AnA: An Attentive Autonomous Driving System

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.