self-driving-car

Pros of openpilot

• Active development with frequent updates and improvements
• Supports a wider range of vehicle models
• More advanced features, including lane keeping and adaptive cruise control

Cons of openpilot

• Steeper learning curve for beginners
• Requires specific hardware (e.g., EON, comma two) for full functionality
• More complex installation process

Code Comparison

openpilot:

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.v_cruise_kph

self-driving-car:

def process_image(image):
    image = cv2.resize(image, (200, 66))
    image = cv2.cvtColor(image, cv2.COLOR_RGB2YUV)
    return image

Summary

openpilot is a more advanced and actively maintained project, offering support for a wider range of vehicles and more sophisticated features. However, it comes with a steeper learning curve and specific hardware requirements. self-driving-car, while less feature-rich, may be more accessible for beginners and provides a solid foundation for learning autonomous driving concepts. The code comparison shows openpilot's focus on real-time vehicle control, while self-driving-car emphasizes image processing for perception tasks.

Pros of Autoware

• More comprehensive and production-ready framework for autonomous driving
• Active development with frequent updates and a larger community
• Supports ROS2, providing better performance and real-time capabilities

Cons of Autoware

• Steeper learning curve due to its complexity and extensive features
• Requires more computational resources to run effectively
• Less beginner-friendly compared to the Udacity project

Code Comparison

Self-driving-car (Python):

def process_image(image):
    yuv = cv2.cvtColor(image, cv2.COLOR_RGB2YUV)
    yuv[:,:,0] = cv2.equalizeHist(yuv[:,:,0])
    return cv2.cvtColor(yuv, cv2.COLOR_YUV2RGB)

Autoware (C++):

void LaneDetector::detectLanes(const cv::Mat& image, std::vector<Lane>& lanes) {
    cv::Mat gray, edges;
    cv::cvtColor(image, gray, cv::COLOR_BGR2GRAY);
    cv::Canny(gray, edges, 50, 150);
    // Further processing...
}

The Self-driving-car code focuses on basic image processing, while Autoware's example demonstrates a more complex lane detection algorithm. Autoware's codebase is generally more extensive and modular, reflecting its production-ready nature.

Pros of Autoware

• More comprehensive and production-ready framework for autonomous driving
• Active development with frequent updates and a larger community
• Supports ROS2, providing better performance and real-time capabilities

Cons of Autoware

• Steeper learning curve due to its complexity and extensive features
• Requires more computational resources to run effectively
• Less beginner-friendly compared to the Udacity project

Code Comparison

Self-driving-car (Python):

def process_image(image):
    yuv = cv2.cvtColor(image, cv2.COLOR_RGB2YUV)
    yuv[:,:,0] = cv2.equalizeHist(yuv[:,:,0])
    return cv2.cvtColor(yuv, cv2.COLOR_YUV2RGB)

Autoware (C++):

void LaneDetector::detectLanes(const cv::Mat& image, std::vector<Lane>& lanes) {
    cv::Mat gray, edges;
    cv::cvtColor(image, gray, cv::COLOR_BGR2GRAY);
    cv::Canny(gray, edges, 50, 150);
    // Further processing...
}

The Self-driving-car code focuses on basic image processing, while Autoware's example demonstrates a more complex lane detection algorithm. Autoware's codebase is generally more extensive and modular, reflecting its production-ready nature.

Pros of CARLA

• More advanced and realistic simulation environment
• Actively maintained with regular updates
• Extensive documentation and tutorials

Cons of CARLA

• Steeper learning curve
• Higher system requirements

Code Comparison

CARLA example (Python):

import carla

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

Self-Driving Car example (Python):

from vehicle import Vehicle

car = Vehicle()
car.start_engine()
car.accelerate(0.5)
car.steer(0.2)
car.brake(0.1)

The CARLA example demonstrates a more complex setup with a realistic simulation environment, while the Self-Driving Car example shows a simpler, more abstract approach to vehicle control.

CARLA offers a more comprehensive and realistic simulation platform, making it suitable for advanced research and development. However, it may require more resources and time to set up and use effectively. The Self-Driving Car repository provides a simpler starting point for learning basic concepts but may lack the depth and realism needed for more advanced applications.