CARLA（an open urban driving simulator）
阿新 • • 發佈：2019-01-02
介紹：
CARLA包含三個模組的自動駕駛：① 經典的規則化無人駕駛 ② 端對端模仿學習無人駕駛 ③端對端強化學習無人駕駛
CARLA支援感知和控制兩個模組，包含城市堵路（有汽車，建築物，行人和道路指示標誌），CARLA提供世界和智慧體的介面，客戶端API是python命令控制，以類似插槽（socket）的方式連線智慧體和伺服器。客戶端client傳送命令和下層指令，直接命令包括轉向，加速和剎車，下層命令包括控制伺服器的行為和重置模擬器，改變模擬環境和修改感測器引數。CARLA可以調整視覺資訊質量和速度。CARLA有兩個城鎮，TOWN1用來訓練，TOWN2用來測試。CARLA包含許多感測器，有RGB攝像頭，提供深度資訊的攝像頭（該深度資訊和語義分割，CARLA已經做好了，語義分割有12個種類：道路，道路線，交通燈，行人等等）GPS定位感測器，速度加速度感測器和碰撞感測器等等。
在CARLR中存在探索狀態
啟動：
linux系統下安裝各種依賴：
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++-7 ninja-build python python-pip python-dev tzdata sed curl wget unzip autoconf libtool pip install --user setuptools nose2 sudo update-alternatives --install /usr/bin/clang++ clang++ /usr/lib/llvm-5.0/bin/clang++ 101 sudo update-alternatives --install /usr/bin/clang clang /usr/lib/llvm-5.0/bin/clang 101
安裝 Unreal Engine:
git clone --depth=1 -b 4.19 https://github.com/EpicGames/UnrealEngine.git ~/UnrealEngine_4.19
cd ~/UnrealEngine_4.19
./Setup.sh && ./GenerateProjectFiles.sh && make安裝CARLA：
git clone https://github.com/carla-simulator/carla
export UE4_ROOT=~/UnrealEngine_4.19在指定路徑下（下載CARLA的路徑下）：
CarlaUE4.shCARLA預設的TCP介面是2000和2001，可以通過以下命令修改：
-carla-port=N如果需要執行示例樣例：
python example.py修改地圖：
./CarlaUE4.sh /Game/Carla/Maps/Town02配置：
快進訓練時間：
./CarlaUE4.sh -benchmark -fps=5修改攝像頭和感測器的引數：
在Example.CarlaSettings.ini檔案中修改，圖片在伺服器之間以BGRA陣列的格式傳送，使用者也可以自己定義其他格式。
場景終端相機：（一般情況下選擇在python中，後續將不再列出ini的修改）讓整個場景看起來更加真實，在Python中：
camera = carla.sensor.Camera('MyCamera', PostProcessing='SceneFinal')
camera.set(FOV=90.0)
camera.set_image_size(800, 600)
camera.set_position(x=0.30, y=0, z=1.30)
camera.set_rotation(pitch=0, yaw=0, roll=0)
carla_settings.add_sensor(camera)在CarlaSettings.ini中：
[CARLA/Sensor/MyCamera]
SensorType=CAMERA
PostProcessing=SceneFinal
ImageSizeX=800
ImageSizeY=600
FOV=90
PositionX=0.30
PositionY=0
PositionZ=1.30
RotationPitch=0
RotationRoll=0
RotationYaw=0深度地圖相機：
camera = carla.sensor.Camera('MyCamera', PostProcessing='Depth')
camera.set(FOV=90.0)
camera.set_image_size(800, 600)
camera.set_position(x=0.30, y=0, z=1.30)
camera.set_rotation(pitch=0, yaw=0, roll=0)
carla_settings.add_sensor(camera)語義分割相機：將影象中的每一個目標進行分類
camera = carla.sensor.Camera('MyCamera', PostProcessing='SemanticSegmentation')
camera.set(FOV=90.0)
camera.set_image_size(800, 600)
camera.set_position(x=0.30, y=0, z=1.30)
camera.set_rotation(pitch=0, yaw=0, roll=0)
carla_settings.add_sensor(camera)鐳射雷達：一個旋轉的鐳射雷達，投射出周圍的三維點雲
lidar = carla.sensor.Lidar('MyLidar')
lidar.set(
Channels=32,
Range=50,
PointsPerSecond=100000,
RotationFrequency=10,
UpperFovLimit=10,
LowerFovLimit=-30)
lidar.set_position(x=0, y=0, z=1.40)
lidar.set_rotation(pitch=0, yaw=0, roll=0)
carla_settings.add_sensor(lidar)benchmark agent:
agent 和 experiment suite 都需要使用者定義
# We instantiate a forward agent, a simple policy that just set
# acceleration as 0.9 and steering as zero
agent = ForwardAgent()
# We instantiate an experiment suite. Basically a set of experiments
# that are going to be evaluated on this benchmark.
experiment_suite = 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)定義agent：這裡的measurements傳送回來的資料是agent的位置，方向，動態資訊等等；sensor_data傳送回來的資訊是攝像頭資訊和雷達資訊；Directions傳送回來的是規劃器傳送的直行、右轉、左轉等資訊；target傳送回來的是位置和方向資訊。函式會根據上述資訊返回控制資訊：轉向角度、節氣門開度、剎車制動力等。
from carla.agent.agent import Agent
from carla.client import VehicleControl
class ForwardAgent(Agent):
def run_step(self, measurements, sensor_data, directions, target):
"""
Function to run a control step in the CARLA vehicle.
"""
control = VehicleControl()
control.throttle = 0.9
return control定義 experiment suite:
from carla.agent_benchmark.experiment import Experiment
from carla.sensor import Camera
from carla.settings import CarlaSettings
from .experiment_suite import ExperimentSuite
class BasicExperimentSuite(ExperimentSuite):
@property
def train_weathers(self):
return [1]
@property
def test_weathers(self):
return [1]檢視起始位置：
python view_start_positions.py增加一些其他的選項：
# Define the start/end position below as tasks
poses_task0 = [[7, 3]]
poses_task1 = [[138, 17]]
poses_task2 = [[140, 134]]
poses_task3 = [[140, 134]]
# Concatenate all the tasks
poses_tasks = [poses_task0, poses_task1 , poses_task1 , poses_task3]
# Add dynamic objects to tasks
vehicles_tasks = [0, 0, 0, 20]
pedestrians_tasks = [0, 0, 0, 50]定義實驗向量：
experiments_vector = []
# The used weathers is the union between test and train weathers
for weather in used_weathers:
for iteration in range(len(poses_tasks)):
poses = poses_tasks[iteration]
vehicles = vehicles_tasks[iteration]
pedestrians = pedestrians_tasks[iteration]
conditions = CarlaSettings()
conditions.set(
SendNonPlayerAgentsInfo=True,
NumberOfVehicles=vehicles,
NumberOfPedestrians=pedestrians,
WeatherId=weather
)
# Add all the cameras that were set for this experiments
conditions.add_sensor(camera)
experiment = Experiment()
experiment.set(
Conditions=conditions,
Poses=poses,
Task=iteration,
Repetitions=1
)
experiments_vector.append(experiment)定義評價標準：
@property
def metrics_parameters(self):
"""
Property to return the parameters for the metrics module
Could be redefined depending on the needs of the user.
"""
return {
'intersection_offroad': {'frames_skip': 10,
'frames_recount': 20,
'threshold': 0.3
},
'intersection_otherlane': {'frames_skip': 10,
'frames_recount': 20,
'threshold': 0.4
},
'collision_other': {'frames_skip': 10,
'frames_recount': 20,
'threshold': 400
},
'collision_vehicles': {'frames_skip': 10,
'frames_recount': 30,
'threshold': 400
},
'collision_pedestrians': {'frames_skip': 5,
'frames_recount': 100,
'threshold': 300
},
}