Feature Sharing and Integration for Cooperative Cognition and Perception with Volumetric Sensors
This work addresses the problem of reliable cooperative cognition and perception for autonomous vehicles or similar systems, but it appears incremental as it analyzes and compares existing DFS techniques rather than introducing a new method.
The paper tackles the challenges of limited network resources and data discrepancies in cooperative perception systems for detecting occluded targets and expanding sensing range, finding that Deep Feature Sharing (DFS) methods are significantly less sensitive to GPS noise localization error and offer comparable detection gains to raw information sharing while enabling design flexibility for communication requirements.
The recent advancement in computational and communication systems has led to the introduction of high-performing neural networks and high-speed wireless vehicular communication networks. As a result, new technologies such as cooperative perception and cognition have emerged, addressing the inherent limitations of sensory devices by providing solutions for the detection of partially occluded targets and expanding the sensing range. However, designing a reliable cooperative cognition or perception system requires addressing the challenges caused by limited network resources and discrepancies between the data shared by different sources. In this paper, we examine the requirements, limitations, and performance of different cooperative perception techniques, and present an in-depth analysis of the notion of Deep Feature Sharing (DFS). We explore different cooperative object detection designs and evaluate their performance in terms of average precision. We use the Volony dataset for our experimental study. The results confirm that the DFS methods are significantly less sensitive to the localization error caused by GPS noise. Furthermore, the results attest that detection gain of DFS methods caused by adding more cooperative participants in the scenes is comparable to raw information sharing technique while DFS enables flexibility in design toward satisfying communication requirements.