Vishnu Tejaa Nandam
Computer science
Hometown: Hyderabad, Telangana, India
Graduation date: Spring 2025
FURI | Summer 2024
Benchmarks for Large Language Model Agents in Mixed Autonomy Traffic
This research aims to develop and benchmark Large Language Model (LLM) autonomous agents in mixed autonomy traffic environments, where human-driven and autonomous vehicles coexist. The objective is to assess the potential of LLM agents to improve traffic flow, reduce congestion, lower emissions, and enhance urban safety. Preliminary assumptions suggest that LLM agents can interpret vast amounts of traffic data and make real-time management decisions, contributing to more efficient and sustainable urban transportation systems. This research aligns with the goals of advancing urban sustainability and safety by leveraging AI technology. Future developments may involve refining the LLM agents’ decision-making algorithms, expanding their application to diverse urban scenarios, and validating their performance with real-world traffic data to ensure robust and reliable applications in various urban settings.
Mentor: Hua Wei
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