University of Central Florida Research Foundation Inc

Arash Raftari

Yaser Pourmohammadi Fallah

Ehsan Emad Marvasti

Situational awareness as a necessity in connected and autonomous vehicles domain is a subject of significant research in recent years. The driver's safety is directly dependent on robustness, reliability and scalability of such systems. Cooperative mechanisms have provided a solution to improve situational awareness by utilizing communication networks. These mechanisms mitigate problems such as occlusion and sensor range limitation. However, the network capacity is a factor determining the maximum amount of information being shared among cooperative entities. A focus of this work is to reduce the network capacity requirements while maintaining the desirable object detection performance by utilizing and modifying the concept of feature sharing. Described here is a mechanism to further improve object detection performance by utilizing novel decentralized parallel frameworks and a new shared data alignment method to allow parallel and cooperative processing of sensed data in multiple locations yielding significant improvements.

Growing community of inventors

Orlando, FL, United States of America

Arash Raftari