Pros of Autoware

• Comprehensive open-source autonomous driving software stack
• Active community and regular updates
• Supports various sensors and platforms

Cons of Autoware

• Steeper learning curve due to complexity
• May require more computational resources
• Limited pre-trained models compared to Waymo Open Dataset

Code Comparison

Waymo Open Dataset (Python):

import tensorflow as tf
import math
import numpy as np
import itertools

from waymo_open_dataset import dataset_pb2 as open_dataset

Autoware (C++):

#include <ros/ros.h>
#include <sensor_msgs/PointCloud2.h>
#include <pcl_ros/point_cloud.h>
#include <pcl/point_types.h>
#include <autoware_msgs/DetectedObjectArray.h>

The Waymo Open Dataset focuses on providing a large-scale dataset for autonomous driving research, while Autoware offers a complete software stack for autonomous vehicle development. Waymo's repository primarily contains tools for data handling and evaluation, whereas Autoware includes modules for perception, planning, and control. The code snippets reflect this difference, with Waymo's code centered around dataset manipulation and Autoware's code demonstrating ROS integration and sensor data processing.

Pros of Apollo

• Comprehensive autonomous driving platform with full-stack capabilities
• Active development and regular updates from a large community
• Extensive documentation and tutorials for implementation

Cons of Apollo

• Steeper learning curve due to complex architecture
• Requires more computational resources for deployment
• Limited to specific hardware configurations

Code Comparison

Apollo (C++):

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

Waymo Open Dataset (Python):

def process_example(example):
    # Data processing and feature extraction
    return processed_features

Key Differences

• Apollo focuses on end-to-end autonomous driving solutions, while Waymo Open Dataset primarily provides data and tools for research
• Apollo offers a complete software stack, whereas Waymo Open Dataset is centered around dataset access and evaluation metrics
• Apollo uses C++ as its primary language, while Waymo Open Dataset utilizes Python for data processing and analysis

Use Cases

• Apollo: Developing and testing full autonomous driving systems
• Waymo Open Dataset: Research, benchmarking, and algorithm development for perception and prediction tasks

Pros of openpilot

• Open-source and actively maintained by a community of developers
• Designed for real-world deployment on consumer vehicles
• Supports a wide range of car models and can be installed by end-users

Cons of openpilot

• Limited to specific hardware configurations (e.g., comma devices)
• Smaller dataset compared to Waymo Open Dataset
• May have less rigorous safety testing and validation processes

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.sm['carState'].cruiseState.speedCluster * CV.MS_TO_KPH

Waymo Open Dataset:

def parse_range_image_and_camera_projection(range_image, camera_projection):
  return range_image_utils.parse_range_image_and_camera_projection(
      range_image, camera_projection)

The code snippets show different focuses: openpilot deals with real-time vehicle control, while Waymo Open Dataset processes sensor data for research and development purposes.

Pros of Detectron2

• Comprehensive library for object detection and segmentation tasks
• Modular design allows easy customization and extension
• Extensive documentation and community support

Cons of Detectron2

• Focused primarily on computer vision tasks, less versatile for other domains
• Steeper learning curve for beginners compared to simpler datasets

Code Comparison

Detectron2 (model configuration):

cfg = get_cfg()
cfg.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"))
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5
cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")

Waymo Open Dataset (data loading):

dataset = tf.data.TFRecordDataset(FILENAME, compression_type='')
for data in dataset:
    frame = open_dataset.Frame()
    frame.ParseFromString(bytearray(data.numpy()))

The Waymo Open Dataset provides a large-scale dataset for autonomous driving research, while Detectron2 offers a powerful framework for implementing and experimenting with various computer vision models. Detectron2 is more suitable for researchers and developers working on general object detection and segmentation tasks, while the Waymo Open Dataset is specifically tailored for autonomous driving applications.