Cooperative LIDAR Object Detection Via Feature Sharing in Deep Networks

Technology #11564

Key Points

  • A mechanism to improve object detection, for example, environment perception in connected and autonomous vehicle applications
  • Uses novel decentralized parallel frameworks and a new shared data alignment method to allow parallel and cooperative processing of sensed data in multiple locations
  • Can be used with any sensor processing application employing neural networks that have more than one observer of a scene

Abstract

The University of Central Florida invention is an approach that enables “cooperative cognition” by sharing partially processed data (“feature sharing”) from light detection and ranging (LIDAR) sensors among cooperative vehicles. The partially processed data are the features derived from an intermediate layer of a deep neural network. Experimental results show that the approach significantly improves object detection performance while keeping the required communication capacity low compared to sharing raw information methods.