CARLA（anopenurbandrivingsimulator）

论文&＃xff1a;http://proceedings.mlr.press/v78/dosovitskiy17a/dosovitskiy17a.pdf

文档&＃xff1a;https://carla.readthedocs.io/en/latest/

介绍&＃xff1a;

CARLA包含三个模块的自动驾驶&＃xff1a;① 经典的规则化无人驾驶 ② 端对端模仿学习无人驾驶 ③端对端强化学习无人驾驶

CARLA支持感知和控制两个模块&＃xff0c;包含城市堵路&＃xff08;有汽车&＃xff0c;建筑物&＃xff0c;行人和道路指示标志&＃xff09;&＃xff0c;CARLA提供世界和智能体的接口&＃xff0c;客户端API是python命令控制&＃xff0c;以类似插槽&＃xff08;socket&＃xff09;的方式连接智能体和服务器。客户端client发送命令和下层指令&＃xff0c;直接命令包括转向&＃xff0c;加速和刹车&＃xff0c;下层命令包括控制服务器的行为和重置仿真器&＃xff0c;改变仿真环境和修改传感器参数。CARLA可以调整视觉信息质量和速度。CARLA有两个城镇&＃xff0c;TOWN1用来训练&＃xff0c;TOWN2用来测试。CARLA包含许多传感器&＃xff0c;有RGB摄像头&＃xff0c;提供深度信息的摄像头&＃xff08;该深度信息和语义分割&＃xff0c;CARLA已经做好了&＃xff0c;语义分割有12个种类&＃xff1a;道路&＃xff0c;道路线&＃xff0c;交通灯&＃xff0c;行人等等&＃xff09;GPS定位传感器&＃xff0c;速度加速度传感器和碰撞传感器等等。

在CARLR中存在探索状态和行动&＃xff0c;包含转向、节气门和刹车&＃xff0c;包含传感器输入信息。

启动&＃xff1a;

linux系统下安装各种依赖&＃xff1a;

sudo add-apt-repository ppa:ubuntu-toolchain-r/test
sudo apt-get update
sudo apt-get install build-essential clang-5.0 lld-5.0 g&＃43;&＃43;-7 ninja-build python python-pip python-dev tzdata sed curl wget unzip autoconf libtool
pip install --user setuptools nose2sudo update-alternatives --install /usr/bin/clang&＃43;&＃43; clang&＃43;&＃43; /usr/lib/llvm-5.0/bin/clang&＃43;&＃43; 101
sudo update-alternatives --install /usr/bin/clang clang /usr/lib/llvm-5.0/bin/clang 101

安装 Unreal Engine:

git clone --depth&＃61;1 -b 4.19 https://github.com/EpicGames/UnrealEngine.git ~/UnrealEngine_4.19
cd ~/UnrealEngine_4.19
./Setup.sh && ./GenerateProjectFiles.sh && make

安装CARLA&＃xff1a;

git clone https://github.com/carla-simulator/carlaexport UE4_ROOT&＃61;~/UnrealEngine_4.19

在指定路径下&＃xff08;下载CARLA的路径下&＃xff09;&＃xff1a;

CarlaUE4.sh

CARLA默认的TCP接口是2000和2001&＃xff0c;可以通过以下命令修改&＃xff1a;

-carla-port&＃61;N

如果需要运行示例样例&＃xff1a;

python example.py

修改地图&＃xff1a;

./CarlaUE4.sh /Game/Carla/Maps/Town02

配置&＃xff1a;

快进训练时间&＃xff1a;

./CarlaUE4.sh -benchmark -fps&＃61;5

修改摄像头和传感器的参数&＃xff1a;

在Example.CarlaSettings.ini文件中修改&＃xff0c;图片在服务器之间以BGRA数组的格式传送&＃xff0c;用户也可以自己定义其他格式。

场景终端相机&＃xff1a;&＃xff08;一般情况下选择在python中&＃xff0c;后续将不再列出ini的修改&＃xff09;让整个场景看起来更加真实&＃xff0c;在Python中&＃xff1a;

camera &＃61; carla.sensor.Camera(&＃39;MyCamera&＃39;, PostProcessing&＃61;&＃39;SceneFinal&＃39;)
camera.set(FOV&＃61;90.0)
camera.set_image_size(800, 600)
camera.set_position(x&＃61;0.30, y&＃61;0, z&＃61;1.30)
camera.set_rotation(pitch&＃61;0, yaw&＃61;0, roll&＃61;0)carla_settings.add_sensor(camera)

在CarlaSettings.ini中&＃xff1a;

[CARLA/Sensor/MyCamera]
SensorType&＃61;CAMERA
PostProcessing&＃61;SceneFinal
ImageSizeX&＃61;800
ImageSizeY&＃61;600
FOV&＃61;90
PositionX&＃61;0.30
PositionY&＃61;0
PositionZ&＃61;1.30
RotationPitch&＃61;0
RotationRoll&＃61;0
RotationYaw&＃61;0

深度地图相机&＃xff1a;

camera &＃61; carla.sensor.Camera(&＃39;MyCamera&＃39;, PostProcessing&＃61;&＃39;Depth&＃39;)
camera.set(FOV&＃61;90.0)
camera.set_image_size(800, 600)
camera.set_position(x&＃61;0.30, y&＃61;0, z&＃61;1.30)
camera.set_rotation(pitch&＃61;0, yaw&＃61;0, roll&＃61;0)carla_settings.add_sensor(camera)

语义分割相机&＃xff1a;将图像中的每一个目标进行分类

camera &＃61; carla.sensor.Camera(&＃39;MyCamera&＃39;, PostProcessing&＃61;&＃39;SemanticSegmentation&＃39;)
camera.set(FOV&＃61;90.0)
camera.set_image_size(800, 600)
camera.set_position(x&＃61;0.30, y&＃61;0, z&＃61;1.30)
camera.set_rotation(pitch&＃61;0, yaw&＃61;0, roll&＃61;0)carla_settings.add_sensor(camera)

激光雷达&＃xff1a;一个旋转的激光雷达&＃xff0c;投射出周围的三维点云

lidar &＃61; carla.sensor.Lidar(&＃39;MyLidar&＃39;)
lidar.set(Channels&＃61;32,Range&＃61;50,PointsPerSecond&＃61;100000,RotationFrequency&＃61;10,UpperFovLimit&＃61;10,LowerFovLimit&＃61;-30)
lidar.set_position(x&＃61;0, y&＃61;0, z&＃61;1.40)
lidar.set_rotation(pitch&＃61;0, yaw&＃61;0, roll&＃61;0)carla_settings.add_sensor(lidar)

benchmark agent:

agent 和 experiment suite 都需要用户定义

# We instantiate a forward agent, a simple policy that just set
# acceleration as 0.9 and steering as zero
agent &＃61; ForwardAgent()# We instantiate an experiment suite. Basically a set of experiments
# that are going to be evaluated on this benchmark.
experiment_suite &＃61; BasicExperimentSuite(city_name)# Now actually run the driving_benchmark
# Besides the agent and experiment suite we should send
# the city name ( Town01, Town02) the log
run_driving_benchmark(agent, experiment_suite, city_name,log_name, continue_experiment,host, port)

定义agent&＃xff1a;这里的measurements传送回来的数据是agent的位置&＃xff0c;方向&＃xff0c;动态信息等等&＃xff1b;sensor_data传送回来的信息是摄像头信息和雷达信息&＃xff1b;Directions传送回来的是规划器发送的直行、右转、左转等信息&＃xff1b;target传送回来的是位置和方向信息。函数会根据上述信息返回控制信息&＃xff1a;转向角度、节气门开度、刹车制动力等。

from carla.agent.agent import Agent
from carla.client import VehicleControlclass ForwardAgent(Agent):def run_step(self, measurements, sensor_data, directions, target):"""Function to run a control step in the CARLA vehicle."""control &＃61; VehicleControl()control.throttle &＃61; 0.9return control

定义 experiment suite:

from carla.agent_benchmark.experiment import Experiment
from carla.sensor import Camera
from carla.settings import CarlaSettingsfrom .experiment_suite import ExperimentSuiteclass BasicExperimentSuite(ExperimentSuite):


&＃64;property
def train_weathers(self):return [1]
&＃64;property
def test_weathers(self):return [1]

查看起始位置&＃xff1a;

python view_start_positions.py

增加一些其他的选项&＃xff1a;

# Define the start/end position below as tasks
poses_task0 &＃61; [[7, 3]]
poses_task1 &＃61; [[138, 17]]
poses_task2 &＃61; [[140, 134]]
poses_task3 &＃61; [[140, 134]]
# Concatenate all the tasks
poses_tasks &＃61; [poses_task0, poses_task1 , poses_task1 , poses_task3]
# Add dynamic objects to tasks
vehicles_tasks &＃61; [0, 0, 0, 20]
pedestrians_tasks &＃61; [0, 0, 0, 50]

定义实验向量&＃xff1a;

experiments_vector &＃61; []# The used weathers is the union between test and train weathersfor weather in used_weathers:for iteration in range(len(poses_tasks)):poses &＃61; poses_tasks[iteration]vehicles &＃61; vehicles_tasks[iteration]pedestrians &＃61; pedestrians_tasks[iteration]conditions &＃61; CarlaSettings()conditions.set(SendNonPlayerAgentsInfo&＃61;True,NumberOfVehicles&＃61;vehicles,NumberOfPedestrians&＃61;pedestrians,WeatherId&＃61;weather)# Add all the cameras that were set for this experimentsconditions.add_sensor(camera)experiment &＃61; Experiment()experiment.set(Conditions&＃61;conditions,Poses&＃61;poses,Task&＃61;iteration,Repetitions&＃61;1)experiments_vector.append(experiment)

定义评价标准&＃xff1a;

&＃64;propertydef metrics_parameters(self):"""Property to return the parameters for the metrics moduleCould be redefined depending on the needs of the user."""return {&＃39;intersection_offroad&＃39;: {&＃39;frames_skip&＃39;: 10,&＃39;frames_recount&＃39;: 20,&＃39;threshold&＃39;: 0.3},&＃39;intersection_otherlane&＃39;: {&＃39;frames_skip&＃39;: 10,&＃39;frames_recount&＃39;: 20,&＃39;threshold&＃39;: 0.4},&＃39;collision_other&＃39;: {&＃39;frames_skip&＃39;: 10,&＃39;frames_recount&＃39;: 20,&＃39;threshold&＃39;: 400},&＃39;collision_vehicles&＃39;: {&＃39;frames_skip&＃39;: 10,&＃39;frames_recount&＃39;: 30,&＃39;threshold&＃39;: 400},&＃39;collision_pedestrians&＃39;: {&＃39;frames_skip&＃39;: 5,&＃39;frames_recount&＃39;: 100,&＃39;threshold&＃39;: 300},}