Introspective Closed-Loop Perception for Energy-efficient Sensors

Abstract

Task-driven closed-loop perception-sensing systems have shown considerable energy savings over traditional open-loop systems. Prior works on such systems have used simple feedback signals such as object detections and tracking which led to poor perception quality. This paper proposes an improved approach based on perceptual risk. First, a method is proposed to estimate the risk of failure to detect a target of interest. The risk estimate is used as a signal in a feedback system to determine how sensor resources are utilized. Two feedback algorithms are proposed: one based on proportional/integral methods and the other based on 0/1 (bang-bang) methods. These feedback algorithms are compared based on the efficiency with which they use available sensor resources as well as their absolute detection rates. Experiments on two real-world autonomous driving datasets show that the proposed system has better object detection recall and lower marginal cost of prediction than prior work.