Unmanned ground vehicles (UGV) and self-driving cars utilize visual sensors including cameras, Lidars and radars not only for localization and obstacle avoidance purposes but also to generate a 3D map of the surroundings. When an emergency vehicle — such as a fire truck or an ambulance — is approaching, self-driving cars are required to modify their path plan and find a safe spot rapidly. However early detection of a fast approaching emergency vehicle in urban environment is challenging with a visual perception system since it requires direct view without an obstacle in between. To improve the safety of self-driving cars, a localization algorithm is required to maximize the path modification time constraint as well as to minimize location and direction detection time, especially at an intersection in urban environments. To overcome this challenge, we mounted a transducer array on top of a mobile robot and applied beam forming algorithms to predict the location and velocity vector of the remote dynamic vehicle. Even with high uncertainty, this strategy improved time requirement of occupancy grid update which marks all possible unsafe areas to avoid a collision. Two experimental setups of controlled and uncontrolled environments were prepared. Followed by preliminary transducer characteristic analysis in an anechoic chamber, an outdoor experiment with two mobile robots are executed to benchmark the capability of signal processing techniques while both source and observer are in motion.
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ASME 2018 International Mechanical Engineering Congress and Exposition
November 9–15, 2018
Pittsburgh, Pennsylvania, USA
Conference Sponsors:
- ASME
ISBN:
978-0-7918-5203-3
PROCEEDINGS PAPER
On Self-Driving Car Safety: Occupancy Map Modification With Rapid Emergency Vehicle Detection
Akin Tatoglu,
Akin Tatoglu
University of Hartford, West Hartford, CT
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Eoin King,
Eoin King
University of Hartford, West Hartford, CT
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Jarrett Lagler
Jarrett Lagler
University of Hartford, West Hartford, CT
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Akin Tatoglu
University of Hartford, West Hartford, CT
Eoin King
University of Hartford, West Hartford, CT
Jarrett Lagler
University of Hartford, West Hartford, CT
Paper No:
IMECE2018-88492, V04AT06A061; 6 pages
Published Online:
January 15, 2019
Citation
Tatoglu, A, King, E, & Lagler, J. "On Self-Driving Car Safety: Occupancy Map Modification With Rapid Emergency Vehicle Detection." Proceedings of the ASME 2018 International Mechanical Engineering Congress and Exposition. Volume 4A: Dynamics, Vibration, and Control. Pittsburgh, Pennsylvania, USA. November 9–15, 2018. V04AT06A061. ASME. https://doi.org/10.1115/IMECE2018-88492
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