Project Highlights

RDA: Open-Source Platform for Fast Collision Avoidance Model Predictive Control

Project link: https://github.com/hanruihua/rda_ros

RDA planner is a fast and efficient motion planner for autonomous navigation in cluttered environments. The key idea of RDA is to decompose the complex optimization problem into several subproblems by ADMM, which allows the collision avoidance constraints to be computed in parallel for each obstacle to reduce computation time significantly. Key features:

  • Shape aware planner, which can tackle robots and obstacles with arbitrary convex shapes.
  • Highly accurate control achieved through the use of an optimization solver.
  • Support for both static and dynamic obstacles.
  • Fast computation time, which is suitable for real-time applications.
  • Support different types of dynamics, including differential, Ackermann, and omnidirectional robots.

CarlaFLCAV: Open-Source Platform for Design and Verification of Federated Learning Automonous Driving

Project link: https://github.com/SIAT-INVS/CarlaFLCAV

CarlaFLCAV is an open-source FLCAV simulation platform based on CARLA simulator that supports:

  • Multi-modal dataset generation: Including point-cloud, image, radar data with associated calibration, synchronization, and annotation
  • Training and inference: Examples for CAV perception, including object detection, traffic sign detection, and weather classification
  • Various FL frameworks: FedAvg, device selection, noisy aggregation, parameter selection, distillation, and personalization
  • Optimization based modules: Network resource and road sensor pose optimization.

CarlaFLCAV: Open-Source Platform for Design and Verification of Federated Learning Automonous Driving

Project link: https://github.com/MoCAM-ResearchGroup/grandprix

We have organized a competition based on the Macau Grand Prix Circuit and established a virtual racing system. Hundreds of participants are required to apply intelligent control, machine learning, and ROS (Robot Operating System) programming in the virtual environment to compete in a speed race.

  • Visual Perception: The autonomous driving system must accurately perceive its surroundings, including roads, vehicles, pedestrians, and obstacles. This requires the system to effectively process large amounts of visual data from cameras, LiDAR (Light Detection and Ranging), and other sensors, to identify and track surrounding objects and road signs, perform Simultaneous Localization and Mapping (SLAM), understand and predict real-time traffic conditions. Additionally, the system must handle various challenging scenarios such as different weather conditions, changes in lighting, and visual occlusion.
  • Decision Making: The autonomous driving system needs to make appropriate decisions based on real-time environmental and vehicle information, including route selection, speed adjustment, and adherence to traffic rules. This requires the system to consider multiple factors like traffic conditions, road surface status, passenger needs, and safety, and to determine the optimal driving strategy. Moreover, the system must adapt to rapidly changing traffic environments and respond to unexpected situations.
  • Planning and Control: The autonomous driving system must precisely execute decisions, including controlling the vehicle’s acceleration, braking, and steering. This requires the system to monitor the vehicle’s state in real time, adjust control commands as needed, and ensure smooth, safe, and comfortable driving.
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