一句话：提出 AnA，通过查询接口让规划按需引导感知，并区分意识与注意，提升自动驾驶安全与资源效率。
当前自动驾驶系统的感知模块与驾驶决策脱节，不像人类驾驶那样根据当前驾驶知识选择关注对象。论文提出 AnA，将 ADS 重构为：规划通过查询接口表达对感知的兴趣；查询执行器把查询映射为最优感知任务集合；监控器结合驾驶知识处理异常执行。AnA 区分 awareness 与 attention，使感知资源按需分配。在困难驾驶基准上，AnA 能及时响应对抗事件，碰撞减少 2 倍；计算量降低 44% 且不牺牲驾驶安全。作者认为其效果来自类似人类的 attentive driving，提高了资源比例性。
In an autonomous driving system (ADS), the perception module is crucial to driving safety and efficiency. Unfortunately, the perception in today's ADS remains oblivious to driving decisions, contrasting to how humans drive. Our idea is to refactor ADS so that (1) the ADS guides its perception with the driving knowledge in situ; (2) the perception differentiates between awareness and attention. We propose a system called AnA with three novel mechanisms: (1) a query interface for the planning to express its interest in perception; (2) a query executor that maps queries to an optimal set of perception tasks; (3) a monitor for handling abnormal task executions with driving knowledge. On challenging driving benchmarks, AnA outperforms competitive baselines: it responds to adversarial events timely, reducing collisions by 2x; it reduces compute usage by 44% without compromising driving safety. We attribute AnA's efficacy to its attentive driving, a human-like behavior that improves resource proportionality.