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16 changes: 9 additions & 7 deletions 3rd_party_integrations/index.html
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</ul>
<p class="caption"><span class="caption-text">Carla 生态系统</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../ecosys_iss/">ISS 智能驾驶系统</a>
</li>
<li class="toctree-l1"><a class="reference internal" href="../ecosys_ansys/">Ansys 实时雷达模型</a>
</li>
<li class="toctree-l1"><a class="reference internal" href="../tuto_G_rllib_integration/">RLlib 集成</a>
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<div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
<div class="section" itemprop="articleBody">

<h1 id="3rd-party-integrations">3rd Party Integrations</h1>
<div><h1 id="3rd-party-integrations">3rd Party Integrations</h1>
<p>Carla has been developed to integrate with several 3rd party applications in order to maximise its utility and extensability. The following </p>
<ul>
<li><a href="https://carla.readthedocs.io/projects/ros-bridge/en/latest/"><strong>ROS bridge</strong></a></li>
Expand All @@ -288,7 +290,7 @@ <h1 id="3rd-party-integrations">3rd Party Integrations</h1>
<li><a href="../adv_rss/"><strong>RSS</strong></a> </li>
<li><a href="../tuto_G_rllib_integration/"><strong>AWS and RLlib</strong></a></li>
</ul>
<hr />
<hr>
<h2 id="ros-bridge">ROS bridge</h2>
<p><strong>Full documentation of the ROS bridge is found <a href="https://carla.readthedocs.io/projects/ros-bridge/en/latest/"><strong>here</strong></a>.</strong></p>
<p>The ROS bridge enables two-way communication between ROS and Carla. The information from the Carla server is translated to ROS topics. In the same way, the messages sent between nodes in ROS get translated to commands to be applied in Carla.</p>
Expand All @@ -300,18 +302,18 @@ <h2 id="ros-bridge">ROS bridge</h2>
<li>Control of AD agents through steering, throttle and brake.</li>
<li>Control of aspects of the Carla simulation like synchronous mode, playing and pausing the simulation and setting simulation parameters.</li>
</ul>
<hr />
<hr>
<h2 id="sumo">SUMO</h2>
<p>Carla has developed a co-simulation feature with <a href="https://www.eclipse.org/sumo/"><strong>SUMO</strong></a>. This allows to distribute the tasks at will, and exploit the capabilities of each simulation in favour of the user.</p>
<p>Please refer to the full documentation <a href="../adv_sumo/"><strong>here</strong></a>.</p>
<hr />
<hr>
<h2 id="ptv-vissim">PTV Vissim</h2>
<p><a href="https://www.ptvgroup.com/en/solutions/products/ptv-vissim/"><strong>PTV Vissim</strong></a> is a proprietary software package providing a comprehensive traffic simulation solution with a powerful GUI. To use PTV-Vissim with Carla refer to <a href="../adv_ptv/"><strong>this guide</strong></a></p>
<hr />
<hr>
<h2 id="scenic">Scenic</h2>
<p>Scenic is a set of libraries and a language for scenario specification and scene generation. Carla and scenic can work seemlessly together, read <a href="../tuto_G_scenic/"><strong>this guid</strong></a> to understand how to use scenic with Carla. </p>
<p>If you need to learn more about Scenic, then read their <a href="https://scenic-lang.readthedocs.io/en/latest/quickstart.html">"Getting Started with Scenic"</a> guide and have a look at their tutorials for creating <a href="https://scenic-lang.readthedocs.io/en/latest/tutorials/tutorial.html">static</a> and <a href="https://scenic-lang.readthedocs.io/en/latest/tutorials/dynamics.html">dynamic</a> scenarios.</p>
<hr />
<hr>
<h2 id="carsim">CarSIM</h2>
<p>Carla's integration with CarSim allows vehicle controls in Carla to be forwarded to CarSim. CarSim will do all required physics calculations of the vehicle and return the new state to Carla. </p>
<p>Learn how to use Carla alongside CarSIM <a href="../tuto_G_carsim_integration/">here</a>.</p>
Expand All @@ -323,7 +325,7 @@ <h2 id="aws-and-rllib-integration">AWS and RLlib integration</h2>
<p>The RLlib integration brings support between the Ray/RLlib library and Carla, allowing the easy use of the Carla environment for training and inference purposes. Ray is an open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library. Read more about operating Carla on AWS and RLlib <a href="../tuto_G_rllib_integration/"><strong>here</strong></a>.</p>
<h2 id="chrono-physics">Chrono physics</h2>
<p><a href="https://projectchrono.org/"><strong>Chrono</strong></a> is a multi-physics simulation engine providing high realism vehicle dynamics using templates. Carla's Chrono integraion allows Carla users to add Chrono templates to simulate vehicle dynamics. Please refer to the full documentation <a href="../tuto_G_chrono/"><strong>here</strong></a>.</p>
<hr />
<hr></div>

</div>
</div><footer>
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</ul>
<p class="caption"><span class="caption-text">Carla 生态系统</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="/ecosys_iss/">ISS 智能驾驶系统</a>
</li>
<li class="toctree-l1"><a class="reference internal" href="/ecosys_ansys/">Ansys 实时雷达模型</a>
</li>
<li class="toctree-l1"><a class="reference internal" href="/tuto_G_rllib_integration/">RLlib 集成</a>
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28 changes: 15 additions & 13 deletions adv_agents/index.html
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</ul>
<p class="caption"><span class="caption-text">Carla 生态系统</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../ecosys_iss/">ISS 智能驾驶系统</a>
</li>
<li class="toctree-l1"><a class="reference internal" href="../ecosys_ansys/">Ansys 实时雷达模型</a>
</li>
<li class="toctree-l1"><a class="reference internal" href="../tuto_G_rllib_integration/">RLlib 集成</a>
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<div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
<div class="section" itemprop="articleBody">

<h1 id="carla">Carla 智能体</h1>
<div><h1 id="carla">Carla 智能体</h1>
<p>Carla 智能体脚本允许车辆沿着随机的、无限的路线行驶，或者采用最短的路线到达给定的目的地。智能体遵守交通信号灯并对道路上的其他障碍物做出反应。提供三种智能体类型。可以修改目标速度、制动距离、尾随行为等参数。可以根据用户的需要修改 Actor 类或将其用作基类来创建自定义智能体。</p>
<ul>
<li><a href="#overview-of-agent-scripts"><strong>智能体脚本概述</strong></a><ul>
Expand All @@ -309,7 +311,7 @@ <h1 id="carla">Carla 智能体</h1>
</li>
<li><a href="#creating-an-agent"><strong>创建智能体</strong></a></li>
</ul>
<hr />
<hr>
<h2 id="_1">智能体脚本概述</h2>
<p>Carla 智能体中涉及的主要脚本位于<code>PythonAPI/carla/agents/navigation</code>中。它们分为两类； <strong>计划和控制</strong> 和 <strong>智能体行为</strong>。</p>
<h3 id="_2">计划与控制</h3>
Expand All @@ -324,7 +326,7 @@ <h3 id="_3">智能体行为</h3>
<li><strong><code>behavior_agent.py</code>:</strong> 包含一个实现更复杂的 <strong>Behavior Agent</strong> 的类，它可以在尽可能短的距离内到达目标目的地，跟随交通信号灯、标志和速度限制，同时尾随其他车辆。有三种预定义的类型决定了智能体的行为方式。</li>
<li><strong><code>behavior_types.py</code>:</strong> 包含影响 <strong>Behavior Agent</strong> 的行为类型的参数；谨慎、正常和进取。</li>
</ul>
<hr />
<hr>
<h2 id="_4">实现一个智能体</h2>
<p>本节将解释如何在您自己的脚本中使用示例 Carla 智能体类。在本节的最后，您将了解如何运行一个示例脚本来显示不同智能体的运行情况。</p>
<p><strong>1.</strong> 导入要使用的智能体类：</p>
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<p><strong>6.</strong> 您可以检查智能体是否已完成其轨迹并在发生这种情况时执行操作。一旦您的车辆到达目的地，以下代码段将结束仿真：</p>
<pre><code class="language-py">while True：
if agent.done（）：
print(&quot;The taerget has been reached, stopping the simulation&quot;)
print("The taerget has been reached, stopping the simulation")
break

vehicle.apply_control(agent.run_step())
Expand All @@ -362,7 +364,7 @@ <h2 id="_4">实现一个智能体</h2>
<pre><code class="language-py">while True：
if agent.done():
agent.set_destination(random.choice(spawn_points).location)
print(&quot;The target has been reached, searching for another target&quot;)
print("The target has been reached, searching for another target")
vehicle.apply_control(agent.run_step())
</code></pre>
<p><strong>Basic Agent</strong> 提供了一些方法来操纵智能体行为或遵循的程序路线：</p>
Expand All @@ -383,7 +385,7 @@ <h2 id="_4">实现一个智能体</h2>
# 使用行为智能体运行
python3 automatic_control.py --agent=Behavior --behavior=aggressive
</code></pre>
<hr />
<hr>
<h2 id="_5">行为类型</h2>
<p>行为智能体的行为类型在 <code>behavior_types.py</code> 中定义。三个预配置的配置文件是 <strong>'cautious'</strong>、<strong>'normal'</strong> 和 <strong>'aggressive'</strong>。您可以使用设置的配置文件、修改它们或创建您自己的配置文件。可以调整以下变量：</p>
<ul>
Expand Down Expand Up @@ -415,7 +417,7 @@ <h2 id="_6">创建自己的行为类型</h2>
elif behavior == '&lt;type_name&gt;':
self._behavior = &lt;TypeName&gt;()
</code></pre>
<hr />
<hr>
<h2 id="_7">创建智能体</h2>
<p>Carla 智能体只是用户可以运行的智能体类型的示例。用户可以在 <strong>Basic Agent</strong> 的基础上创建自己的智能体。可能性是无止境。每个智能体只需要两个元素，<strong>初始化</strong> 和 <strong>运行步</strong>。</p>
<p>在下面查找自定义智能体的最小布局示例：</p>
Expand All @@ -425,24 +427,24 @@ <h2 id="_7">创建智能体</h2>

class CustomAgent(BasicAgent):
def __init__(self, vehicle, target_speed=20, debug=False):
&quot;&quot;&quot;
"""
:param vehicle: 应用到本地规划器逻辑的actor
:param target_speed: 车辆移动的速度（Km/h）
&quot;&quot;&quot;
"""
super().__init__(target_speed, 调试)

def run_step(self, debug=False):
&quot;&quot;&quot;
"""
执行一步导航
:return: carla.VehicleControl
&quot;&quot;&quot;
"""
# 在每个仿真步骤中采取的行动
control = carla.VehicleControl()
return control
</code></pre>
<p>查看 <code>basic_agent.py</code> 和 <code>behavior_agent.py</code> 脚本以探索它们的结构和功能，以获取有关如何创建自己的更多想法。</p>
<hr />
<p>您可以探索提供的智能体脚本、扩展它们或将它们用作创建自己的基准。如果您对智能体有任何疑问，请随时在 <a href="https://github.com/carla-simulator/carla/discussions/">论坛</a> 发帖。</p>
<hr>
<p>您可以探索提供的智能体脚本、扩展它们或将它们用作创建自己的基准。如果您对智能体有任何疑问，请随时在 <a href="https://github.com/carla-simulator/carla/discussions/">论坛</a> 发帖。</p></div>

</div>
</div><footer>
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</ul>
<p class="caption"><span class="caption-text">Carla 生态系统</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../ecosys_iss/">ISS 智能驾驶系统</a>
</li>
<li class="toctree-l1"><a class="reference internal" href="../ecosys_ansys/">Ansys 实时雷达模型</a>
</li>
<li class="toctree-l1"><a class="reference internal" href="../tuto_G_rllib_integration/">RLlib 集成</a>
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<div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
<div class="section" itemprop="articleBody">

<h1 id="_1">基准性能</h1>
<div><h1 id="_1">基准性能</h1>
<p>我们提供了一个基准测试脚本，使用户能够轻松地分析 Carla 在自己的环境中的性能。该脚本可以配置为运行多种结合不同地图、传感器和天气条件的场景。它报告请求场景下 FPS 的平均值和标准偏差。</p>
<p>在本节中，我们详细介绍了运行基准测试的要求、在哪里可以找到脚本、可用于自定义运行场景的标志以及有关如何运行命令的示例。</p>
<p>我们还包含了单独基准测试的结果，该基准测试在使用不同车辆数量组合、启用物理和/或启用交通管理器时测量 Carla 在特定环境中的性能。结果与使用的 Carla 版本和执行测试的环境一起显示。</p>
<ul>
<li><a href="#基准测试脚本"><strong>基准脚本</strong></a><ul>
<li><a href="#开始之前"><strong>开始之前</strong></a></li>
<li><a href="#概要"><strong>概要</strong></a><ul>
<li><a href="#标识"><strong>标志</strong></a></li>
<li><a href="#%E5%9F%BA%E5%87%86%E6%B5%8B%E8%AF%95%E8%84%9A%E6%9C%AC"><strong>基准脚本</strong></a><ul>
<li><a href="#%E5%BC%80%E5%A7%8B%E4%B9%8B%E5%89%8D"><strong>开始之前</strong></a></li>
<li><a href="#%E6%A6%82%E8%A6%81"><strong>概要</strong></a><ul>
<li><a href="#%E6%A0%87%E8%AF%86"><strong>标志</strong></a></li>
</ul>
</li>
</ul>
</li>
<li><a href="#carla性能报告"><strong>Carla 性能报告</strong></a></li>
<li><a href="#carla%E6%80%A7%E8%83%BD%E6%8A%A5%E5%91%8A"><strong>Carla 性能报告</strong></a></li>
</ul>
<hr />
<hr>
<h2 id="_2">基准测试脚本</h2>
<p>基准脚本可以在<code>PythonAPI/util</code>中找到。它有几个标志可用于自定义要测试的场景，下面的概要中有详细说明。</p>
<h3 id="_3">开始之前</h3>
Expand All @@ -317,8 +319,7 @@ <h3 id="_3">开始之前</h3>
python -m pip install GPUtil
</code></pre>
<h3 id="_4">概要</h3>
<p><code>python3</code> <a href="https://github.com/carla-simulator/carla/blob/master/PythonAPI/util/performance_benchmark.py"><code>performance_benchmark.py</code></a> <a href="#- host-ip_address"><code>[--host HOST]</code></a> <a href="#-port-port"><code>[--port PORT]</code></a> <a href="#-file-filenamemd"><code>[--file FILE]</code></a> <a href="#- Tm值）
[[--ticks TICKS]](#-ticks) [[--sync]](#-sync) [[--async]](#-async)"><code>[--tm]</code></a>
<p><code>python3</code> <a href="https://github.com/carla-simulator/carla/blob/master/PythonAPI/util/performance_benchmark.py"><code>performance_benchmark.py</code></a> <a href="#-%20host-ip_address"><code>[--host HOST]</code></a> <a href="#-port-port"><code>[--port PORT]</code></a> <a href="#-file-filenamemd"><code>[--file FILE]</code></a> <a href="#-%20Tm%E5%80%BC%EF%BC%89%0A%5B%5B--ticks%20TICKS%5D%5D(#-ticks)%20%5B%5B--sync%5D%5D(#-sync)%20%5B%5B--async%5D%5D(#-async)"><code>[--tm]</code></a>
<a href="#-fixed_dt"><code>[--fixed_dt FIXED_DT]</code></a> <a href="#-render_mode"><code>[--render_mode]</code></a>
<a href="#-no_render_mode"><code>[--no_render_mode]</code></a> <a href="#-show_scenarios"><code>[--show_scenarios]</code></a>)
<a href="#-sensors-integer"><code>[--sensors SENSORS [SENSORS ...]]</code></a>
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</blockquote>
<pre><code class="language-sh">python3 performance_benchmark.py --async --render_mode
</code></pre>
<hr />
<hr>
<h2 id="carla">Carla 性能报告</h2>
<p>下表详细说明了在随着车辆数量增加以及启用和/或禁用物理和交通管理器的不同组合运行 Carla 时对平均 FPS 的性能影响。</p>
<ul>
Expand Down Expand Up @@ -559,15 +560,15 @@ <h2 id="carla">Carla 性能报告</h2>
</tr>
</tbody>
</table>
<hr />
<hr>
<p>如果您对性能基准有任何疑问，请不要犹豫，在论坛中发帖。</p>
<div class="build-buttons">
<!-- 最新发布按钮 -->
<p>
<a href="https://github.com/carla-simulator/carla/discussions/" target="_blank" class="btn btn-neutral" title="转到最新的 CARLA 版本">
Carla 论坛</a>
</p>
</div>
</div></div>

</div>
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