The simulation-based testing is essential for safely implementing autonomous vehicles (AV) on roads, necessitating simulated traffic environments that dynamically interact with the Vehicle Under Test (VUT). This study introduces a VUT-Centered environmental Dynamics Inference (VCDI) model for realistic, interactive, and diverse background traffic simulation. Serving the purpose of AV testing, VCDI employs Transformer-based modules in a conditional trajectory inference framework to simulate VUT-centered driving interaction events. First, the VUT future motion is taken as an augmented model input to bridge the action dependence between VUT and background objects. Second, to enrich the scenario diversity, a Gaussian-distributional cost function module is designed to capture the uncertainty of the VUT's strategy, triggering various scenario evolution. Experimental results validate VCDI's trajectory-level simulation precision which outperforms the state-of-the-art trajectory prediction work. The flexibility of the distributional cost function allows VCDI to provide diverse-yet-realistic scenarios for AV testing. We demonstrate such capability by modifying the anticipation to the VUT's cost-based strategy and thus achieve multiple testing scenarios with explainable background traffic evolution. Codes are available at https://github.com/YNYSNL/VCDI.

翻译：基于仿真的测试对于在道路上安全部署自动驾驶车辆至关重要，这需要能够与被测车辆动态交互的模拟交通环境。本研究提出了一种以被测车辆为中心的环境动态推断模型，用于实现真实、交互且多样化的背景交通仿真。为服务于自动驾驶测试目的，该模型在条件轨迹推断框架中采用基于Transformer的模块，以模拟以被测车辆为中心的驾驶交互事件。首先，将VUT的未来运动作为增强模型输入，以桥接VUT与背景车辆之间的动作依赖关系。其次，为丰富场景多样性，设计了一个高斯分布代价函数模块来捕捉VUT策略的不确定性，从而触发多样化的场景演化。实验结果验证了该模型在轨迹级仿真精度上优于当前最先进的轨迹预测方法。分布代价函数的灵活性使该模型能够为自动驾驶测试提供多样且真实的场景。我们通过修改对VUT基于代价的策略预期来展示这种能力，从而实现了具有可解释背景交通演化的多重测试场景。代码发布于https://github.com/YNYSNL/VCDI。