Pros of Apollo

• Comprehensive autonomous driving platform with real-world deployment focus
• Extensive hardware integration and support for various sensors
• Strong community and industry partnerships

Cons of Apollo

• Steeper learning curve due to complexity
• Higher system requirements for running simulations
• Less focus on visual realism compared to CARLA

Code Comparison

Apollo (C++):

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

CARLA (Python):

def run_step(self, vehicle, sensors_data):
    # Autonomous driving logic implementation
    pass

Key Differences

• Apollo is a full-stack autonomous driving platform, while CARLA is primarily a simulation environment
• Apollo focuses on real-world deployment, whereas CARLA emphasizes research and development
• CARLA offers more realistic graphics and urban environments, while Apollo provides more comprehensive sensor integration

Use Cases

• Apollo: Developing and testing complete autonomous driving systems for real-world deployment
• CARLA: Simulating urban driving scenarios for research, algorithm development, and initial testing of autonomous driving components

Pros of openpilot

• Real-world application: Designed for actual vehicle control and autonomous driving
• Active community: Large user base contributing to development and testing
• Hardware compatibility: Supports various car models and aftermarket devices

Cons of openpilot

• Limited simulation capabilities: Primarily focused on real-world deployment
• Steeper learning curve: Requires understanding of automotive systems and hardware

Code Comparison

openpilot (Python):

def update(self):
  self.sm.update()
  self.state = State.from_dict(self.sm['controlsState'])
  self.enabled = self.state.enabled
  self.active = self.state.active

CARLA (Python):

def tick(self, world, clock):
    self._notifications.tick(world, clock)
    if not self._show_info:
        return
    t = world.get_snapshot().timestamp

Summary

openpilot is a real-world autonomous driving system with broad hardware support and an active community. CARLA, on the other hand, is a powerful simulation environment for developing and testing autonomous driving algorithms. While openpilot excels in practical application, CARLA offers a more controlled and diverse testing environment for research and development purposes.

Pros of AirSim

• Supports multiple platforms (Windows, Linux) and environments (indoor, outdoor)
• Offers physics simulation for drones and ground vehicles
• Provides APIs for various programming languages (C++, Python, C#, Java)

Cons of AirSim

• Less realistic urban environments compared to CARLA
• Smaller community and fewer ready-to-use scenarios
• Limited built-in sensor types

Code Comparison

AirSim Python API example:

import airsim

client = airsim.MultirotorClient()
client.takeoffAsync().join()
client.moveToPositionAsync(-10, 10, -10, 5).join()

CARLA Python API example:

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)

Both simulators offer intuitive APIs for controlling vehicles and accessing sensor data. AirSim focuses on drone simulation and cross-platform support, while CARLA excels in realistic urban environments and traffic scenarios. The choice between them depends on specific project requirements and the type of autonomous systems being developed.

Pros of LGSVL Simulator

• More flexible and customizable environment creation
• Better support for ROS (Robot Operating System) integration
• Easier to set up and use for beginners

Cons of LGSVL Simulator

• Smaller community and less extensive documentation compared to CARLA
• Fewer built-in scenarios and less diverse pre-made assets
• Less frequent updates and maintenance

Code Comparison

LGSVL Simulator (Python API):

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)

CARLA (Python API):

import carla

client = carla.Client('localhost', 2000)
world = client.get_world()
blueprint = world.get_blueprint_library().find('vehicle.lincoln.mkz2017')
vehicle = world.spawn_actor(blueprint, spawn_point)
world.tick()

Both simulators offer Python APIs for vehicle spawning and simulation control. LGSVL's API appears slightly more concise, while CARLA's API provides more granular control over the simulation environment.

Pros of self-driving-car-sim

• Simpler and more lightweight, making it easier to set up and run
• Designed specifically for Udacity's Self-Driving Car Nanodegree, providing a focused learning experience
• Includes a track editor for creating custom environments

Cons of self-driving-car-sim

• Less realistic graphics and physics compared to CARLA
• Limited sensor options and environmental conditions
• Lacks advanced features like pedestrians, traffic, and weather simulation

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
{
    private float steeringAngle;
    private float acceleration;

    void FixedUpdate()
    {
        ApplySteeringAndAcceleration();
    }
}

The code examples show that CARLA uses a Python API for vehicle spawning and control, while self-driving-car-sim uses Unity's C# scripting for car behavior. CARLA's approach offers more flexibility and realism, while self-driving-car-sim's Unity-based system is more accessible for beginners.

Pros of Autoware

• Real-world focused: Designed for actual autonomous vehicle deployment
• Comprehensive: Includes planning, perception, and control modules
• Open-source ecosystem: Large community and extensive ROS integration

Cons of Autoware

• Steeper learning curve: More complex setup and configuration
• Hardware requirements: Needs powerful hardware for full functionality
• Limited simulation capabilities: Primarily focused on real-world applications

Code Comparison

CARLA (Python):

import carla

client = carla.Client('localhost', 2000)
world = client.get_world()
blueprint = world.get_blueprint_library().find('vehicle.tesla.model3')
spawn_point = carla.Transform(carla.Location(x=230, y=195, z=40))
vehicle = world.spawn_actor(blueprint, spawn_point)

Autoware (C++):

#include <autoware_auto_msgs/msg/vehicle_kinematic_state.hpp>
#include <rclcpp/rclcpp.hpp>

rclcpp::Node::SharedPtr node = rclcpp::Node::make_shared("vehicle_node");
auto publisher = node->create_publisher<autoware_auto_msgs::msg::VehicleKinematicState>("/vehicle/state", 10);

Both repositories offer valuable tools for autonomous vehicle development, with CARLA focusing on simulation and Autoware on real-world deployment. CARLA provides an easier entry point for beginners, while Autoware offers a more comprehensive solution for production-ready autonomous systems.