path.
附图说明BRIEF DESCRIPTION
图1为本发明决策***总体框图。Figure 1 is an overall block diagram of the decision-making system of the present invention.
图2为给定目标位置点进行最优化搜索方法对应的示意图。FIG. 2 is a schematic diagram corresponding to an optimization search method for a given target position point.
具体实施方式detailed description
为了便于本领域技术人员的理解，下面结合实施例与附图对本发明作进一步的说明，实施方式提及的内容并非对本发明的限定。In order to facilitate the understanding of those skilled in the art, the present invention will be further described below in conjunction with the embodiments and drawings, and the content mentioned in the embodiments does not limit the present invention.
参照图1所示，本发明的一种复杂工况下自动驾驶车辆决策***，其特征在于，包括：环境感知单元、实时决策单元、轨迹规划单元及控制单元；其中，Referring to FIG. 1, a decision-making system for an autonomous driving vehicle under complex working conditions of the present invention is characterized by comprising: an environment awareness unit, a real-time decision unit, a trajectory planning unit and a control unit; wherein,
环境感知单元，包含自动驾驶车辆上设有的GPS、激光雷达、摄像头、超声波雷达及横摆角速度传感器；用于实时感知自动驾驶车辆及周围车辆的运动信息；所述运动信息包含：位置信息、速度信息及横摆角信息。Environment awareness unit, including GPS, lidar, camera, ultrasonic radar, and yaw rate sensor installed on the self-driving vehicle; used to sense the motion information of the self-driving vehicle and surrounding vehicles in real time; the motion information includes: location information, Speed information and yaw angle information.
实时决策单元，包含自动驾驶车辆上的车载计算机及与之相连的CAN通信模块；CAN通信模块与上述环境感知单元连接，并接收上述运动信息；车载计算机根据运动信息决策出当前状态下自动驾驶车辆最佳的驾驶行为；Real-time decision-making unit, including the on-board computer on the autonomous driving vehicle and the CAN communication module connected to it; the CAN communication module is connected to the above-mentioned environment sensing unit and receives the above-mentioned motion information; the on-board computer decides the automatic driving vehicle in the current state according to the motion information Best driving behavior;
轨迹规划单元，与上述实时决策单元相连接，其根据实时决策单元决策出的自动驾驶车辆最佳的驾驶行为对车辆的路径及速度进行双规划，得到最优轨迹；在规划时考虑车辆的安全性约束、高效性约束、平稳性约束。The trajectory planning unit is connected to the above-mentioned real-time decision-making unit, which double-plans the path and speed of the vehicle according to the best driving behavior of the self-driving vehicle decided by the real-time decision-making unit, and obtains the optimal trajectory; the safety of the vehicle is considered when planning Sexual constraints, high efficiency constraints, stability constraints.
危险性约束：
Figure PCTCN2019115628-appb-000016
即保证最优轨迹对应的危险度R小于乘客可接受的危险等级
Figure PCTCN2019115628-appb-000017
根据该危险性约束，即可将三维决策场约束成一个二维平面；具体求解如下： Dangerous constraints:
Figure PCTCN2019115628-appb-000016
That is to ensure that the hazard degree R corresponding to the optimal trajectory is less than the hazard level acceptable to passengers
Figure PCTCN2019115628-appb-000017
According to the risk constraint, the three-dimensional decision field can be constrained into a two-dimensional plane; the specific solution is as follows:
Figure PCTCN2019115628-appb-000018
Figure PCTCN2019115628-appb-000018
式中，γ为权重因子；s t表示t时刻给定轨迹对应的状态参数，具体包括横纵坐标、速度、横摆角信息；p t表示t时刻周围车辆对应的状态参数，具体包括横纵坐标、速度、横摆角信息；F为危险度评估函数； In the formula, γ is the weighting factor; s t represents the state parameters corresponding to the given trajectory at time t, specifically including the horizontal and vertical coordinates, speed, and yaw angle information; p t represents the state parameters corresponding to the surrounding vehicles at time t, specifically including the horizontal and vertical Coordinate, speed, yaw angle information; F is the risk assessment function;
高效性约束：
Figure PCTCN2019115628-appb-000019
将上述二维平面约束成一条曲线；其中，v final为给定轨迹若干个周期后对应的速度，
Figure PCTCN2019115628-appb-000020
为当前交通状况下对应的车辆最佳车速； Efficiency constraints:
Figure PCTCN2019115628-appb-000019
Constrain the above two-dimensional plane to a curve; where v final is the corresponding velocity after a certain period of a given trajectory,
Figure PCTCN2019115628-appb-000020
The best speed of the corresponding vehicle under current traffic conditions;
平稳性约束：min Δy，将上述的曲线约束成最优的决策点，通过该决策点来确定自动驾驶车辆下一时刻的最优行驶轨迹；其中，Δy为决策出的轨迹与当前道路的纵向偏差。Stationary constraint: minΔy, which constrains the above curve to the optimal decision point, and determines the optimal driving trajectory of the next time for the autonomous vehicle by this decision point; where Δy is the longitudinal direction of the determined trajectory and the current road deviation.
控制单元，包含电子控制单元(ECU)及与之电气连接的主动转向电机、制动及油门踏板位移电机；电子控制单元根据上述轨迹规划单元规划出的最优轨迹，产生对主动转向电机、制动及油门踏板位移电机的控制指令，进而控制车辆行驶状态。The control unit includes an electronic control unit (ECU) and an active steering motor, a brake and an accelerator pedal displacement motor electrically connected thereto; the electronic control unit generates an active steering motor, a control system according to the optimal trajectory planned by the above trajectory planning unit Control commands for moving and accelerator pedal displacement motors, which in turn control the vehicle's driving status.
在工作时，环境感知单元先利用各种传感器来获取自动驾驶车辆周围的运动信息，并将这些信息传输到车载计算机内进行实时决策，具体决策时，利用“遍历轨迹+最优搜索法”来进行轨迹规划，该轨迹既包含了自动驾驶车辆的路径信息，又包含了速度信息，从而很好的适应实际复杂工况下的决策规划。得到轨迹信息后，车辆的控制单元会利用相应的控制算法将轨迹信息转化为控制器直接输入的控制量信息，并将这些控制量输入到主动转向电机、制动及油门踏板位移电机上，从而实现车辆的自动驾驶。When working, the environment awareness unit first uses various sensors to obtain the motion information around the autonomous vehicle, and transmits this information to the on-board computer for real-time decision-making. When making specific decisions, use the "traversal trajectory + optimal search method" to Carry out trajectory planning, the trajectory contains not only the path information of the self-driving vehicle, but also the speed information, so it is well adapted to the decision planning under the actual complex working conditions. After obtaining the trajectory information, the vehicle's control unit will use the corresponding control algorithm to convert the trajectory information into the control quantity information directly input by the controller, and input these control quantities to the active steering motor, brake, and accelerator pedal displacement motor, thereby Realize the automatic driving of vehicles.
本发明还公开了一种复杂工况下自动驾驶车辆决策***的轨迹规划方法，基于上述***，包括以下步骤：The invention also discloses a trajectory planning method of the automatic driving vehicle decision system under complex working conditions. Based on the above system, it includes the following steps:
1)获得遍历轨迹：先给定若干周期后车辆可能到达的目标位置点，每个目标位置点代表一条轨迹，即遍历轨迹；1) Obtain the traversal trajectory: given a target position point that the vehicle may reach after several cycles, each target position point represents a trajectory, that is, the traversal trajectory;
2)根据上述遍历轨迹进行最优化搜索，通过车辆面对的危险性、高效性和稳定性约束来优化出当前时刻车辆最优的一条轨迹。2) Perform an optimization search based on the above traversal trajectory, and optimize the vehicle's optimal trajectory at the current moment through the danger, efficiency and stability constraints faced by the vehicle.
所述步骤1)中获得遍历轨迹的方法，具体包括如下步骤：The method for obtaining the traversing trajectory in step 1) specifically includes the following steps:
11)根据车辆当前位置及给定的目标位置点，利用多项式拟合出车辆未来的遍历轨迹对应的路径，利用4个约束，使用3次多项式进行拟合，具体如下：11) According to the current position of the vehicle and the given target position point, the path corresponding to the future traversal trajectory of the vehicle is fitted using a polynomial, and the third-order polynomial is used to fit using 4 constraints, as follows:
y＝a 0+a 1x+a 2x 2+a 3x 3 y=a 0 +a 1 x+a 2 x 2 +a 3 x 3
Figure PCTCN2019115628-appb-000021
Figure PCTCN2019115628-appb-000021
其中，a 0,a 1,a 2,a 3分别为多项式路径对应的拟合参数，
Figure PCTCN2019115628-appb-000022
为当前时刻车辆对应的横摆角，(x k,y k)为当前状态自动驾驶车辆对应的位置坐标，(x p,y p)为给定的若干个周期之后自动驾驶车辆到达的目标点； Among them, a 0 , a 1 , a 2 , a 3 are the fitting parameters corresponding to the polynomial path,
Figure PCTCN2019115628-appb-000022
Is the yaw angle corresponding to the vehicle at the current moment, (x k ,y k ) is the position coordinate corresponding to the current state of the automatic driving vehicle, (x p ,y p ) is the target point reached by the automatic driving vehicle after a given number of cycles ;
12)利用积分求出上述路径对应的长度，将该过程视为匀加速运动过程，得到遍历轨迹对应的速度；12) Use integral to find the length corresponding to the above path, and regard the process as a uniform acceleration motion process to obtain the speed corresponding to the traversal trajectory;
每条决策路径的长度S：The length S of each decision path:
Figure PCTCN2019115628-appb-000023
Figure PCTCN2019115628-appb-000023
其中，y’为多项式路径的斜率；Where y’ is the slope of the polynomial path;
将该过程视为匀加速运动；从而得到每个决策点对应的加速度：Think of this process as a uniform acceleration motion; thus obtaining the acceleration corresponding to each decision point:
Figure PCTCN2019115628-appb-000024
Figure PCTCN2019115628-appb-000024
其中，T为每个周期对应的时间，v k为车辆当前速度，a为该过程对应的加速度； Where T is the time corresponding to each cycle, v k is the current speed of the vehicle, and a is the acceleration corresponding to the process;
该过程对应的速度表示如下：The speed corresponding to this process is expressed as follows:
v t＝v k+a*(t-k) v t = v k +a*(tk)
其中，v k为当前时刻车辆对应的速度，v t表示t个周期后车辆对应的速度； Where v k is the speed corresponding to the vehicle at the current moment, and v t is the speed corresponding to the vehicle after t cycles;
13)根据步骤11)拟合出的路径和步骤12)得到的在该路径上的速度，即得到了车辆遍历轨迹的参数信息(路径+速度)。13) According to the path fitted in step 11) and the speed on the path obtained in step 12), the parameter information (path + speed) of the traversing trajectory of the vehicle is obtained.
参照图2所示，所述步骤2)中的最优轨迹搜索方法，具体包括如下步骤：Referring to FIG. 2, the optimal trajectory search method in step 2) specifically includes the following steps:
21)获取每条轨迹对应的危险度，该危险度即可形成一个车辆在未来时刻的三维危险场，其中危险度评估函数F建立如下：21) Obtain the hazard degree corresponding to each trajectory, and the hazard degree can form a three-dimensional hazard field of the vehicle at the future moment, in which the hazard assessment function F is established as follows:
Figure PCTCN2019115628-appb-000025
Figure PCTCN2019115628-appb-000025
其中，S r为道路安全系数；D sf为标准安全距离；b为限幅系数；D es为自车与周围车辆实际距离，t为周围车辆到达自车前方的时间，t b为刹车时间； Among them, S r is the road safety factor; D sf is the standard safety distance; b is the limiting factor; Des is the actual distance between the vehicle and the surrounding vehicle, t is the time when the surrounding vehicle reaches the front of the vehicle, and t b is the braking time;
22)对上述三维危险场进行约束，得到一条最优轨迹。22) Constrain the above three-dimensional hazard field to obtain an optimal trajectory.
其中，所述步骤22)中对三维危险场进行约束具体包括：Wherein, the step 22) restricting the three-dimensional hazard field specifically includes:
221)危险性约束：
Figure PCTCN2019115628-appb-000026
即保证最优轨迹对应的危险度R小于乘客可接受的危险等级
Figure PCTCN2019115628-appb-000027
根据该约束，即可将三维决策场约束成一个二维平面；具体求解如下： 221) Dangerous constraints:
Figure PCTCN2019115628-appb-000026
That is to ensure that the hazard degree R corresponding to the optimal trajectory is less than the hazard level acceptable to passengers
Figure PCTCN2019115628-appb-000027
According to the constraint, the three-dimensional decision field can be constrained into a two-dimensional plane; the specific solution is as follows:
Figure PCTCN2019115628-appb-000028
Figure PCTCN2019115628-appb-000028
其中，γ为权重因子；s t表示t时刻给定轨迹对应的状态参数，具体包括横纵坐标、速度、横摆角信息；p t表示t时刻周围车辆对应的状态参数，具体包括横纵坐标、速度、横摆角信息；F为危险度评估函数； Among them, γ is the weighting factor; s t represents the state parameter corresponding to the given trajectory at time t, specifically including the horizontal and vertical coordinates, speed, and yaw angle information; p t represents the state parameter corresponding to the surrounding vehicles at time t, specifically including the horizontal and vertical coordinates , Speed, yaw angle information; F is the risk assessment function;
222)高效性约束：
Figure PCTCN2019115628-appb-000029
该约束保证车辆能沿着交通道路提供的最佳车速行驶，根据该高效性约束，将上述二维平面约束成一条曲线；其中，v final为给定轨迹若干个周期后对应的速度，
Figure PCTCN2019115628-appb-000030
为当前交通状况下对应的车辆最佳车速； 222) Efficiency constraints:
Figure PCTCN2019115628-appb-000029
This constraint ensures that the vehicle can travel along the optimal speed provided by the traffic road. According to the efficiency constraint, the above two-dimensional plane is constrained into a curve; where, v final is the corresponding speed after a certain period of the given trajectory,
Figure PCTCN2019115628-appb-000030
The best speed of the corresponding vehicle under current traffic conditions;
223)平稳性约束：min Δ y，该平稳性约束能保证车辆少换道，从而提高乘坐舒适性；根据该约束，将上述的曲线约束成最优的决策点，通过该决策点来确定自动驾驶车辆t+1时刻的最优行驶轨迹；其中，Δ y为决策出的轨迹与当前道路的纵向偏差。 223) stationary constraint: min Δ y, which can ensure a smooth constraint less for road vehicles, thereby improving the ride comfort; According to the constrained, constrained to the above-mentioned curve optimal decision point, decision point is determined automatically by the vehicle driving time t + 1 of the optimum traveling locus; wherein, Δ y longitudinal deviation of the trajectory of the decision of the current road.
3)以上得到了车辆在当前状态下的最优轨迹，下面需要控制车辆跟踪该轨迹，基于轮胎小角度转角下的线性化假设建立如下车辆动力学模型进行跟踪。3) The above obtains the optimal trajectory of the vehicle in the current state. Next, the vehicle needs to be controlled to track the trajectory, and the following vehicle dynamics model is established to track based on the linearization assumption under the small-angle tire rotation angle.
其中，步骤3)中使用的基于车辆动力学的跟踪方法，包括如下步骤：Among them, the tracking method based on vehicle dynamics used in step 3) includes the following steps:
31)基于动力学的车辆状态方程获得：31) The vehicle state equation based on dynamics is obtained:
当车辆控制量u dyn为前轮转角σ f及车速v，即u dyn＝[σ f,v]时，状态量为车辆运动轨迹序列
Figure PCTCN2019115628-appb-000031
时， When the vehicle control quantity u dyn is the front wheel rotation angle σ f and the vehicle speed v, that is, u dyn =[σ f ,v], the state quantity is the vehicle motion trajectory sequence
Figure PCTCN2019115628-appb-000031
Time,
其中，
Figure PCTCN2019115628-appb-000032
为车辆纵向速度，
Figure PCTCN2019115628-appb-000033
为车辆侧向速度时，
Figure PCTCN2019115628-appb-000034
为车辆横摆角，
Figure PCTCN2019115628-appb-000035
为横摆角速度，Y,X为地球坐标系下的纵横坐标； among them,
Figure PCTCN2019115628-appb-000032
Is the longitudinal speed of the vehicle,
Figure PCTCN2019115628-appb-000033
When it is the lateral speed of the vehicle,
Figure PCTCN2019115628-appb-000034
For the vehicle yaw angle,
Figure PCTCN2019115628-appb-000035
Is the yaw rate, Y and X are the vertical and horizontal coordinates in the earth coordinate system;
可建立如下离散化的状态方程：The following discrete state equations can be established:
Figure PCTCN2019115628-appb-000036
Figure PCTCN2019115628-appb-000036
其中，k为当前时刻，T是每个离散化周期的采样时间，I为单位矩阵，f为动力学方程，(u 0,ξ 0)为参考状态点。 Where k is the current moment, T is the sampling time of each discretization period, I is the identity matrix, f is the dynamic equation, and (u 0 ,ξ 0 ) is the reference state point.
32)跟踪目标函数的建立：32) The establishment of the tracking objective function:
有以上基于动力学的预测方程，则可进一步根据预测的轨迹与期望轨迹的偏差来跟踪，从而控制车辆安全行驶，具体采用如下目标函数J(k)：With the above dynamics-based prediction equation, it can be further tracked according to the deviation of the predicted trajectory from the expected trajectory to control the safe driving of the vehicle. The specific objective function J(k) is used as follows:
Figure PCTCN2019115628-appb-000037
Figure PCTCN2019115628-appb-000037
其中，η为预测的实际状态量，η ref为规划轨迹得到的参考状态量，ΔU为控制量增量；该目标函数的第一项是预测轨迹与参考轨迹的偏差，体现轨迹跟随性；该目标函数的第二项是控制量的偏差，体现稳定性。 Where η is the predicted actual state quantity, η ref is the reference state quantity obtained from the planned trajectory, and ΔU is the control quantity increment; the first term of the objective function is the deviation of the predicted trajectory from the reference trajectory, reflecting the trajectory followability; The second term of the objective function is the deviation of the control quantity, which reflects stability.
33)对于以上目标函数，通过以下两个约束求得最终轨迹跟踪序列；33) For the above objective function, the final trajectory tracking sequence is obtained through the following two constraints;
321)控制量约束：其中控制量定义如下：321) Control amount constraint: where the control amount is defined as follows:
u min(t+k)≤u(t+k)≤u max(t+k),k＝0,1,L,20 u min (t+k)≤u(t+k)≤u max (t+k),k=0,1,L,20
322)控制增量约束：322) Control incremental constraints:
Δu min(t+k)≤Δu(t+k)≤Δu max(t+k),k＝0,1,L,20 Δu min (t+k)≤Δu(t+k)≤Δu max (t+k), k=0,1,L,20
有了以上约束，即可在每个时刻求解出自动驾驶车辆最佳的跟踪控制量序列,从而保证车辆沿着最安全的轨迹行驶。With the above constraints, the optimal tracking control quantity sequence of the autonomous vehicle can be solved at every moment, so as to ensure that the vehicle travels along the safest trajectory.
本发明具体应用途径很多，以上所述仅是本发明的优选实施方式，应当指出，对于本技术领域的普通技术人员来说，在不脱离本发明原理的前提下，还可以作出若干改进，这些改进也应视为本发明的保护范围。There are many specific application ways of the present invention, and the above are only preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, several improvements can be made without departing from the principles of the present invention. Improvements should also be regarded as the scope of protection of the present invention.

Claims (9)

  1. 一种复杂工况下自动驾驶车辆决策***，其特征在于，包括：环境感知单元、实时决策单元、轨迹规划单元及控制单元；其中，A decision-making system for autonomous driving vehicles under complex working conditions, which is characterized by comprising: an environment perception unit, a real-time decision unit, a trajectory planning unit and a control unit; wherein,
    环境感知单元，用于实时感知自动驾驶车辆及周围车辆的运动信息；Environment awareness unit, used to sense the motion information of autonomous vehicles and surrounding vehicles in real time;
    实时决策单元，包含自动驾驶车辆上的车载计算机及与之相连的CAN通信模块；CAN通信模块与上述环境感知单元连接，并接收上述运动信息；车载计算机根据运动信息决策出当前状态下自动驾驶车辆最佳的驾驶行为；Real-time decision-making unit, including the on-board computer on the autonomous driving vehicle and the CAN communication module connected to it; the CAN communication module is connected to the above-mentioned environment sensing unit and receives the above-mentioned motion information; the on-board computer decides the automatic driving vehicle in the current state according to the motion information Best driving behavior;
    轨迹规划单元，与上述实时决策单元相连接，其根据实时决策单元决策出的自动驾驶车辆最佳的驾驶行为对车辆的路径及速度进行双规划，得到最优轨迹；The trajectory planning unit is connected to the above-mentioned real-time decision-making unit, which performs double planning on the path and speed of the vehicle according to the optimal driving behavior of the autonomous vehicle decided by the real-time decision-making unit to obtain the optimal trajectory;
    控制单元，包含电子控制单元及与之电气连接的主动转向电机、制动及油门踏板位移电机；电子控制单元根据上述轨迹规划单元规划出的最优轨迹，产生对主动转向电机、制动及油门踏板位移电机的控制指令，进而控制车辆行驶状态。The control unit includes the electronic control unit and the active steering motor, brake and accelerator pedal displacement motor electrically connected thereto; the electronic control unit generates the active steering motor, brake and throttle according to the optimal trajectory planned by the above trajectory planning unit The control command of the pedal displacement motor, which in turn controls the driving state of the vehicle.
  2. 根据权利要求1所述的复杂工况下自动驾驶车辆决策***，其特征在于，所述环境感知单元包含自动驾驶车辆上设有的GPS、激光雷达、摄像头、超声波雷达及横摆角速度传感器。The automatic driving vehicle decision-making system according to claim 1, wherein the environment sensing unit includes a GPS, a laser radar, a camera, an ultrasonic radar, and a yaw rate sensor provided on the automatic driving vehicle.
  3. 根据权利要求1所述的复杂工况下自动驾驶车辆决策***，其特征在于，所述运动信息包含：位置信息、速度信息及横摆角信息。The automatic driving vehicle decision-making system according to claim 1, wherein the motion information includes position information, speed information, and yaw angle information.
  4. 根据权利要求1所述的复杂工况下自动驾驶车辆决策***，其特征在于，所述轨迹规划单元在规划时考虑车辆的安全性约束、高效性约束、平稳性约束。The automatic driving vehicle decision-making system according to claim 1, wherein the trajectory planning unit considers vehicle safety constraints, efficiency constraints, and stability constraints during planning.
  5. 根据权利要求4所述的复杂工况下自动驾驶车辆决策***，其特征在于，所述的危险性约束：
    Figure PCTCN2019115628-appb-100001
    即保证最优轨迹对应的危险度R小于乘客可接受的危险等级
    Figure PCTCN2019115628-appb-100002
    根据该危险性约束，即将三维决策场约束成一个二维平面；具体求解如下：     The automatic driving vehicle decision-making system according to claim 4, wherein the risk constraint is:
    Figure PCTCN2019115628-appb-100001
    That is to ensure that the hazard degree R corresponding to the optimal trajectory is less than the hazard level acceptable to passengers
    Figure PCTCN2019115628-appb-100002
    According to the risk constraint, the three-dimensional decision field is constrained into a two-dimensional plane; the specific solution is as follows:
    Figure PCTCN2019115628-appb-100003
    Figure PCTCN2019115628-appb-100003
    其中，γ为权重因子；s t表示t时刻给定轨迹对应的状态参数，具体包括横纵坐标、速度、横摆角信息；p t表示t时刻周围车辆对应的状态参数，具体包括横纵坐标、速度、横摆角信息；F为危险度评估函数； Among them, γ is the weighting factor; s t represents the state parameter corresponding to the given trajectory at time t, specifically including the horizontal and vertical coordinates, speed, and yaw angle information; p t represents the state parameter corresponding to the surrounding vehicles at time t, specifically including the horizontal and vertical coordinates , Speed, yaw angle information; F is the risk assessment function;
    高效性约束：
    Figure PCTCN2019115628-appb-100004
    将上述二维平面约束成一条曲线；其中，v final为给定轨迹若干个周期后对应的速度，
    Figure PCTCN2019115628-appb-100005
    为当前交通状况下对应的车辆最佳车速；     Efficiency constraints:
    Figure PCTCN2019115628-appb-100004
    Constrain the above two-dimensional plane to a curve; where v final is the corresponding velocity after a certain period of a given trajectory,
    Figure PCTCN2019115628-appb-100005
    The best speed of the corresponding vehicle under current traffic conditions;
    平稳性约束：minΔy，将上述的曲线约束成最优的决策点，通过该决策点来确定自动驾驶车辆下一时刻的最优行驶轨迹；其中，Δy为决策出的轨迹与当前道路的纵向偏差。Stationary constraint: minΔy, constrain the above curve to the optimal decision point, and determine the optimal driving trajectory for the next time of the autonomous vehicle by this decision point; where Δy is the longitudinal deviation of the determined trajectory from the current road .
  6. 一种复杂工况下自动驾驶车辆决策***的轨迹规划方法，基于上述权利要求1至5中任意一项所述的***，包括以下步骤：A trajectory planning method for an automated driving vehicle decision system under complex working conditions, based on the system of any one of claims 1 to 5, includes the following steps:
    1)获得遍历轨迹：先给定若干周期后车辆可能到达的目标位置点，每个目标位置点代表一条轨迹，即遍历轨迹；1) Obtain the traversal trajectory: given a target position point that the vehicle may reach after several cycles, each target position point represents a trajectory, that is, the traversal trajectory;
    2)根据上述遍历轨迹进行最优化搜索，通过车辆面对的危险性、高效性和稳定性约束来优化出当前时刻车辆最优的一条轨迹。2) Perform an optimization search based on the above traversal trajectory, and optimize the vehicle's optimal trajectory at the current moment through the danger, efficiency and stability constraints faced by the vehicle.
  7. 根据权利要求6所述的复杂工况下自动驾驶车辆决策***的轨迹规划方法，其特征在于，所述步骤1)中获得遍历轨迹的方法，具体包括如下步骤：The trajectory planning method of the automatic driving vehicle decision system under complex working conditions according to claim 6, characterized in that the method of obtaining the traversal trajectory in step 1) specifically includes the following steps:
    11)根据车辆当前位置及给定的目标位置点，利用多项式拟合出车辆未来的遍历轨迹对应的路径，利用4个约束，使用3次多项式进行拟合，具体如下：11) According to the current position of the vehicle and the given target position point, the path corresponding to the future traversal trajectory of the vehicle is fitted using a polynomial, and the third-order polynomial is used to fit using 4 constraints, as follows:
    y＝a 0+a 1x+a 2x 2+a 3x 3 y=a 0 +a 1 x+a 2 x 2 +a 3 x 3
    Figure PCTCN2019115628-appb-100006
    Figure PCTCN2019115628-appb-100006
    其中，a 0,a 1,a 2,a 3分别为多项式路径对应的拟合参数，
    Figure PCTCN2019115628-appb-100007
    为当前时刻车辆对应的横摆角，(x k,y k)为当前状态自动驾驶车辆对应的位置坐标，(x p,y p)为给定的若干个周期之后自动驾驶车辆到达的目标点；     Among them, a 0 , a 1 , a 2 , a 3 are the fitting parameters corresponding to the polynomial path,
    Figure PCTCN2019115628-appb-100007
    Is the yaw angle corresponding to the vehicle at the current moment, (x k , y k ) is the position coordinate corresponding to the current state of the automatic driving vehicle, (x p , y p ) is the target point reached by the automatic driving vehicle after a given number of cycles ;
    12)利用积分求出上述路径对应的长度，将该过程视为匀加速运动过程，得到遍历轨迹对应的速度；12) Use the integral to find the length corresponding to the above path, and treat the process as a uniform acceleration motion process to obtain the speed corresponding to the traversal trajectory;
    每条决策路径的长度S：The length S of each decision path:
    Figure PCTCN2019115628-appb-100008
    Figure PCTCN2019115628-appb-100008
    其中，y’为多项式路径的斜率；Where y’ is the slope of the polynomial path;
    将该过程视为匀加速运动；从而得到每个决策点对应的加速度：Think of this process as a uniform acceleration motion; thus obtaining the acceleration corresponding to each decision point:
    Figure PCTCN2019115628-appb-100009
    Figure PCTCN2019115628-appb-100009
    其中，T为每个周期对应的时间，v k为车辆当前速度，a为该过程对应的加速度； Where T is the time corresponding to each cycle, v k is the current speed of the vehicle, and a is the acceleration corresponding to the process;
    该过程对应的速度表示如下：The speed corresponding to this process is expressed as follows:
    v t＝v k+a*(t-k) v t = v k +a*(tk)
    其中，v k为当前时刻车辆对应的速度，v t表示t个周期后车辆对应的速度； Where v k is the speed corresponding to the vehicle at the current moment, and v t is the speed corresponding to the vehicle after t cycles;
    13)根据步骤11)拟合出的路径和步骤12)得到的在该路径上的速度，即得到车辆遍历 轨迹的参数信息。13) According to the path fitted in step 11) and the speed on the path obtained in step 12), the parameter information of the trajectory of the vehicle is obtained.
  8. 根据权利要求6所述的复杂工况下自动驾驶车辆决策***的轨迹规划方法，其特征在于，所述步骤2)中的最优轨迹搜索方法，具体包括如下步骤：The trajectory planning method of the automatic driving vehicle decision system under complex working conditions according to claim 6, wherein the optimal trajectory search method in step 2) specifically includes the following steps:
    21)获取每条轨迹对应的危险度，该危险度即可形成一个车辆在未来时刻的三维危险场，其中危险度评估函数F建立如下：21) Obtain the hazard degree corresponding to each trajectory, and the hazard degree can form a three-dimensional hazard field of the vehicle at the future moment, in which the hazard assessment function F is established as follows:
    Figure PCTCN2019115628-appb-100010
    Figure PCTCN2019115628-appb-100010
    其中，S r为道路安全系数；D sf为标准安全距离；b为限幅系数；D es为自车与周围车辆实际距离，t为周围车辆到达自车前方的时间，t b为刹车时间； Among them, S r is the road safety factor; D sf is the standard safety distance; b is the limiting factor; Des is the actual distance between the vehicle and the surrounding vehicle, t is the time when the surrounding vehicle reaches the front of the vehicle, and t b is the braking time;
    22)对上述三维危险场进行约束，得到一条最优轨迹。22) Constrain the above three-dimensional danger field to obtain an optimal trajectory.
  9. 根据权利要求8所述的复杂工况下自动驾驶车辆决策***的轨迹规划方法，其特征在于，所述步骤22)中对三维危险场进行约束具体包括：The trajectory planning method of the automatic driving vehicle decision system under complex working conditions according to claim 8, wherein the step 22) of the constraint on the three-dimensional hazard field specifically includes:
    221)危险性约束：
    Figure PCTCN2019115628-appb-100011
    即保证最优轨迹对应的危险度R小于乘客可接受的危险等级
    Figure PCTCN2019115628-appb-100012
    根据该约束，即可将三维决策场约束成一个二维平面；具体求解如下：     221) Dangerous constraints:
    Figure PCTCN2019115628-appb-100011
    That is to ensure that the hazard degree R corresponding to the optimal trajectory is less than the hazard level acceptable to passengers
    Figure PCTCN2019115628-appb-100012
    According to the constraint, the three-dimensional decision field can be constrained into a two-dimensional plane; the specific solution is as follows:
    Figure PCTCN2019115628-appb-100013
    Figure PCTCN2019115628-appb-100013
    其中，γ为权重因子；s t表示t时刻给定轨迹对应的状态参数，具体包括横纵坐标、速度、横摆角信息；p t表示t时刻周围车辆对应的状态参数，具体包括横纵坐标、速度、横摆角信息；F为危险度评估函数； Among them, γ is the weighting factor; s t represents the state parameter corresponding to the given trajectory at time t, specifically including the horizontal and vertical coordinates, speed, and yaw angle information; p t represents the state parameter corresponding to the surrounding vehicles at time t, specifically including the horizontal and vertical coordinates , Speed, yaw angle information; F is the risk assessment function;
    222)高效性约束：
    Figure PCTCN2019115628-appb-100014
    将上述二维平面约束成一条曲线；其中，v final为给定轨迹若干个周期后对应的速度，
    Figure PCTCN2019115628-appb-100015
    为当前交通状况下对应的车辆最佳车速；     222) Efficiency constraints:
    Figure PCTCN2019115628-appb-100014
    Constrain the above two-dimensional plane to a curve; where v final is the corresponding velocity after a certain period of a given trajectory,
    Figure PCTCN2019115628-appb-100015
    The best speed of the corresponding vehicle under current traffic conditions;
    223)平稳性约束：minΔy，将上述的曲线约束成最优的决策点，通过该决策点来确定自动驾驶车辆t+1时刻的最优行驶轨迹；其中，Δy为决策出的轨迹与当前道路的纵向偏差。223) Stationarity constraint: minΔy, the above curve is constrained to an optimal decision point, and the optimal driving trajectory at time t+1 is determined by this decision point; where Δy is the determined trajectory and the current road Longitudinal deviation.
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