Traffic simulation, complementing real-world data with a long-tail distribution, allows for effective evaluation and enhancement of the ability of autonomous vehicles to handle accident-prone scenarios. Simulating such safety-critical scenarios is nontrivial, however, from log data that are typically regular scenarios, especially in consideration of dynamic adversarial interactions between the future motions of autonomous vehicles and surrounding traffic participants. To address it, this paper proposes an innovative and efficient strategy, termed IntSim, that explicitly decouples the driving intentions of surrounding actors from their motion planning for realistic and efficient safety-critical simulation. We formulate the adversarial transfer of driving intention as an optimization problem, facilitating extensive exploration of diverse attack behaviors and efficient solution convergence. Simultaneously, intention-conditioned motion planning benefits from powerful deep models and large-scale real-world data, permitting the simulation of realistic motion behaviors for actors. Specially, through adapting driving intentions based on environments, IntSim facilitates the flexible realization of dynamic adversarial interactions with autonomous vehicles. Finally, extensive open-loop and closed-loop experiments on real-world datasets, including nuScenes and Waymo, demonstrate that the proposed IntSim achieves state-of-the-art performance in simulating realistic safety-critical scenarios and further improves planners in handling such scenarios.

翻译：交通仿真通过补充具有长尾分布的真实世界数据，能够有效评估和提升自动驾驶车辆处理易发事故场景的能力。然而，从通常记录常规场景的日志数据中仿真此类安全关键场景并非易事，尤其是在考虑自动驾驶车辆未来运动与周围交通参与者之间动态对抗性交互的情况下。为解决此问题，本文提出了一种创新且高效的策略，称为 IntSim，该策略明确地将周围参与者的驾驶意图与其运动规划解耦，以实现真实且高效的安全关键仿真。我们将驾驶意图的对抗性迁移构建为一个优化问题，从而促进对多样化攻击行为的广泛探索和高效求解收敛。同时，基于意图条件的运动规划受益于强大的深度模型和大规模真实世界数据，允许仿真参与者真实的运动行为。特别地，通过根据环境调整驾驶意图，IntSim 促进了与自动驾驶车辆之间动态对抗性交互的灵活实现。最后，在包括 nuScenes 和 Waymo 在内的真实世界数据集上进行的大量开环和闭环实验表明，所提出的 IntSim 在仿真真实安全关键场景方面实现了最先进的性能，并进一步提升了规划器处理此类场景的能力。