Introduction

谈判交通信号灯的决策逻辑是自动驾驶应用程序的基本组成部分。决策逻辑必须对交通信号灯和周围车辆状态等输入的反应。然后，决策逻辑为控制器提供所需的速度和路径。由于交通信号灯交叉点对测试很危险，因此模拟此类驾驶场景可以洞悉决策逻辑和控制器的相互作用。

This example shows how to design and test the decision logic for negotiating a traffic light. The decision logic in this example reacts to the state of the traffic light, distance to the traffic light, and distance to the closest vehicle ahead. In this example, you will:

您可以应用本示例中使用的建模模式来测试自己的决策逻辑和控件以协商交通信号灯。

Explore Test Bench Model

To explore the test bench model, open a working copy of the project example files. MATLAB® copies the files to an example folder so that you can edit them.

addpath(fullfile(matlabroot,'toolbox','driving','drivingdemos')); helperDrivingProjectSetup('TrafficLightNegotiation.zip','workDir', pwd);

要探索交通信号灯协商系统的行为，请打开系统的仿真测试基准模型。

open_system（“交通灯”);

Opening this model runs thehelperSLTrafficLightNegotiationSetupscript that initializes the road scenario using thedrivingScenario基本工作区中的对象。它运行默认测试方案，方案_02_tln_left_turn_with_cross_over_vehicle, that contains an ego vehicle and two other vehicles. This setup script also configures the controller design parameters, vehicle model parameters, and Simulink® bus signals required for defining the inputs and outputs for the交通点式测试台模型。

测试工作台模型包含以下子系统：

TheLane Following Controller参考模型和车辆动力学子系统从高速公路车道跟随example. This example focuses on the传感器和环境andTraffic Light Decision Logic子系统。

The传感器和环境subsystem configures the road network, defines target vehicle trajectories, and synthesizes sensors. Open the传感器和环境子系统。

open_system（“交通点燃的测试箱/传感器和环境”);

The scenario and sensors on the ego vehicle are specified by the following parts of the subsystem:

Plot the road network provided by the scenario.

hfigscenario =图（'位置'，[1 1 800 600]）;情节（场景，'Parent', axes(hFigScenario));

This default scenario has one intersection with an ego vehicle, one lead vehicle, and one cross-traffic vehicle.

关闭图。

关闭（hfigscenario）;

TheTracking and Sensor Fusion子系统融合了从Driving Radar Data Generatorand视觉检测发生器blocks by using a多对象跟踪器块提供对象跟踪周围的the ego vehicle.

视觉检测发生器块还提供了有关自我车辆的车道检测，该车辆有助于识别自我车道中存在的车辆。

The交通灯传感器子系统模拟交通信号灯。它被配置为支持在交叉路口的四个交通灯传感万博1manbetx器，TL传感器1,TL传感器2,TL传感器3, andTL传感器4。

Plot the scenario with traffic light sensors.

hfigscenario = helpplotscenariowithtrafficlights（）;

观察到这是与以前相同的情况，只有添加了交通灯传感器。这些传感器在交叉路口的红色圆圈表示，表明红色的交通信号灯。交通信号灯的标签1,2,3,4correspond toTL传感器1,TL传感器2,TL传感器3, andTL传感器4, respectively.

关闭图。

关闭（hfigscenario）;

The test scenarios in交通点式测试台are configured such that the ego vehicle negotiates withTL传感器1。There are three modes in which you can configure this交通灯传感器subsystem:

您可以使用交通灯传感器模式mask parameter.

Open the交通灯传感器子系统。

open_system（'TrafficLightNegotiationTestBench/Sensors and Environment/Traffic Light Sensor','力量');

TheTraffic Light Switching LogicStateflow® chart implements the traffic light state change logic for the four traffic light sensors. The initial state for all the traffic lights is set to red. Transition to a different mode is based on a trigger condition defined by distance of the ego vehicle to theTL传感器1traffic light. This distance is defined by the variabledistanceToTrafficLight。Traffic light transition is triggered if this distance is less than交通灯 - statetriggerThreshold。This threshold is currently set to 60 meters and can be changed in thehelperSLTrafficLightNegotiationSetupscript.

The Compute Distance To Traffic Light block calculatesdistanceToTrafficLightusing the traffic light position ofTL传感器1, defined by the variable交通聚光度。这是从交通信号灯位置蒙版参数交通灯传感器子系统。掩码参数的值设置为IntersectionInfo.tlSensor1 position, a variable set in the base workspace by thehelperSLTrafficLightNegotiationSetupscript.intersectionInfo结构是来自HelpergetTrafficlightscenefunction. This function is used to create the test scenarios that are compatible with the交通点式测试台模型。

流量灯决策逻辑和控制器需要以下输入来实施其功能：

Model Traffic Light Decision Logic

TheTraffic Light Decision Logicreference model arbitrates between the lead car and the traffic light. It also calculates the lane center information as required by the controller either using the detected lanes or a predefined path. Open theTraffic Light Decision Logicreference model.

open_system（“交通lightdecisionLogic”);

TheFind Lead Carsubsystem finds the lead car in the current lane from input object tracks. It provides relative distance,relativeDistToLeadCar和相对速度，相对级别的曲线，关于领先工具。如果没有铅车，则该块认为铅车在无限距离处存在。

The仲裁逻辑Stateflow chart uses the lead car information and implements the logic required to arbitrate between the traffic light and the lead vehicle at the intersection. Open the仲裁逻辑状态流图。

open_system（“交通lightdecisionLogic/仲裁逻辑”);

The仲裁逻辑Stateflow chart consists of two states,OnentryandOnRedAndYellowLightDetection。如果交通信号灯状态是绿色的，或者没有交通信号灯检测，则该状态仍在Onentrystate. If the traffic light state is red or yellow, then the state transitions to theOnRedAndYellowLightDetectionstate. The control flow switches between these states based ontrafficLightDetectionanddistanceToTrafficLight变量。在每个状态中，计算相对于最重要的对象（MIO）的相对距离和相对速度。领先的车辆和红色交通信号灯被认为是元人。

Onentry:

relativeDistance = relativeDistToLeadCar;

relativeVelocity = relativeVelocityOfLeadCar;

OnRedAndYellowLightDetection:

相对主义= min（perativeStoladcar，distancetRafficlight）;

相对速度=最小（相对级别的曲线，纵向级别）;

The纵向级别代表自我车辆的纵向速度。

与当前自我位置相交中心的距离计算距离距离相交中心的距离。由于交叉路口没有车道，因此自我车辆使用此距离落回交叉路口的预定义参考路径。

The车道中心决策逻辑子系统根据要求计算车道中心信息Path Following Control System(Model Predictive Control Toolbox)。Open the车道中心决策逻辑子系统。

open_system（“交通灯/车道中心决策逻辑”);

The车道中心决策逻辑子系统主要依赖于车道检测视觉检测发生器块到估计巷中心的信息，例如曲率，曲率导数，侧向偏移和标题角。但是，在交叉路口没有泳道标记可检测。在这种情况下，可以从预定义的参考路径中估算车道中心信息。

TheReference Path Lane Centersubsystem computes lane center information based on the current ego pose and predefined reference path. A switch is configured to useLANECENTERFROMREFERNECHETH什么时候伸缩式调查小于referencePathSwitchThreshold。此阈值当前设置为20米，可以在helperSLTrafficLightNegotiationSetupscript.

用交通信号灯和领先的车辆模拟左转弯

In this test scenario, a lead vehicle travels in the ego lane and crosses the intersection. The traffic light state keeps green for the lead vehicle and turns red for the ego vehicle. The ego vehicle is expected to follow the lead vehicle, negotiate the traffic light, and make a left turn.

配置交通点式测试台model to use the设想_03_TLN_left_turn_with_lead_vehicle设想。

helperSLTrafficLightNegotiationSetup("scenario_03_TLN_left_turn_with_lead_vehicle");% To reduce command-window output, first turn off the MPC update messages.mpcverbosity('离开');％模拟模型。sim(“交通灯”);

绘制仿真结果。

hfigresults = helperplottrafficlightnogotiationResults（logSout）;

检查结果。

关闭图。

close(hFigResults);

Simulate Left Turn with Traffic Light and Cross Traffic

This test scenario is an extension to the previous scenario. In addition to the previous conditions, in this scenario, a slow-moving cross-traffic vehicle is in the intersection when the traffic light is green for the ego vehicle. The ego vehicle is expected to wait for the cross-traffic vehicle to pass the intersection before taking the left turn.

配置交通点式测试台model to use the方案_02_tln_left_turn_with_cross_over_vehicle设想。

helperSLTrafficLightNegotiationSetup("scenario_02_TLN_left_turn_with_cross_over_vehicle");％模拟模型。sim(“交通灯”);

绘制仿真结果。

hfigresults = helperplottrafficlightnogotiationResults（logSout）;

检查结果。

关闭图。

close(hFigResults);

探索其他场景

在前面的部分中，您探索了系统行为设想_03_TLN_left_turn_with_lead_vehicleand方案_02_tln_left_turn_with_cross_over_vehicle方案。以下是兼容的方案列表交通点式测试台。

设想_01_TLN_left_turn scenario_02_TLN_left_turn_with_cross_over_vehicle [Default] scenario_03_TLN_left_turn_with_lead_vehicle scenario_04_TLN_straight scenario_05_TLN_straight_with_lead_vehicle

使用这些其他场景进行分析交通点式测试台under different conditions. For example, while learning about the interactions between the traffic light decision logic and controls, it can be helpful to begin with a scenario that has an intersection with a traffic light but no vehicles. To configure the model and workspace for such a scenario, use this code:

helperSLTrafficLightNegotiationSetup("scenario_04_TLN_straight");

Enable the MPC update messages.

mpcverbosity('on');

Conclusion

在此示例中，您实现了交通信号灯协商的决策逻辑，并在闭环Simulink模型中使用Controller的车道进行了测试。万博1manbetx