The simulation-based testing is essential for safely implementing autonomous vehicles (AVs) 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. VCDI is built on a Transformer-based trajectory inference model to generate trajectories for background objects. Serving the purpose of AV testing, VCDI additionally considers VUT-centered interactivity and scenario diversity using a conditional inference framework. First, the VUT future motion is taken as an augmented model input to bridge the interaction between VUT and background objects. Second, to enrich the scenario diversity, a Bayesian-network-based cost function module is designed. The module, learned in a distributional form, captures 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 VUT's cost-based strategy and thus achieve multiple testing scenarios with explainable background traffic evolution.

翻译：基于仿真的测试对于在道路上安全实施自动驾驶车辆（AVs）至关重要，这需要与被测车辆（VUT）动态交互的模拟交通环境。本研究提出一种以VUT为中心的环境动态推理（VCDI）模型，用于实现真实、交互且多样化的背景交通仿真。VCDI基于Transformer轨迹推理模型生成背景对象的轨迹。为服务于AV测试目的，VCDI通过条件推理框架额外考虑了以VUT为中心的交互性和场景多样性。首先，将VUT未来运动作为增强模型输入，以桥接VUT与背景对象之间的交互；其次，为丰富场景多样性，设计了一种基于贝叶斯网络的代价函数模块。该模块以分布形式学习，捕捉VUT策略的不确定性，从而触发多样的场景演化。实验结果验证了VCDI在轨迹级仿真精度上优于现有最先进的轨迹预测方法。分布代价函数的灵活性使VCDI能够为AV测试提供多样且真实的场景。我们通过调整对VUT基于代价策略的预期来展示这一能力，从而以可解释的背景交通演化实现多种测试场景。