Learning to Change: Choreographing Mixed Traffic Through Lateral Control and Hierarchical Reinforcement Learning
CoRR(2024)
摘要
The management of mixed traffic that consists of robot vehicles (RVs) and
human-driven vehicles (HVs) at complex intersections presents a multifaceted
challenge. Traditional signal controls often struggle to adapt to dynamic
traffic conditions and heterogeneous vehicle types. Recent advancements have
turned to strategies based on reinforcement learning (RL), leveraging its
model-free nature, real-time operation, and generalizability over different
scenarios. We introduce a hierarchical RL framework to manage mixed traffic
through precise longitudinal and lateral control of RVs. Our proposed
hierarchical framework combines the state-of-the-art mixed traffic control
algorithm as a high level decision maker to improve the performance and
robustness of the whole system. Our experiments demonstrate that the framework
can reduce the average waiting time by up to 54
state-of-the-art mixed traffic control method. When the RV penetration rate
exceeds 60
control programs in terms of the average waiting time for all vehicles at the
intersection.
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