Can multiple cameras prevent collisions using AI agents?
Multi-Object Tracking for Collision Avoidance Using Multiple Cameras in Open RAN Networks
This paper demonstrates a system for preventing car collisions using multiple cameras and an Open RAN network. The system detects and tracks objects (cars, pedestrians) from different camera viewpoints. These "tracklets" are sent over the network to an edge server, which combines the data to predict object trajectories and assess collision risk. This is achieved using Particle Filters. The system is tested within the CARLA simulator.
Key points for LLM-based multi-agent systems: This architecture represents a distributed multi-agent system where the CARLA clients act as independent agents, each performing object detection. The edge server acts as a central coordinator fusing information and making predictions. This could be extended by incorporating LLMs for more sophisticated reasoning about object behavior and proactive collision avoidance strategies. The use of a realistic Open RAN network highlights the importance of network connectivity considerations in distributed multi-agent applications. The reliance on simulated environments (CARLA) is a crucial aspect for developing and testing multi-agent systems, especially in safety-critical domains.