AI 中文总结
该研究针对端到端驾驶智能体违反交通规则的问题，提出无需重训的神经符号安全护卫，在Fail2Drive和Bench2Drive基准上使成功率提15%、安全碰撞降53%且保留原驾驶分数。
AI 中文摘要
现代端到端驾驶智能体可实现较高的平均性能，但仍会违反人类驾驶员绝不会忽视的基本交通规则。其原因是结构性的：它们学习的是统计模式，而非保障安全驾驶的物理条件，导致决策过程不透明且安全约束无法执行。我们提出一种神经符号安全护卫，这是一个轻量级模块，可附加到已训练智能体的最终命令接口。在命令送达车辆前，它会对照显式安全规则检查命令，仅在必要时将其替换为最近的安全替代方案。每次干预可直接执行且可追溯至触发它的规则，而护卫本身无需重新训练，也不添加任何学习组件。以最先进的TransFuser v6（TFv6）为案例，在长尾基准Fail2Drive和Bench2Drive上评估，该护卫将成功率提升15%，安全关键碰撞最多减少53%，同时保留原始驾驶分数。
英文摘要
Modern end-to-end driving agents can achieve high average performance yet still violate basic traffic rules that a human driver would never miss. The reason is structural: they learn statistical patterns rather than the physical conditions that guarantee safe driving, leaving their decision-making process opaque and safety constraints unenforced. We introduce a neuro-symbolic safety guard, a lightweight module that attaches to the final command interface of an already-trained agent. Immediately before a command reaches the vehicle, it checks the command against explicit safety rules and, only when necessary, replaces it with the nearest safe alternative. Each intervention is directly executable and traceable to the rule that triggered it, while the guard itself requires no retraining and adds no learned component. Evaluated on the long-tail benchmarks Fail2Drive and Bench2Drive using the state-of-the-art TransFuser v6 (TFv6) as a case study, the guard improves Success Rate by 15% and reduces safety-critical collisions by up to 53%, while preserving the original Driving Score.
