Ensure robustness of CMPP under unpredictable agent behaviors

Establish methods and guarantees to ensure the robustness of congestion mitigation path planning (CMPP) when deployed in environments where agents exhibit unpredictable behaviors, including deviations from guidance or local collision-avoidance assumptions, so that overall navigation efficiency and congestion mitigation are preserved despite noncompliant or noisy agent actions.

Background

CMPP provides coarse-level, time-independent route guidance and relies on agents handling local collision avoidance in a decentralized manner. In real-world settings, agents may behave unpredictably due to noise, delays, or noncompliance, which can degrade performance or invalidate implicit assumptions.

The paper explicitly notes that ensuring robustness in such environments is an unresolved issue, motivating the need for methods that maintain CMPP’s effectiveness under uncertainty and variability in agent behavior.