Pros of Apollo

• More comprehensive simulation environment with advanced scenario generation
• Stronger support for HD maps and localization
• Extensive hardware integration capabilities, including LiDAR and radar

Cons of Apollo

• Steeper learning curve due to complex architecture
• Less modular design, making it harder to customize specific components
• More resource-intensive, requiring higher-end hardware for optimal performance

Code Comparison

Apollo (C++):

void Planning::RunOnce(const LocalView& local_view,
                       ADCTrajectory* const trajectory_pb) {
  const double start_timestamp = Clock::NowInSeconds();
  // ... (planning logic)
  const double end_timestamp = Clock::NowInSeconds();
}

Autoware (C++):

bool PlanningNode::on_timer() {
  // ... (planning logic)
  auto trajectory = planner_->planTrajectory(current_pose_, goal_pose_);
  publishTrajectory(trajectory);
  return true;
}

Both repositories use C++ for core functionality, but Apollo's codebase tends to be more complex and tightly integrated. Autoware's modular design is reflected in its simpler function structure, making it easier for developers to understand and modify specific components.

Pros of openpilot

• More user-friendly and accessible for consumer vehicles
• Active community with frequent updates and contributions
• Designed for real-world deployment on existing cars

Cons of openpilot

• Limited to specific supported vehicle models
• Less flexible for research and custom applications
• Focused primarily on highway driving scenarios

Code Comparison

openpilot (Python):

def update(self):
  if self.sm.updated['carState']:
    self.v_cruise_kph = self.sm['carState'].cruiseState.speed * CV.MS_TO_KPH
    self.v_cruise_cluster_kph = self.sm['carState'].cruiseState.speedCluster * CV.MS_TO_KPH

Autoware (C++):

void update()
{
  if (current_velocity_ptr_->twist.linear.x < param_.stopping_velocity) {
    state_ = State::STOPPED;
  } else {
    state_ = State::DRIVING;
  }
}

Both projects aim to provide autonomous driving capabilities, but they target different use cases. openpilot focuses on enhancing existing consumer vehicles with driver assistance features, while Autoware is a more comprehensive platform for autonomous driving research and development across various vehicle types.

Pros of Autoware

• More established and mature project with a larger community
• Broader range of features and functionalities for autonomous driving
• Better documentation and support resources

Cons of Autoware

• Higher complexity and steeper learning curve
• Requires more computational resources
• Less flexible for customization in specific use cases

Code Comparison

Autoware (C++):

void VehicleInterface::publishVehicleStatus(const ros::Time & stamp)
{
  autoware_vehicle_msgs::VehicleStatus status;
  status.header.stamp = stamp;
  status.twist.linear.x = current_velocity_;
  vehicle_status_pub_.publish(status);
}

Autoware.Auto (C++):

void VehicleInterface::publish_vehicle_state(const std::chrono::system_clock::time_point & stamp)
{
  auto msg = autoware_auto_msgs::msg::VehicleStateReport();
  msg.stamp = stamp;
  msg.velocity_mps = current_velocity_;
  vehicle_state_pub_->publish(msg);
}

The code comparison shows similar functionality but highlights differences in message types, naming conventions, and time handling between the two projects. Autoware.Auto uses more modern C++ features and follows a slightly different coding style.

Pros of CARLA

• More realistic and visually appealing simulation environment
• Extensive sensor suite and weather conditions for diverse testing scenarios
• Built-in Python API for easier integration and scripting

Cons of CARLA

• Higher computational requirements due to advanced graphics
• Limited focus on full autonomous driving stack compared to Autoware
• Steeper learning curve for users new to Unreal Engine

Code Comparison

CARLA (Python API):

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)

Autoware (ROS2 launch file):

<launch>
  <include file="$(find-pkg-share autoware_launch)/launch/planning_simulator.launch.xml">
    <arg name="vehicle_model" value="lexus"/>
    <arg name="sensor_model" value="aip_xx1"/>
  </include>
</launch>

Pros of LGSVL Simulator

• Focused on simulation, providing a more comprehensive virtual testing environment
• Supports multiple autonomous driving platforms, not limited to Autoware
• Offers a user-friendly interface for scenario creation and testing

Cons of LGSVL Simulator

• Less integrated with real-world autonomous driving systems
• Requires more setup and configuration for use with specific platforms
• May have a steeper learning curve for users new to simulation environments

Code Comparison

Autoware (C++):

void VehicleInterface::publishVehicleStatus(const ros::Time& stamp)
{
  autoware_msgs::VehicleStatus msg;
  msg.header.frame_id = "base_link";
  msg.header.stamp = stamp;
  // ... (additional code)
}

LGSVL Simulator (Python):

def on_collision(agent, other, contact):
    print(f"{agent.name} collided with {other.name} at {contact}")
    if config.stop_on_collision:
        sim.stop()

The code snippets demonstrate the different focus areas of each project. Autoware's code relates to publishing vehicle status in a real-world scenario, while LGSVL Simulator's code handles collision events in a simulated environment.