Date of Award
10-11-2024
Publication Type
Thesis
Degree Name
M.A.Sc.
Department
Electrical and Computer Engineering
Supervisor
Ahmed Sakr
Supervisor
Ning Zhang
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Abstract
Connected autonomous vehicles (CAVs) have been a hot topic of research for a while now. Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) can help improve the safety of the road network environment as well as traffic flow and efficiency of the infrastructure. This research investigates the concept of communication in CAVs and the current standard in place. CAVs communicate through collective perception messages (CPMs) to transmit and receive information with other vehicles. This transmission and reception consist of information about their onboard sensor detections (perceived objects) to help each connected entity gain better awareness of its surroundings. I studied the concept of CPMs and redundancy mitigation rules currently in place to develop foundation knowledge of the concept. This research then proposes a set of hybrid redundancy mitigation rules (RMRs) which are designed to enhance the efficiency of collective perception services (CPS) in CAV networks. The proposed mechanisms integrate the ETSI inclusion rules for CPMs with distancebased criteria to omit redundant locally perceived objects. This thesis then evaluates the performance of several strategies: no RMR, self-announcement-based (SAB) RMR, the proposed distance-based SAB (DSAB) RMR, frequency-based (FB) RMR, and the proposed distance-based FB (DFB) RMR, across various market penetration rates. Key metrics analyzed include redundancy level (RL), channel busy ratio (CBR), and environment awareness ratio (EAR). The findings indicate that hybrid algorithms consistently outperform other methods in terms of balancing redundancy, channel load, and environmental awareness, particularly at higher market penetration rates.
Recommended Citation
Malik, Arjit, "Hybrid Redundancy Mitigation Rules for Collective Perception Services" (2024). Electronic Theses and Dissertations. 9421.
https://scholar.uwindsor.ca/etd/9421