Price of the Autonomous Strategy With Reinforcement Learning in Mixed-Autonomy Traffic Networks

초록

With the increasing focus on research on autonomous driving, road environments have evolved into mixed-autonomy traffic networks. In this context, developing an autonomous strategy that can reduce societal costs is important because autonomous vehicles have a direct impact on the entire traffic network. Deep reinforcement learning (RL), a promising autonomous decision-making process, typically leads to an egocentric strategy characterized by a static target and disregards rapidly changing traffic conditions. However, this approach can incur significant societal costs in complex traffic scenarios. In this study, we propose a fast-follower strategy that effectively reduces societal costs in a mixed-autonomy traffic network by dynamically adjusting the reward standards to accommodate varying traffic conditions. To assess the impact of autonomous strategies on transportation networks, we introduce a novel metric, the price of autonomous strategy (PoAS), which is designed to quantify the societal costs associated with autonomous decision-making. Additionally, we provide a traffic-aware analysis using PoAS to identify the driving conditions under which the fast-follower strategy results in a lower societal cost than the egocentric strategy. This theoretical analysis is validated using PoAS comparisons across various societal metrics and traffic conditions. The simulation results confirm that the fast-follower strategy outperforms other autonomous strategies in mixed and fully autonomous traffic networks.

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