Generating safety-critical scenarios is essential for validating the robustness of autonomous driving systems, yet existing methods often struggle to produce collisions that are both realistic and diverse while ensuring explicit interaction logic among traffic participants. This paper presents a novel framework for traffic-flow level safety-critical scenario generation via multi-objective Monte Carlo Tree Search (MCTS). We reframe trajectory feasibility and naturalistic behavior as optimization objectives within a unified evaluation function, enabling the discovery of diverse collision events without compromising realism. A hybrid Upper Confidence Bound (UCB) and Lower Confidence Bound (LCB) search strategy is introduced to balance exploratory efficiency with risk-averse decision-making. Furthermore, our method is map-agnostic and supports interactive scenario generation with each vehicle individually powered by SUMO's microscopic traffic models, enabling realistic agent behaviors in arbitrary geographic locations imported from OpenStreetMap. We validate our approach across four high-risk accident zones in Hong Kong's complex urban environments. Experimental results demonstrate that our framework achieves an 85\% collision failure rate while generating trajectories with superior feasibility and comfort metrics. The resulting scenarios exhibit greater complexity, as evidenced by increased vehicle mileage and CO\(_2\) emissions. Our work provides a principled solution for stress testing autonomous vehicles through the generation of realistic yet infrequent corner cases at traffic-flow level.

翻译：生成安全关键场景对于验证自动驾驶系统的鲁棒性至关重要，然而现有方法往往难以在确保交通参与者间显式交互逻辑的同时，产生既真实又多样化的碰撞事件。本文提出一种基于多目标蒙特卡洛树搜索（MCTS）的交通流层面安全关键场景生成框架。我们将轨迹可行性与拟人化行为重新定义为统一评估函数中的优化目标，从而在不牺牲真实性的前提下发现多样化的碰撞事件。引入混合上置信界（UCB）与下置信界（LCB）搜索策略，以平衡探索效率与风险规避决策。此外，本方法具有地图无关性，并支持交互式场景生成——每辆车均由SUMO微观交通模型独立驱动，可在从OpenStreetMap导入的任意地理位置中实现真实的智能体行为。我们在香港复杂城市环境中的四个高风险事故区域验证了所提方法。实验结果表明，该框架在生成具有更优可行性与舒适度指标的轨迹同时，实现了85%的碰撞触发率。生成场景展现出更高复杂度，具体表现为车辆行驶里程与CO\(_2\)排放量的显著增加。本研究通过交通流层面生成真实但偶发的极端案例，为自动驾驶系统的压力测试提供了理论严谨的解决方案。