High-density, unsignalized intersection has always been a bottleneck of efficiency and safety. The emergence of Connected Autonomous Vehicles (CAVs) results in a mixed traffic condition, further increasing the complexity of the transportation system. Against this background, this paper aims to study the intricate and heterogeneous interaction of vehicles and conflict resolution at the high-density, mixed, unsignalized intersection. Theoretical insights about the interaction between CAVs and Human-driven Vehicles (HVs) and the cooperation of CAVs are synthesized, based on which a novel cooperative decision-making framework in heterogeneous mixed traffic is proposed. Normalized Cooperative game is concatenated with Level-k game (NCL game) to generate a system optimal solution. Then Lattice planner generates the optimal and collision-free trajectories for CAVs. To reproduce HVs in mixed traffic, interactions from naturalistic human driving data are extracted as prior knowledge. Non-cooperative game and Inverse Reinforcement Learning (IRL) are integrated to mimic the decision making of heterogeneous HVs. Finally, three cases are conducted to verify the performance of the proposed algorithm, including the comparative analysis with different methods, the case study under different Rates of Penetration (ROP) and the interaction analysis with heterogeneous HVs. It is found that the proposed cooperative decision-making framework is beneficial to the driving conflict resolution and the traffic efficiency improvement of the mixed unsignalized intersection. Besides, due to the consideration of driving heterogeneity, better human-machine interaction and cooperation can be realized in this paper.

翻译：高密度无信号交叉口历来是交通效率与安全的瓶颈。网联自动驾驶车辆的出现导致混合交通条件产生，进一步增加了交通系统的复杂性。在此背景下，本文旨在研究高密度混合无信号交叉口处车辆间复杂异构的相互作用与冲突消解。综合分析了关于网联自动驾驶车辆与人类驾驶车辆相互作用及网联自动驾驶车辆协同行为的理论见解，并据此提出了一种异构混合交通中的新型协同决策框架。将归一化合作博弈与层级k博弈串联，生成系统最优解，随后由晶格规划器为网联自动驾驶车辆生成最优无碰撞轨迹。为在混合交通中复现人类驾驶车辆，从自然驾驶数据中提取交互行为作为先验知识：融合非合作博弈与逆强化学习以模拟异构人类驾驶车辆的决策过程。最终通过三种案例验证算法性能，包括与不同方法的对比分析、不同渗透率下的案例研究以及与异构人类驾驶车辆的交互分析。结果表明，所提出的协同决策框架有利于混合无信号交叉口的驾驶冲突消解与通行效率提升。此外，由于考虑了驾驶异构性，本文实现了更优的人机交互与协同。