Can human operators safely supervise large AV fleets?
A Data-Informed Analysis of Scalable Supervision for Safety in Autonomous Vehicle Fleets
September 17, 2024
https://arxiv.org/pdf/2409.09500This research investigates the feasibility of using remote human operators to supervise the safety of autonomous vehicles (AVs) in real-world traffic scenarios, particularly focusing on highway merging.
The key points relevant to LLM-based multi-agent systems are:
- Data-driven simulation: The researchers use large-scale, real-world traffic data and microsimulation to create a realistic testing environment for their multi-agent system (traffic).
- Scalability of Supervision: The study explores how the number of required human supervisors changes with factors like AV penetration rate, connectivity of AVs, and cooperative driving behaviors.
- Potential of Connected and Cooperative AVs: Results show that using connected and cooperative AVs significantly reduces the need for human supervision, highlighting their importance in multi-agent system design.
- Pooling Resources: The research demonstrates that pooling supervision tasks across larger areas can further enhance the scalability and efficiency of human oversight in such multi-agent systems.