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mmtracking is a PyTorch video perception framework designed for training and deploying computer vision models that analyze sequential image data. It provides specialized tools for multi-object tracking, video instance segmentation, and a configuration-driven system for managing deep learning models. The project utilizes a deep learning model registry and a configuration-driven pipeline to swap model backbones and detectors without modifying the core codebase. This modular approach allows for the development of custom perception architectures by combining various components and configurations.
Apollo is a comprehensive software stack designed for autonomous vehicle development, providing the necessary components for perception, planning, and control. It functions as a high-performance robotics middleware, utilizing a publish-subscribe data bus to facilitate low-latency communication between distributed modules and hardware sensors. The platform integrates data from cameras, lidar, and radar through a sensor fusion framework to generate a real-time environmental model for navigation. The system features a component-based runtime framework that manages task scheduling and resource al
Autoware is an open-source autonomous driving software platform built on the robotics middleware standard. It provides a comprehensive stack for managing perception, planning, and control, enabling the development and deployment of full-stack autonomous driving software on commercial transport hardware. The platform utilizes a component-based modular architecture that organizes driving functions into isolated, interchangeable nodes. This design is supported by a hardware-abstraction layer and plugin-based sensor integration, which allow the software to interface with diverse hardware configur
Autoware is a modular autonomous driving stack and open-source platform for advanced driver assistance systems. It functions as an integrated operating environment that manages the full pipeline from sensor data processing to vehicle actuation, utilizing the ROS 2 robotics framework for distributed communication and hardware abstraction. The system provides a comprehensive software architecture to enable autonomous driving across various vehicle platforms. It coordinates perception, planning, and control systems to operate vehicles without human intervention. The platform covers several core
UniAD is a unified deep learning framework for autonomous driving that integrates perception, prediction, and planning into a single end-to-end model. It functions as a neural network architecture that maps raw sensor data directly to driving trajectories and motion plans.
The main features of opendrivelab/uniad are: End-to-End Driving Pipelines, Autonomous Driving Stacks, End-to-End Architectures, Multi-Task Vision Training, Perception Module Training, Temporal Feature Aggregation, Planning-Oriented Learning, Motion Planning.
Projects with overlapping indexed features include: open-mmlab/mmtracking — mmtracking is a PyTorch video perception framework designed for training and deploying computer vision models that… autowarefoundation/autoware — Autoware is an open-source autonomous driving software platform built on the robotics middleware standard. It provides… cpfl/autoware — Autoware is a modular autonomous driving stack and open-source platform for advanced driver assistance systems. It… stuckatprototype/racer — Racer is a self-driving remote-controlled car platform that integrates hardware schematics, an autonomous vehicle… apolloauto/apollo — Apollo is a comprehensive software stack designed for autonomous vehicle development, providing the necessary… commaai/openpilot — Openpilot is an open-source driver assistance system that integrates with vehicle control units to provide automated…