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This paper introduces an adaptive potential game (APG) framework for cooperative driving in mixed traffic scenarios, aiming to optimize both individual vehicle utility and overall system efficiency. The framework uses Shapley values to quantify each vehicle's marginal utility and dynamically refines HDV preference estimation by comparing observed behavior with APG-estimated actions. Field tests validate the APG's applicability and demonstrate its superior safety and efficiency compared to other cooperative methods.
Cooperative driving in mixed traffic gets a boost: adaptively learning human driver preferences within a potential game framework significantly improves safety and efficiency in real-world field tests.
Connected autonomous vehicles (CAVs), which represent a significant advancement in autonomous driving technology, have the potential to greatly increase traffic safety and efficiency through cooperative decision-making. However, existing methods often overlook the individual needs and heterogeneity of cooperative participants, making it difficult to transfer them to environments where they coexist with human-driven vehicles (HDVs).To address this challenge, this paper proposes an adaptive potential game (APG) cooperative driving framework. First, the system utility function is established on the basis of a general form of individual utility and its monotonic relationship, allowing for the simultaneous optimization of both individual and system objectives. Second, the Shapley value is introduced to compute each vehicle's marginal utility within the system, allowing its varying impact to be quantified. Finally, the HDV preference estimation is dynamically refined by continuously comparing the observed HDV behavior with the APG's estimated actions, leading to improvements in overall system safety and efficiency. Ablation studies demonstrate that adaptively updating Shapley values and HDV preference estimation significantly improve cooperation success rates in mixed traffic. Comparative experiments further highlight the APG's advantages in terms of safety and efficiency over other cooperative methods. Moreover, the applicability of the approach to real-world scenarios was validated through field tests.
