Using scaled down testing to improve full scale intelligent transportation

• Published in: 2014 IEEE Intelligent Vehicles Symposium Proceedings pp. 655 - 660
• Main Authors: Ozbilgin, Guchan, Kurt, Arda, Ozguner, Umit
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-06-2014
• Subjects: autonomous driving; autonomous parking; autonomous vehicles; Global Positioning System; Intelligent transportation; Mobile robots; parking; Robot sensing systems; scaled-down testing; testbed; Testing; Vehicles
• ISSN: 1931-0587; 2642-7214
• DOI: 10.1109/IVS.2014.6856488

This study illustrates a methodology to reduce the time and effort spent on full-scale Intelligent Transportation System testing, through the use of small-scale testbeds. Scaled down testing platforms enable the researchers to implement, compare, and assess different architectures for intelligent transportation by deploying hardware-in-the-loop (HIL) simulation and testing, giving strong indications on the performance and high-level behavior of such systems at full scale. The performance of the scaled down testing is illustrated using a specific example based on an autonomous parking. The approach is demonstrated on intelligent transportation system testbed in The Ohio State University Control and Intelligent Transportation Research Laboratory. The detailed experimental results show the applicability and robustness of the proposed system.