CARLA: Close-Loop Evaluation Without Slurm

Hey guys! Ever wondered how to dive into the world of autonomous driving with CARLA but without the hassle of setting up a Slurm cluster? Well, you're in the right place! This guide will walk you through setting up a close-loop evaluation in CARLA on your local workstation, step by step. We'll break down the necessary components and configurations to get your virtual vehicle cruising in no time. Let’s get started!

Understanding Close-Loop Evaluation in CARLA

Before we jump into the specifics, let's quickly clarify what close-loop evaluation means in the context of CARLA. Imagine you’re teaching a self-driving car how to navigate a city. A close-loop system is one where the car's actions directly influence its future environment and decisions. In simpler terms, the car doesn't just react to pre-set scenarios; it actively interacts with the world around it. This is crucial for testing the robustness and adaptability of autonomous driving systems. For those using CARLA 0.9.15, which is built from source, this process involves running several key components that simulate the environment, the vehicle dynamics, and the decision-making algorithms. The goal here is to create a realistic and interactive simulation where the vehicle can drive, sense its surroundings, and react in real-time.

To truly appreciate the intricacies of close-loop evaluation, think about the real-world scenarios a self-driving car might encounter. It's not enough for the car to simply follow a set of instructions in a controlled environment. It needs to handle unexpected events, such as pedestrians crossing the road, sudden changes in weather, or even other vehicles behaving erratically. Close-loop evaluation allows us to simulate these unpredictable situations and see how well our autonomous system responds. This involves a continuous feedback loop where the car's sensors gather data, the driving algorithm processes that data and makes decisions, and the car's actuators execute those decisions. The consequences of these actions then feed back into the system, influencing the next set of decisions. This iterative process is what makes close-loop evaluation so powerful for identifying weaknesses and improving the overall performance of self-driving systems. So, by setting up a close-loop evaluation environment, you’re not just testing your car; you're training it to handle the complexities of the real world. This method is particularly valuable because it goes beyond simple open-loop simulations where actions do not affect future states. Instead, it creates a dynamic, interactive scenario where the vehicle's decisions have direct consequences, mirroring real-world driving conditions. Setting up this kind of environment effectively means ensuring that every component—from the sensors to the actuators—works in harmony to provide a seamless simulation experience.

Components Involved in a CARLA Close-Loop Simulation

Okay, so what exactly are the moving parts in this setup? First off, you've got the CARLA server, which is the heart of the simulation. It’s responsible for rendering the environment, managing the actors (vehicles, pedestrians, etc.), and simulating the physics. Think of it as the stage where your autonomous vehicle will perform. Next, you have the client, which is your agent or controller. This is where your self-driving algorithm lives, making decisions based on sensor data from the CARLA environment. The client communicates with the server to control the vehicle's actions. For example, if your algorithm detects an obstacle, the client will send commands to the server to steer the vehicle away. To observe the vehicle’s behavior in real-time, you'll also need a visualizer. CARLA provides a built-in visualizer that allows you to see what the vehicle sees, monitor its actions, and even view sensor data. This is super helpful for debugging and understanding how your algorithm is performing.

Another critical component is the scenario runner. This tool allows you to define specific scenarios or test cases, such as navigating a roundabout or responding to a pedestrian crossing the street. By using the scenario runner, you can systematically evaluate your autonomous system under various conditions. Additionally, you'll likely need some form of logging and metrics collection. This involves recording data about the vehicle's performance, such as speed, trajectory, and collision rate. Analyzing this data is essential for identifying areas for improvement and validating the effectiveness of your algorithms. In a full-fledged simulation, you might also have components for sensor simulation, such as lidar and camera models, which provide realistic data to the autonomous system. Furthermore, the agent, which houses your autonomous driving algorithm, is a critical component. The agent processes the sensor data, makes decisions, and sends commands to the vehicle. The interaction between these components is what defines the close-loop system. The CARLA server simulates the world, providing sensor data to the agent, which then acts on this data, influencing the future state of the simulation. Understanding these components and how they interact is crucial for setting up and running effective close-loop evaluations. So, with these components in mind, let's move on to setting up your CARLA environment for a close-loop evaluation without Slurm.

Setting Up CARLA Without Slurm

Now, let’s get our hands dirty and set up CARLA for close-loop evaluation without needing Slurm. Since you’re running CARLA 0.9.15 from a source-built version on a local workstation, the process is pretty straightforward. Slurm is typically used for managing resources on a cluster, but we’re doing this locally, so we can skip that part. First things first, you'll need to ensure that CARLA is properly installed and configured on your workstation. This involves compiling the source code, setting up the Python API, and verifying that the CARLA server can run. Once you've got CARLA up and running, the next step is to configure your environment for close-loop simulations. This involves setting up the necessary Python scripts and configurations to control the vehicle and run the simulation.

To begin, you should navigate to the CARLA directory and verify that you can start the CARLA server. This is usually done by running the CarlaUE4.sh executable. Once the server is running, you can then connect to it using a Python client. For close-loop evaluation, you'll need a client that can interact with the server, receive sensor data, make decisions, and send control commands to the vehicle. This client will typically be a Python script that uses the CARLA Python API. Inside this script, you'll define your autonomous driving algorithm and the logic for controlling the vehicle. This might include things like path planning, obstacle avoidance, and speed control. The script will also need to handle the sensor data coming from CARLA, such as camera images, lidar point clouds, and GPS information. One of the key aspects of setting up the close-loop evaluation is defining the environment in which the vehicle will operate. This includes selecting a map, setting weather conditions, and defining traffic scenarios. CARLA provides a variety of maps that you can use, ranging from urban environments to rural roads. You can also customize the weather conditions, such as the time of day, the amount of rain, and the level of fog. To make the simulation more realistic, you can also add traffic by spawning vehicles and pedestrians into the environment. To manage these aspects of the simulation, you’ll use the CARLA Python API to control the server and define the simulation parameters. This will involve writing scripts to set up the environment, control the vehicle, and collect data during the simulation. Once everything is set up, you’ll be able to run the simulation and observe the vehicle’s driving behavior in real-time using the CARLA visualizer. This visual feedback is crucial for debugging your algorithms and ensuring that your autonomous system is performing as expected.

Running the Necessary Components

Okay, let's talk about the specific components you'll need to run to get this show on the road. The first thing you'll want to do is start the CARLA server. This is the backbone of the simulation, handling the world rendering, physics, and actor management. You can usually do this by running the CarlaUE4.sh executable from the CARLA directory. Think of it as launching the virtual stage where your autonomous vehicle will perform. Once the server is up and running, you’ll need to connect your client. This is where your autonomous driving algorithm resides. The client communicates with the server to receive sensor data and send control commands.

To run the client, you’ll typically use a Python script that interacts with the CARLA Python API. This script will handle the logic for controlling the vehicle, processing sensor data, and making driving decisions. When running the client, make sure that you have the necessary Python dependencies installed, including the CARLA Python API. You’ll also need to configure the client to connect to the CARLA server. This usually involves specifying the server’s IP address and port number. The third component you'll want to launch is the CARLA visualizer. This provides a visual representation of the simulation, allowing you to see what the vehicle sees and monitor its behavior in real-time. The visualizer is incredibly helpful for debugging and understanding how your algorithm is performing. To run the visualizer, you’ll typically use the same CarlaUE4.sh executable that you used to start the server, but with a different set of command-line arguments. You can also use the visualizer to control certain aspects of the simulation, such as the viewpoint and the display settings. Finally, if you’re using scenarios to evaluate your autonomous system, you’ll need to run the scenario runner. This tool allows you to define and execute predefined scenarios, such as navigating a roundabout or responding to a pedestrian crossing the street. The scenario runner will automatically set up the environment and trigger the events in the scenario, allowing you to systematically test your algorithm under various conditions. By running these components in concert, you can create a complete close-loop evaluation environment in CARLA, without the need for Slurm. So, with these components in mind, let's dive into the specific code components you need to run.

Identifying the Code Components

Alright, let's break down the specific code components you'll need to run to get your close-loop evaluation up and running in CARLA 0.9.15. Since you're working with a source-built version, you'll have access to the full codebase, which gives you a lot of flexibility but also means you need to know which parts to focus on. The first key component is the CARLA server executable, which, as we mentioned before, is CarlaUE4.sh. This executable is the entry point for the CARLA simulation engine. It handles everything from rendering the environment to simulating the physics of the vehicles and other actors. You'll find this script in the main CARLA directory, and you'll need to run it to start the simulation. Next up is your client-side script. This is where your autonomous driving logic lives. It’s typically a Python script that uses the CARLA Python API to connect to the server, receive sensor data, and send control commands.

This script will likely include modules for sensor management, perception, planning, and control. You'll need to make sure that this script is properly configured to connect to the server and that it can handle the sensor data being sent from CARLA. If you're using the scenario runner, you'll also need to run the scenario runner script. This script is responsible for setting up and executing predefined scenarios, allowing you to test your autonomous system under specific conditions. The scenario runner typically uses a configuration file that defines the scenario parameters, such as the actors involved, the events that should occur, and the metrics that should be collected. Additionally, you may have a separate visualizer script that allows you to observe the simulation in real-time. This script will connect to the CARLA server and display the sensor data and the vehicle's behavior. The visualizer script is incredibly helpful for debugging and understanding how your autonomous system is performing. Besides these main components, you might also have utility scripts for tasks such as data logging, metrics collection, and environment setup. These scripts can help you automate certain tasks and make it easier to analyze the results of your simulations. For example, you might have a script that automatically logs the vehicle's trajectory, speed, and collision rate. Or you might have a script that sets up a specific environment configuration, such as the weather conditions and the traffic density. Identifying and running these code components is crucial for setting up a close-loop evaluation environment in CARLA. So, with these components in mind, let's talk about how to configure your system to observe the vehicle’s driving behavior in real-time within CARLA.

Configuring the System for Real-Time Observation

Now, let’s dive into configuring your system so you can actually see what’s going on in your CARLA simulation in real-time. This is super important because, without real-time feedback, it’s tough to debug and fine-tune your autonomous driving algorithms. Luckily, CARLA provides a built-in visualizer that’s perfect for this purpose. To get started, you'll need to make sure the CARLA server is running. We’ve talked about this before, but it’s worth reiterating: the server is the foundation of the simulation, and it needs to be running before you can visualize anything. Once the server is up, you can connect to it using the CARLA visualizer. The visualizer is essentially a client that displays the simulation environment, sensor data, and vehicle behavior in real-time.

To run the visualizer, you’ll typically use the same CarlaUE4.sh executable that you used to start the server, but with a different set of command-line arguments. Specifically, you’ll want to use the -windowed flag to run the visualizer in a separate window, and you may also want to specify the resolution and graphics settings. Inside the visualizer, you’ll be able to see the simulated world from various perspectives, including the vehicle’s point of view. This is incredibly helpful for understanding what the vehicle “sees” and how it’s reacting to the environment. You can also use the visualizer to monitor sensor data, such as camera images, lidar point clouds, and GPS information. This allows you to verify that the sensors are working correctly and that the data is being processed as expected. In addition to the basic visualization features, CARLA also provides a range of advanced tools for debugging and analysis. For example, you can use the visualizer to display debug information, such as the vehicle’s planned trajectory, the detected obstacles, and the control commands being sent to the vehicle. You can also use the visualizer to record and replay simulations, which is incredibly helpful for analyzing complex scenarios and identifying potential issues. To configure the system for real-time observation, you’ll also need to ensure that your client script is properly set up to send sensor data to the visualizer. This typically involves setting up a communication channel between the client and the visualizer, such as a TCP socket or a shared memory buffer. You’ll also need to format the sensor data in a way that the visualizer can understand, such as converting the data to a common image format or point cloud format. By configuring your system in this way, you’ll be able to observe the vehicle’s driving behavior in real-time and gain valuable insights into the performance of your autonomous system. So, with the system configured, let's summarize the steps to run the close-loop evaluation in CARLA without Slurm.

Steps to Run Close-Loop Evaluation Without Slurm: A Recap

Alright, let’s recap the key steps to running a close-loop evaluation in CARLA without relying on Slurm. This will help solidify your understanding and give you a clear roadmap to follow. First, make sure your CARLA environment is properly set up. Since you're using a source-built version of CARLA 0.9.15, ensure that you've compiled the code, set up the Python API, and verified that the CARLA server can run. This is the foundational step, and if something’s amiss here, the rest won’t work. Next, start the CARLA server. This is the backbone of the simulation, handling the world rendering, physics, and actor management. You can typically do this by running the CarlaUE4.sh executable from the CARLA directory.

Once the server is running, you need to run your client-side script. This is where your autonomous driving algorithm resides. Your script will connect to the server, receive sensor data, make driving decisions, and send control commands to the vehicle. Ensure that your script is configured to connect to the server and that it can handle the sensor data being sent from CARLA. The third crucial step is to launch the CARLA visualizer. This provides a visual representation of the simulation, allowing you to see what the vehicle sees and monitor its behavior in real-time. The visualizer is incredibly helpful for debugging and understanding how your algorithm is performing. You can typically run the visualizer using the same CarlaUE4.sh executable, but with a different set of command-line arguments. If you’re using scenarios to evaluate your autonomous system, make sure to run the scenario runner script. This tool allows you to define and execute predefined scenarios, such as navigating a roundabout or responding to a pedestrian crossing the street. The scenario runner will automatically set up the environment and trigger the events in the scenario, allowing you to systematically test your algorithm under various conditions. Finally, monitor and analyze the results of your simulation. Use the CARLA visualizer to observe the vehicle’s behavior in real-time, and collect data about the vehicle’s performance, such as speed, trajectory, and collision rate. Analyze this data to identify areas for improvement and validate the effectiveness of your algorithms. By following these steps, you can effectively run close-loop evaluations in CARLA without the complexity of Slurm. This approach is perfect for local workstation setups and allows you to iterate quickly on your autonomous driving algorithms. And that's a wrap, guys! You're now equipped with the knowledge to set up and run close-loop evaluations in CARLA without needing a Slurm cluster. Happy simulating!