Development and Motion Analysis of Multiple Scaled Autonomous Vehicles

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The Ohio State University

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Presently, extensive testing has been conducted with a single autonomous vehicle that interacts solely with dynamics and static objects but not with other autonomous vehicles. An analysis of how autonomous vehicles interact with other autonomous vehicles has been rarely done. This research focuses on the development of multiple, scaled-down, simple autonomous cars using Arduino logic and analyzing how they follow a trajectory. The objective of this research is to avoid unsafe and uncomfortable conditions for the consumer when this technology becomes readily available to the public. This will be done by creating small-scale vehicles capable of implementing object avoidance algorithms similar to a full-scale autonomous vehicle. The first step is to focus on developing a vehicle that can interact with static and dynamic objects which are not autonomous, and successfully avoid them. This is analogous to an autonomous vehicle that can avoid objects similar to the current standard for autonomous vehicles. The final step is to develop a controller to follow a trajectory for future use in a head-on collision test. Advanced scenarios will be researched alongside this project. From this research we have developed three vehicles that meet scalability parameters and have the ability to follow a straight-line trajectory. We have also found that the bicycle model with constant velocity is not sufficient for our needs and must include acceleration. We have successfully built scaled down autonomous vehicles to analyze multiple scenarios, but further design for the system controllers is necessary. This research will allow us to understand if certain safety parameters are necessary for autonomous vehicles while contributing to developing these safety algorithms if necessary.



Autonomous Vehicles, Scaled Vehicles, Multiple Vehicles, Development