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 anegocentric strategycharacterized 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 afast-follower strategythat 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, theprice 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.

Offline reinforcement learning has emerged as a promising technology by enhancing its practicality through the use of pre-collected large datasets. Despite its practical benefits, most algorithm development research in offline reinforcement learning still relies on game tasks with synthetic datasets. To address such limitations, this paper provides autonomous driving datasets and benchmarks for offline reinforcement learning research. We provide 19 datasets, including real-world human driver’s datasets, and seven popular offline reinforcement learning algorithms in three realistic driving scenarios. We also provide a unified decision-making process model that can operate effectively across different scenarios, serving as a reference framework in algorithm design. Our research lays the groundwork for further collaborations in the community to explore practical aspects of existing reinforcement learning methods. Dataset and codes can be found in https://sites.google.com/view/ad4rl.