Pros of nvidia-docker

• Focuses on containerization and GPU acceleration for AI/ML workloads
• Provides seamless integration with NVIDIA GPUs in Docker environments
• Widely adopted in enterprise and cloud computing scenarios

Cons of nvidia-docker

• Limited to Docker and NVIDIA GPU ecosystems
• Less emphasis on autonomous driving research compared to research
• Requires more setup and configuration for specific use cases

Code Comparison

research:

def calc_curvature(v_ego, angle_steers, angle_offset=0):
    deg_to_rad = np.pi/180.
    slip_fator = 0.0014 # slip factor obtained from real data
    steer_ratio = 15.3  # from http://www.edmunds.com/acura/ilx/2016/road-test-specs/
    wheel_base = 2.67   # from http://www.edmunds.com/acura/ilx/2016/sedan/features-specs/

    angle_steers_rad = (angle_steers - angle_offset) * deg_to_rad
    curvature = angle_steers_rad/(steer_ratio * wheel_base * (1. + slip_fator * v_ego**2))
    return curvature

nvidia-docker:

FROM nvidia/cuda:11.0-base
LABEL com.nvidia.volumes.needed="nvidia_driver"
ENV PATH /usr/local/nvidia/bin:/usr/local/cuda/bin:${PATH}
ENV LD_LIBRARY_PATH /usr/local/nvidia/lib:/usr/local/nvidia/lib64
ENV NVIDIA_VISIBLE_DEVICES all
ENV NVIDIA_DRIVER_CAPABILITIES compute,utility

Pros of Apollo

• Comprehensive, production-ready autonomous driving platform
• Extensive documentation and community support
• Regular updates and active development

Cons of Apollo

• Steeper learning curve due to complexity
• Requires more computational resources
• Larger codebase, potentially harder to customize

Code Comparison

Apollo (C++):

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

research (Python):

def plan(self, cs, sm):
    # Simpler planning approach
    return self.planner.plan(cs, sm)

Key Differences

• Apollo: Full-stack solution for autonomous driving
• research: Focused on research and experimentation
• Apollo: More structured, enterprise-level approach
• research: Lightweight, easier to understand and modify

Use Cases

• Apollo: Suitable for large-scale autonomous vehicle projects
• research: Ideal for researchers and hobbyists exploring self-driving technology

Community and Support

• Apollo: Large community, corporate backing
• research: Smaller community, more DIY-oriented

Both projects contribute significantly to autonomous driving technology, with Apollo offering a more comprehensive solution and research providing a simpler, more accessible approach for experimentation and learning.

Pros of Autoware

• More comprehensive and production-ready autonomous driving stack
• Larger community and industry support, including ROS integration
• Extensive documentation and tutorials for easier adoption

Cons of Autoware

• Higher complexity and steeper learning curve
• Requires more computational resources and hardware
• Less focus on end-to-end learning approaches

Code Comparison

research:

def process_model(model, img_in, state_in):
    x = model.forward(img_in, state_in)
    return x

Autoware:

void PlanningNode::run()
{
  rclcpp::Rate rate(config_.update_rate);
  while (rclcpp::ok()) {
    updatePlan();
    rate.sleep();
  }
}

The research code snippet shows a simple model processing function, while the Autoware code demonstrates a more complex planning node implementation in C++, reflecting the differences in scope and complexity between the two projects.

Pros of self-driving-car

• More comprehensive curriculum covering various aspects of autonomous driving
• Includes real-world datasets and challenges for practical learning
• Active community engagement and contributions from students and professionals

Cons of self-driving-car

• Less focused on a specific implementation, which may be overwhelming for beginners
• Requires more time investment to work through all materials and projects

Code Comparison

research:

def update_status():
    if state == STARTUP:
        # startup procedure
    elif state == DISENGAGED:
        # disengaged behavior
    elif state == ENGAGED:
        # engaged behavior

self-driving-car:

def process_image(image):
    # Preprocess image
    processed = preprocess(image)
    # Detect lane lines
    lanes = detect_lanes(processed)
    # Determine steering angle
    angle = calculate_steering(lanes)
    return angle

The research code focuses on state management for an autonomous system, while the self-driving-car code demonstrates image processing for lane detection and steering control. This reflects the different approaches of the two repositories, with research emphasizing system architecture and self-driving-car focusing on specific autonomous driving tasks.

Pros of CARLA

• More comprehensive and feature-rich simulation environment
• Active development with regular updates and community support
• Extensive documentation and tutorials for easier adoption

Cons of CARLA

• Higher system requirements and more complex setup process
• Steeper learning curve for beginners
• Potentially slower execution due to its comprehensive nature

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)

commaai/research example (Python):

from selfdrive.car.honda.interface import CarInterface
from selfdrive.controls.lib.drive_helpers import create_event

CI = CarInterface(CP, CarController)
can_sends = CI.apply(CC)

The CARLA code demonstrates setting up a simulation environment and spawning a vehicle, while the commaai/research code shows interfacing with a specific car model and applying control commands. CARLA provides a more generalized simulation framework, while commaai/research focuses on real-world vehicle integration.

Pros of AirSim

• More comprehensive simulation environment for autonomous systems
• Better documentation and community support
• Offers photorealistic environments and physics simulation

Cons of AirSim

• Steeper learning curve due to complexity
• Requires more computational resources
• Less focused on real-world driving scenarios

Code Comparison

AirSim (Python API example):

import airsim

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

research (OpenPilot example):

from selfdrive.car.honda.interface import CarInterface
from selfdrive.controls.lib.longcontrol import LongControl

CI = CarInterface(CP, CarController, CarState)
long_control = LongControl(CP, CI.compute_gb)

Both repositories focus on autonomous systems, but AirSim provides a more general-purpose simulation environment, while research is specifically tailored for real-world driving scenarios. AirSim offers more flexibility and realism in simulations, but research is more directly applicable to on-road autonomous driving development.