Pros of CARLA

• More realistic and detailed urban environments
• Supports multiple sensors and weather conditions
• Offers a Python API for easier integration and control

Cons of CARLA

• Higher system requirements and more complex setup
• Steeper learning curve for beginners
• Limited customization options for non-urban scenarios

Code Comparison

CARLA example (Python):

import carla

client = carla.Client('localhost', 2000)
world = client.get_world()
blueprint = world.get_blueprint_library().find('vehicle.tesla.model3')
spawn_point = world.get_map().get_spawn_points()[0]
vehicle = world.spawn_actor(blueprint, spawn_point)

self-driving-car-sim example (Unity/C#):

public class CarController : MonoBehaviour
{
    public float speed = 0;
    public float acceleration = 0.1f;
    public float steering = 0;
    
    void FixedUpdate()
    {
        speed += acceleration;
        transform.Translate(Vector3.forward * speed * Time.deltaTime);
        transform.Rotate(Vector3.up * steering * Time.deltaTime);
    }
}

The CARLA example demonstrates its Python API for spawning a vehicle, while the self-driving-car-sim example shows a basic car controller in Unity using C#. CARLA offers more advanced simulation capabilities, while self-driving-car-sim provides a simpler, more accessible environment for beginners.

Pros of AirSim

• More comprehensive simulation environment, supporting various vehicles (cars, drones, etc.)
• Advanced physics engine for realistic simulations
• Extensive API support for multiple programming languages

Cons of AirSim

• Steeper learning curve due to its complexity
• Higher system requirements for smooth operation
• Less focused on self-driving cars specifically

Code Comparison

AirSim (Python):

import airsim

client = airsim.CarClient()
client.confirmConnection()
client.enableApiControl(True)
car_controls = airsim.CarControls()
car_controls.throttle = 0.5
car_controls.steering = 0.0
client.setCarControls(car_controls)

self-driving-car-sim (JavaScript):

var throttle = 0.5;
var steering = 0.0;
socket.emit('steer', {
  steering: steering,
  throttle: throttle
});

AirSim offers more detailed control and a richer API, while self-driving-car-sim provides a simpler interface focused on basic car controls. AirSim's code demonstrates its more comprehensive approach to vehicle simulation, whereas self-driving-car-sim's code highlights its straightforward, web-based nature.

Pros of LGSVL Simulator

• More advanced and feature-rich, offering a wider range of sensors and environmental conditions
• Better integration with ROS and Apollo, making it suitable for professional autonomous vehicle development
• Supports multi-agent scenarios, allowing for complex traffic simulations

Cons of LGSVL Simulator

• Steeper learning curve and more complex setup process
• Requires more computational resources to run smoothly
• Less beginner-friendly compared to the Udacity simulator

Code Comparison

LGSVL Simulator (Python API example):

import lgsvl

sim = lgsvl.Simulator(address="localhost", port=8181)
ego_vehicle = sim.add_agent("Lincoln2017MKZ (Apollo 5.0)", lgsvl.AgentType.EGO, state)
sim.run(time_limit=10)

Udacity Self-Driving Car Simulator (JavaScript example):

let car;
function setup() {
  car = new Car(100, 100);
}
function draw() {
  car.update();
  car.display();
}

The LGSVL Simulator code demonstrates its more advanced API with simulator initialization and agent addition, while the Udacity simulator code shows a simpler, more beginner-friendly approach to car simulation.

Pros of openpilot

• Real-world application: Designed for actual vehicles, providing practical autonomous driving capabilities
• Active development: Regularly updated with new features and improvements
• Large community: Extensive user base contributing to development and testing

Cons of openpilot

• Complexity: Requires more technical knowledge to set up and use
• Hardware requirements: Needs specific hardware components for full functionality
• Legal considerations: Usage may be restricted in certain jurisdictions

Code comparison

self-driving-car-sim:

def process_image(image):
    # Image processing for simulation
    processed = cv2.cvtColor(image, cv2.COLOR_RGB2YUV)
    return processed

openpilot:

def process_model_inputs(self, img):
    # Image processing for real-world driving
    img = img[:, :, ::-1]  # RGB to BGR
    img = self.transform(img)
    return img.unsqueeze(0)

The code snippets show different approaches to image processing. self-driving-car-sim focuses on simpler color space conversion, while openpilot employs more complex transformations for real-world scenarios.

Pros of Apollo

• Comprehensive, production-ready autonomous driving platform
• Extensive documentation and active community support
• Supports various hardware configurations and sensor types

Cons of Apollo

• Steeper learning curve due to complexity
• Higher system requirements for running simulations
• More challenging to set up and configure initially

Code Comparison

Apollo (C++):

void Planning::RunOnce(const LocalView& local_view,
                       ADCTrajectory* const trajectory_pb) {
  // Complex planning logic
  // ...
}

Self-driving-car-sim (Python):

def drive(speed, steering_angle):
    # Simple driving logic
    send_control(speed, steering_angle)

Key Differences

• Apollo is a full-fledged autonomous driving platform, while Self-driving-car-sim is a simplified simulator for learning purposes
• Apollo uses C++ for performance, Self-driving-car-sim uses Python for ease of use
• Apollo offers more realistic simulations and real-world testing capabilities
• Self-driving-car-sim is more accessible for beginners and educational purposes

Use Cases

• Apollo: Professional autonomous vehicle development, research, and testing
• Self-driving-car-sim: Learning basic concepts of self-driving cars, prototyping simple algorithms

Community and Support

• Apollo: Large, active community with regular updates and contributions
• Self-driving-car-sim: Smaller community, primarily focused on educational use

Both repositories serve different purposes and target audiences, making direct comparison challenging. The choice between them depends on the user's goals and experience level in autonomous driving development.

Pros of Autoware

• Comprehensive, production-ready autonomous driving stack
• Supports real-world vehicle integration and testing
• Active community and regular updates

Cons of Autoware

• Steeper learning curve due to complexity
• Requires more computational resources
• Less beginner-friendly than self-driving-car-sim

Code Comparison

self-driving-car-sim (Unity C#):

void FixedUpdate()
{
    Vector3 newPosition = transform.position + transform.forward * Time.fixedDeltaTime * speed;
    rb.MovePosition(newPosition);
}

Autoware (C++):

void VehicleInterface::publishVehicleStatus()
{
    autoware_auto_vehicle_msgs::msg::VehicleStatus status;
    status.fuel = fuel_level_;
    status.velocity = current_velocity_;
    vehicle_status_pub_->publish(status);
}

Summary

self-driving-car-sim is a simpler, more accessible option for beginners learning autonomous driving concepts. It provides a Unity-based environment for testing basic algorithms.

Autoware offers a full-fledged autonomous driving platform suitable for real-world applications. It includes advanced features like sensor fusion, localization, and path planning, making it more suitable for professional development and research.

While self-driving-car-sim focuses on simulation and learning, Autoware aims to provide a complete solution for autonomous vehicle development, including hardware integration and real-world testing capabilities.