AutoAgent0：用于安全闭环驾驶的智能体运行时
AutoAgent0: An Agentic Runtime for Safe Closed-Loop Driving
AI总结：
AutoAgent0是一个智能体运行时框架，通过补充检测、规则验证和反馈恢复，提升了自动驾驶在长尾及安全关键场景的闭环鲁棒性，无需重新训练底层策略。
摘要：
安全自动驾驶不仅需要单帧的准确规划，还需要在动态环境中实现可靠的闭环执行。尽管端到端驾驶策略发展迅速，但累积的闭环误差，尤其是在未见场景中，可能会将车辆推向同一策略难以恢复的状态。我们通过将闭环驾驶安全视为测试时执行问题来应对这一挑战。我们引入AutoAgent0，这是一个智能体运行时和 harness 框架，它为现有的驾驶专家补充了互补的检测技能、基于规则的动作验证以及反馈驱动的恢复。为解决不准确的场景感知问题，一个独立的检测模块利用互补感知技能构建场景上下文，而基于规则的验证器在执行前会根据该上下文检查提议的轨迹。当标称轨迹不可靠时，运行时的协调器会激活恢复规划，其中智能体循环从动作原语构建替代动作提议，并使用验证反馈迭代优化它们。该设计将独立场景评估与可行性检查及在线恢复相结合，解决了感知误差和单个专家恢复能力有限的问题。在Fail2Drive和NavSafe-∞上的评估表明，与单个专家执行相比，在长尾和安全关键场景中闭环鲁棒性得到了提升。这些结果表明，运行时监督可以在不重新训练底层策略的情况下，提高多个驾驶专家的闭环鲁棒性。
英文摘要：
Safe autonomous driving requires more than accurate planning at a single frame: it demands reliable closed-loop execution in dynamic environments. Although end-to-end driving policies have advanced rapidly, the accumulated closed-loop errors, especially in unseen scenarios, can push vehicles into states from which the same policy struggles to recover. We address the challenge by treating closed-loop driving safety as a test-time execution problem. We introduce AutoAgent0, an agentic runtime and harness framework that augments existing driving experts with complementary detection skills, rule-based action verification, and feedback-driven recovery. To address inaccurate scene perception, an independent detection module constructs scene context using complementary perception skills, and a rule-based verifier checks proposed trajectories against this context before execution. When the nominal trajectories are unreliable, the orchestrator of the runtime activates Recovery Planning, where an agentic loop constructs alternative action proposals from action primitives and iteratively refines them using verification feedback. This design combines independent scene assessment with feasibility checks and online recovery, addressing both perception errors and the limited recovery capabilities of individual experts. Evaluations on Fail2Drive and NavSafe-$\infty$ demonstrate improved closed-loop robustness over single-expert execution in long-tail and safety-critical scenarios. These results demonstrate that runtime supervision can improve closed-loop robustness across multiple driving experts without retraining the underlying policies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
