Runtime geofencing for ground vehicles is rapidly emerging as a critical technology for enforcing Operational Design Domains (ODDs). However, existing solutions struggle to reconcile high-fidelity learning with the structural requirements of verifiable control. We address this by introducing PCARNN-DCBF, a novel pipeline integrating a Physics-encoded Control-Affine Residual Neural Network with a preview-based Discrete Control Barrier Function. Unlike generic learned models, PCARNN explicitly preserves the control-affine structure of vehicle dynamics, ensuring the linearity required for reliable optimization. This enables the DCBF to enforce polygonal keep-in constraints via a real-time Quadratic Program (QP) that handles high relative degree and mitigates actuator saturation. Experiments in CARLA across electric and combustion platforms demonstrate that this structure-preserving approach significantly outperforms analytical and unstructured neural baselines.

翻译：地面车辆的运行时地理围栏正迅速成为强制执行操作设计域（ODDs）的关键技术。然而，现有解决方案难以在高保真学习与可验证控制的结构要求之间取得平衡。为此，我们提出了PCARNN-DCBF，这是一种新颖的流程，将物理编码的控制仿射残差神经网络与基于预览的离散控制屏障函数相结合。与通用学习模型不同，PCARNN明确保留了车辆动力学的控制仿射结构，确保了可靠优化所需的线性特性。这使得DCBF能够通过处理高相对度并缓解执行器饱和的实时二次规划（QP）来强制执行多边形保持约束。在CARLA中对电动和燃油动力平台的实验表明，这种结构保持方法显著优于解析和非结构化神经基线。