CollaMamba: A Resource-Efficient Framework for Collaborative Perception in Autonomous Systems

**Practical Solutions and Value of CollaMamba Model** **Enhancing Multi-Agent Perception in Autonomous Systems** CollaMamba enhances collaborative perception in autonomous driving and robotics by enabling vehicles and robots to work together effectively. This collaboration improves accuracy and safety in dynamic environments through shared sensory data. **Efficient Data Processing and Resource Management** CollaMamba uses a spatial-temporal state space (SSM) to process cross-agent collaborative perception efficiently. This reduces computational complexity and enhances communication efficiency, ensuring a balance between accuracy and practical resource constraints. **Improving Spatial and Temporal Feature Extraction** The model efficiently captures causal dependencies, processes complex spatial relationships, and refines features using historical data. This approach enhances global scene understanding and accuracy in multi-agent perception tasks. **Performance and Efficiency Gains** CollaMamba surpasses existing solutions by reducing computational overhead by up to 71.9% and communication overhead by 1/64. It delivers significant accuracy improvements, making it highly efficient for real-time applications. **Adaptability in Communication-Challenged Environments** CollaMamba-Miss predicts missing data from neighboring agents, ensuring high performance even in inconsistent communication scenarios. The model remains robust with minimal drops in accuracy, making it suitable for real-world applications. **Advancement in Autonomous Systems** The CollaMamba model represents a significant advancement in autonomous systems by enhancing accuracy, efficiency, and resource management. Its practicality in real-world scenarios makes it a valuable solution for various applications.