4.1RODec 18, 2020Code
Simulation Environment for Safety Assessment of CEAV Deployment in LindenLevent Guvenc, Bilin Aksun-Guvenc, Xinchen Li et al.
This report presents a simulation environment for pre-deployment testing of the autonomous shuttles that will operate in the Linden Residential Area. An autonomous shuttle deployment was already successfully launched and operated in the city of Columbus and ended recently. This report focuses on the second autonomous shuttle deployment planned to start in December, 2019, using a route that will help to solve first-mile / last-mile mobility challenges in the Linden neighborhood of Columbus by providing free rides between St. Stephens Community House, Douglas Community Recreation Center, Rosewind Resident Council and Linden Transit Center. This document presents simulation testing environments in two open source simulators and a commercial simulator for this residential area route and how they can be used for model-in-the-loop and hardware-in-the-loop simulation testing of autonomous shuttle operation before the actual deployment.
2.2RODec 23, 2020
A Survey of Recent Developments in Collision Avoidance, Collision Warning and Inter-Vehicle Communication SystemsOncu Ararat, Bilin Aksun-Guvenc
This paper presents the state-of-the-art on Collision Avoidance and Collision Warning (CA,CW) systems. Traffic accidents result from driver errors or situations that are unpredictable for the driver. CA,CW systems are developed for reducing traffic accidents and saving peoples lives by warning drivers or taking compensatory action when drivers can not react fast enough. Besides these initiatives, this paper explains the importance of CA,CW driving assistance systems by considering economic issues as well. Technological developments are investigated in the context of finished and ongoing projects all over the world. CA,CW system algorithms are discussed regarding performance criteria such as reliability and strictness. This paper also presents the information on Inter-Vehicle Communication Systems (IVC) which will be a key ingredient of future CA,CW systems.
2.2RODec 23, 2020
State of the Art of Adaptive Cruise Control and Stop and Go SystemsEmre Kural, Tahsin Hacibekir, Bilin Aksun-Guvenc
This paper presents the state of the art of Adaptive Cruise Control (ACC) and Stop and Go systems as well as Intelligent Transportation Systems enhanced with inter vehicle communication. The sensors used in these systems and the level of their current technology are introduced. Simulators related to ACC and Stop and Go (S&G) systems are also surveyed and the MEKAR simulator is presented. Finally, future trends of ACC and Stop and Go systems and their advantages are emphasized.
4.1RODec 23, 2020
SmartShuttle: Model Based Design and Evaluation of Automated On-Demand Shuttles for Solving the First-Mile and Last-Mile Problem in a Smart CitySukru Yaren Gelbal, Bilin Aksun-Guvenc, Levent Guvenc
The final project report for the SmartShuttle sub-project of the Ohio State University is presented in this report. This has been a two year project where the unified, scalable and replicable automated driving architecture introduced by the Automated Driving Lab of the Ohio State University has been further developed, replicated in different vehicles and scaled between different vehicle sizes. A limited scale demonstration was also conducted during the first year of the project. The architecture used was further developed in the second project year including parameter space based low level controller design, perception methods and data collection. Perception sensor and other relevant vehicle data were collected in the second project year. Our approach changed to using soft AVs in a hardware-in-the-loop simulation environment for proof-of-concept testing. Our second year work also had a change of localization from GPS and lidar based SLAM to GPS and map matching using a previously constructed lidar map in a geo-fenced area. An example lidar map was also created. Perception sensor and other collected data and an example lidar map are shared as datasets as further outcomes of the project.