通过结构保持方法实现车辆实时地理围栏，精度与安全性双提升
PCARNN-DCBF: Minimal-Intervention Geofence Enforcement for Ground Vehicles
- 用物理编码的神经网络保持车辆动力学结构
- 实现实时二次规划求解，有效处理高阶动态与执行器饱和
- 在CARLA仿真中优于传统模型和通用神经网络
地面车辆的运行时地理围栏正成为约束自动驾驶运行设计域（ODD）的关键技术。现有方案难以兼顾高精度学习与可验证控制的结构需求。本文提出PCARNN-DCBF，将物理编码的控制仿射残差神经网络与基于前瞻的离散控制屏障函数相结合。该方法显式保留车辆动力学的控制仿射结构，确保优化过程的可靠性。由此支持通过实时二次规划（QP）实现多边形区域保持约束，能有效处理高相对阶并缓解执行器饱和问题。在CARLA仿真中，针对电动与内燃平台的实验表明，该结构保持方法显著优于解析模型与非结构化神经基线。
原文摘要 · Abstract (English)
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.
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