abstract: At modern-day intersections, traffic lights and stop signs assist human drivers to cross the intersection safely. Traffic congestion in urban road networks is a costly problem that affects all major cities. Efficiently operating intersections is largely dependent on accuracy and precision of human drivers, engendering a lingering uncertainty of attaining safety and high throughput. To improve the efficiency of the existing traffic network and mitigate the effects of human error in the intersection, many studies have proposed autonomous, intelligent transportation systems. These studies often involve utilizing connected autonomous vehicles, implementing a supervisory system, or both. Implementing a supervisory system is relatively more popular due to the security concerns of vehicle-to-vehicle communication. Even though supervisory systems are a step in the right direction for security, many supervisory systems’ safe operation solely relies on the promise of connected data being correct, making system reliability difficult to achieve. To increase fault-tolerance and decrease the effects of position uncertainty, this thesis proposes the Reliable and Robust Intersection Manager, a supervisory system that uses a separate surveillance system to dependably detect vehicles present in the intersection in order to create data redundancy for more accurate scheduling of connected autonomous vehicles. Adding the Surveillance System ensures that the temporal safety buffers between arrival times of connected autonomous vehicles are maintained. This guarantees that connected autonomous vehicles can traverse the intersection safely in the event of large vehicle controller error, a single rogue car entering the intersection, or a sybil attack. To test the proposed system given these fault-models, MATLAB® was used to create simulations in order to observe the functionality of R2IM compared to the state-of-the-art supervisory system, Robust Intersection Manager. Though R2IM is less efficient than the Robust Intersection Manager, it considers more fault models. The Robust Intersection Manager failed to maintain safety in the event of large vehicle controller errors and rogue cars, however R2IM resulted in zero collisions. / Dissertation/Thesis / Masters Thesis Computer Engineering 2019
| Identifer | oai:union.ndltd.org:asu.edu/item:55473 |
| Date | January 2019 |
| Contributors | Dedinsky, Rachel (Author), Shrivastava, Aviral (Advisor), Sen, Arunabha (Committee member), Syrotiuk, Violet (Committee member), Arizona State University (Publisher) |
| Source Sets | Arizona State University |
| Language | English |
| Detected Language | English |
| Type | Masters Thesis |
| Format | 61 pages |
| Rights | http://rightsstatements.org/vocab/InC/1.0/ |
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