Future intelligent transportation systems are envisioned to evolve toward a long-term mixed-autonomy paradigm, where human-driven vehicles (HVs) and autonomous vehicles (AVs) coexist within highly coupled traffic ecosystems. Such coexistence introduces pronounced heterogeneity, amplified uncertainty, and increasingly intricate interaction dynamics. In this context, it remains fundamentally challenging to simultaneously capture the heterogeneous behavioral distribution shifts arising from dynamic AV penetration, generate diverse yet executable trajectories under strong inter-vehicle coupling, and conduct reliable closed-loop safety and stability diagnostics for rare but high-impact events. To this end, we present risk-constrained diffusion with imitation priors (DRIFT), a mixed-autonomy traffic generation framework which unifies heterogeneity-aware conditional encoding, conditional diffusion-based executable trajectory generation, and progressive adversarial alignment enhanced by risk-aware long-tail feedback, thereby enabling traffic behaviors to be iteratively generated, filtered, selected, and validated within a closed-loop execution pipeline. In addition, a unified evaluation protocol is developed to jointly characterize safety, efficiency, and closed-loop stability across representative traffic scenarios and AV penetration regimes. Experimental results demonstrate that DRIFT achieves a strong safety-efficiency trade-off in closed-loop mixed-autonomy benchmarks, while further revealing the critical influence of candidate executability, online selection, and long-tail feedback on executable traffic evolution.

翻译：未来智能交通系统将向长期混合自主范式演进，其中人类驾驶车辆与自动驾驶车辆在高度耦合的交通生态系统中共存。这种共存状态呈现出显著的异质性、放大的不确定性以及日益复杂的交互动力学。在此背景下，同时捕捉动态自动驾驶渗透带来的异质性行为分布偏移、在强车辆耦合条件下生成多样化的可执行轨迹，以及针对罕见但高影响事件进行可靠的闭环安全与稳定性诊断，仍面临根本性挑战。为此，我们提出基于模仿先验的风险约束扩散方法——一种混合自主交通生成框架，该框架统一了异质性感知条件编码、基于条件扩散的可执行轨迹生成，以及通过风险感知长尾反馈增强的渐进式对抗对齐模块，从而在闭环执行管道中实现交通行为的迭代生成、筛选、选择与验证。此外，我们制定了统一的评估协议，可联合刻画典型交通场景及不同自动驾驶渗透率下的安全性、效率与闭环稳定性。实验结果表明，DRIFT在闭环混合自主基准测试中实现了安全性与效率的强均衡，同时进一步揭示了候选轨迹可执行性、在线选择与长尾反馈对可执行交通演化的关键影响。