Abstract
The expansion of deployed traffic monitoring systems and information transmission is a crucial step towards increasing the efficiency, reliability and safety of vehicular transportation. Under the current context in terrestrial transportation, the implementation of real-time traffic analysis mechanisms can provide more insight into the network, leading to better informed decisions on how to direct traffic and plan roadways. In the future, as we move towards the integration of fully autonomous vehicles to a transportation network with human drivers, the extraction and processing of real time data will become even more crucial to ensure safe transition. In this paper we present a real time data analysis system for in-flight vehicle detection as an option for the expansion of traffic monitoring. The presented solution is able to perform typical post-flight processing in real time, with minimal computational and power requirements, which allows its implementation on light-weight UAS. It utilizes adaptive segmentation and 3D convolutions that take advantage of the structure of the LiDAR point cloud, to identify vehicles and their respective positions within 3D point cloud segments that may include background clutter. All the necessary positioning information required to run the algorithm are introduced along with a detailed description for the computational steps extracting the desired features from the raw data. We provide the timing constraints for the system and evaluate its performance while considering different optimization variables and computation capabilities.