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Evaluation of V2V Reception Cadence- A New Metric for System Level Performance Analysis
ISSN: 0148-7191, e-ISSN: 2688-3627
Published January 16, 2019 by SAE International in United States
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Vehicle to Everything (V2X) communication is a prominent solution for active safety collision avoidance and for providing autonomous vehicles Non-Line of Sight (NLOS) capabilities. For safety purposes, it is essential the V2X technology would support communication between all road users, e.g., Vehicles (V2V), pedestrians (V2P) and road infrastructure (V2I). Hence, the efficiency of a V2V communication solution should be evaluated through system level performance. In addition, the examined performance metrics need to reflect safety related properties. Metrics as Packet Reception Ratio (PRR) and transmission latencies, which are commonly used to assess V2X system’s functionality, aren’t enough since reception latencies are overlooked. The latter is crucial in ensuring messages would reach their destination on time to avoid hazardous incidents. The reception cadence may be much lower than this of the transmission due to various phenomenon (e.g. channel congestion). Still, whether this pose a safety’s threat depends on mechanical aspects such as the distance between vehicles or their relative speed. In this paper, we present a metric called CD-PRR (Cadence Dependent PRR) - a generalization of the PRR metric which considers the reception cadence the system should support per range. Thus, providing better understanding of the system’s reception latencies. Metric’s performance is presented for several scenarios using Dedicated Short-Range communication (DSRC) technology and based on the results some conclusions are drawn. CD-PRR is an example of automotive related metrics. We propose that based on the automotive needs a specific metrics set would be designed and applied for the V2X safety messages.
CitationAharon, A. and Kotzer, I., "Evaluation of V2V Reception Cadence- A New Metric for System Level Performance Analysis," SAE Technical Paper 2019-01-0102, 2019, https://doi.org/10.4271/2019-01-0102.
Data Sets - Support Documents
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