Can V2V networks improve autonomous vehicle safety in occluded scenarios?
An End-to-End Collaborative Learning Approach for Connected Autonomous Vehicles in Occluded Scenarios
December 12, 2024
https://arxiv.org/pdf/2412.08562This paper proposes a collaborative method for autonomous vehicles (CAVs) to navigate safely through occluded intersections (like blind corners or summits). Instead of relying on pre-programmed rules or individual learning, CAVs share compressed LiDAR data via a V2V network. This shared information helps each vehicle understand the overall scene better and make smarter decisions, even when other vehicles are hidden from direct view.
Key points for LLM-based multi-agent systems:
- Decentralized Perception: Each agent (CAV) builds its own understanding of the environment based on local sensor data and information received from its neighbors.
- Compressed Communication: Agents share compressed representations of their sensor data (LiDAR features), crucial for bandwidth efficiency in real-world applications.
- Collaborative Learning: Agents learn to cooperate through Multi-agent Proximal Policy Optimization (MAPPO), a reinforcement learning technique that enables decentralized decision-making while leveraging centralized training.
- Resilience to Noise: The system shows robustness against sensor noise and data dropout, essential for real-world deployment.