How can AVs safely navigate unpredictable human drivers?
Heterogeneous Decision Making in Mixed Traffic: Uncertainty-aware Planning and Bounded Rationality
This paper studies how automated vehicles (AVs) and human-driven vehicles (HVs) can make decisions in mixed traffic. It focuses on how an AV's planning algorithm and the HV's bounded rationality (simplified, sub-optimal decision-making) impact overall system efficiency.
For LLM-based multi-agent systems, the key takeaways are: (1) Modeling bounded rationality of other agents is crucial, especially when dealing with "noisy" human-like behavior. (2) The "lookahead" planning horizon of an agent (like the AV) needs to be carefully tuned, as excessively long horizons can worsen performance due to compounding prediction errors (Goodhart's Law). (3) This research provides a theoretical framework for analyzing regret (performance loss compared to optimal) in multi-agent settings, which could be adapted for other LLM-agent interactions. (4) Prioritizing improvements in predicting other agents' actions provides larger performance gains compared to improvements in individual agent decision-making models.