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Safe and Robust Cooperative Algorithm for Connected Autonomous Vehicles.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Safe and Robust Cooperative Algorithm for Connected Autonomous Vehicles./
作者:
Khayatian, Mohammad.
面頁冊數:
1 online resource (181 pages)
附註:
Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
Contained By:
Dissertations Abstracts International83-02B.
標題:
Robotics. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28643722click for full text (PQDT)
ISBN:
9798535547749
Safe and Robust Cooperative Algorithm for Connected Autonomous Vehicles.
Khayatian, Mohammad.
Safe and Robust Cooperative Algorithm for Connected Autonomous Vehicles.
- 1 online resource (181 pages)
Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
Thesis (Ph.D.)--Arizona State University, 2021.
Includes bibliographical references
Autonomous Vehicles (AVs) have the potential to significantly evolve transportation. AVs are expected to make transportation safer by avoiding accidents that happen due to human errors. When AVs become connected, they can exchange information with the infrastructure or other Connected Autonomous Vehicles (CAVs) to efficiently plan their future motion and therefore, increase the road throughput and reduce energy consumption. Cooperative algorithms for CAVs will not be deployed in real life unless they are proved to be safe, robust, and resilient to different failure models. Since intersections are crucial areas where most accidents happen, this dissertation first focuses on making existing intersection management algorithms safe and resilient against network and computation time, bounded model mismatches and external disturbances, and the existence of a rogue vehicle. Then, a generic algorithm for conflict resolution and cooperation of CAVs is proposed that ensures the safety of vehicles even when other vehicles suddenly change their plan. The proposed approach can also detect deadlock situations among CAVs and resolve them through a negotiation process. A testbed consisting of 1/10th scale model CAVs is built to evaluate the proposed algorithms. In addition, a simulator is developed to perform tests at a large scale. Results from the conducted experiments indicate the robustness and resilience of proposed approaches.
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023
Mode of access: World Wide Web
ISBN: 9798535547749Subjects--Topical Terms:
519753
Robotics.
Subjects--Index Terms:
Autonomous vehiclesIndex Terms--Genre/Form:
542853
Electronic books.
Safe and Robust Cooperative Algorithm for Connected Autonomous Vehicles.
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Safe and Robust Cooperative Algorithm for Connected Autonomous Vehicles.
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Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
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Autonomous Vehicles (AVs) have the potential to significantly evolve transportation. AVs are expected to make transportation safer by avoiding accidents that happen due to human errors. When AVs become connected, they can exchange information with the infrastructure or other Connected Autonomous Vehicles (CAVs) to efficiently plan their future motion and therefore, increase the road throughput and reduce energy consumption. Cooperative algorithms for CAVs will not be deployed in real life unless they are proved to be safe, robust, and resilient to different failure models. Since intersections are crucial areas where most accidents happen, this dissertation first focuses on making existing intersection management algorithms safe and resilient against network and computation time, bounded model mismatches and external disturbances, and the existence of a rogue vehicle. Then, a generic algorithm for conflict resolution and cooperation of CAVs is proposed that ensures the safety of vehicles even when other vehicles suddenly change their plan. The proposed approach can also detect deadlock situations among CAVs and resolve them through a negotiation process. A testbed consisting of 1/10th scale model CAVs is built to evaluate the proposed algorithms. In addition, a simulator is developed to perform tests at a large scale. Results from the conducted experiments indicate the robustness and resilience of proposed approaches.
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