An Approach for Reducing Computational Time for Real-Time Autonomous Vehicle Tracking
Taewook Hwang, Seonhee Kim, Sujeong Kim, Gil‐Sang Jang, Jihye Park, Seongha Park, Eric T. Matson, Kyungsup Kim · International Conference on Control, Automation and Systems · 2018
Autonomous vehicles are familiar to public in daily life nowadays. For a recreational purpose, autonomous vehicles such as drones are commonly adopted for people. However with the easy accessibility, those autonomous vehicles can be a threat to anyone. Moreover, to detect and prevent those possible threats, real-time detection and tracking system is required. With the requirements, we propose a real-time communication between post-processing device and autonomous vehicle tracking sensor, which is a radar and a noise reduction method for post-processing. With the proposed method, a Frequency Modulated Continuous Wave (FMCW) radar can be utilized for real-time monitoring of autonomous vehicle. In this paper, we used an audio file recorded through a FMCW radar for distance tracking. The recorded audio data were processed by Inverse Fast Fourier Transformation (IFFT) and noise cancellation. We propose a data selection formula for faster IFFT processing and a noise reduction method in real-time communication. Also we propose a simple Android application to receive the processed data that sent to as distances of the target autonomous vehicle in time in real-time, so that a user can conveniently watch an autonomous vehicle near the radar.