How many vehicles optimize collaborative SLAMMOT?
Communication-Efficient Cooperative SLAMMOT via Determining the Number of Collaboration Vehicles
November 27, 2024
https://arxiv.org/pdf/2411.17432This paper proposes a communication-efficient approach to multi-agent simultaneous localization, mapping, and moving object tracking (SLAMMOT) for autonomous vehicles. It focuses on minimizing communication overhead between vehicles while maintaining performance by selectively sharing essential information.
Key points relevant to LLM-based multi-agent systems include the use of:
- Selective communication: Agents only communicate necessary information, similar to how LLMs can be prompted to generate concise responses.
- Dynamic collaboration: Collaboration partners are chosen based on current needs and context, reflecting the dynamic nature of LLM-based agent interactions.
- Feature-based communication: Instead of raw data, agents share compact feature representations, mirroring the use of embeddings in LLM systems.
- Attention mechanisms: The system uses axial attention for feature fusion, a technique commonly used in LLMs for processing sequential data.
- Decentralized architecture: Each agent maintains its own state and interacts with others as needed, a common approach in multi-agent LLM systems.