Pros of PX4-Autopilot

• More comprehensive autopilot system for various types of vehicles (drones, rovers, etc.)
• Larger community and ecosystem with extensive documentation
• Supports a wider range of hardware platforms

Cons of PX4-Autopilot

• Steeper learning curve due to its complexity
• May be overkill for simpler automotive applications
• Less focused on specific car integration compared to Panda

Code Comparison

PX4-Autopilot (C++):

void MulticopterPositionControl::control_position(const float dt)
{
    // Position control logic
    Vector3f pos_error = _pos_sp - _pos;
    Vector3f vel_sp = pos_error.emult(_params.pos_p) + _vel_sp;
    control_velocity(dt, vel_sp);
}

Panda (Python):

def update_controls(self):
    # Simple control update
    self.angle_steers = self.CS.angle_steers
    self.angle_steers_rate = self.CS.angle_steers_rate
    self.yaw_rate = self.CS.yaw_rate
    self.standstill = self.CS.standstill

The code snippets illustrate the difference in complexity and focus between the two projects. PX4-Autopilot deals with more advanced position control algorithms, while Panda focuses on simpler vehicle state updates.

Pros of Apollo

• Comprehensive autonomous driving platform with full-stack capabilities
• Extensive documentation and community support
• Supports various vehicle types and sensor configurations

Cons of Apollo

• Higher complexity and steeper learning curve
• Requires more computational resources
• Less focused on specific hardware integration

Code Comparison

Apollo (C++):

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

Panda (Python):

def update_panda():
  # Simple hardware interface
  # ...

Key Differences

• Apollo is a full autonomous driving stack, while Panda focuses on vehicle interface
• Apollo uses C++ for performance, Panda uses Python for simplicity
• Apollo has a broader scope, Panda is more specialized for specific hardware

Use Cases

• Apollo: Research and development of complete autonomous driving systems
• Panda: Interfacing with vehicle CAN bus and developing ADAS features

Community and Support

• Apollo: Large community, corporate backing, frequent updates
• Panda: Smaller but active community, focused on open-source principles

Hardware Requirements

• Apollo: Requires powerful computing hardware
• Panda: Can run on simpler embedded systems

Pros of Autoware

• More comprehensive autonomous driving stack, including perception, planning, and control
• Larger community and industry support, with contributions from multiple companies
• Designed for full-scale autonomous vehicles, offering more advanced features

Cons of Autoware

• Higher complexity and steeper learning curve
• Requires more computational resources and hardware
• Less focus on consumer-grade vehicles and retrofitting existing cars

Code Comparison

Panda (C):

void safety_rx_hook(CANPacket_t *to_push) {
  int bus = GET_BUS(to_push);
  int addr = GET_ADDR(to_push);
  
  if (bus == 0 && addr == 0x18DAF1D2) {
    // Process CAN message
  }
}

Autoware (C++):

void VehicleInterface::processCanMessage(const can_msgs::msg::Frame::SharedPtr msg) {
  if (msg->id == 0x18DAF1D2) {
    // Process CAN message
    vehicle_status_.updateFromCanFrame(*msg);
  }
}

Both repositories handle CAN messages, but Autoware's implementation is more object-oriented and uses ROS 2 message types.