Vehicle safety distance alarming system

Attia, Hussain; Ismail, Shereen; Ali, Halah Y.

doi:10.1109/icedsa.2016.7818501

Cited by 7 publications

(2 citation statements)

References 8 publications

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“…But in other scenarios, to determine the degree of the importance of DENM/DENM+, the IVD (m) and the speed range, V (km/h), are two main factors for fuzzy inputs that are investigated in follows. The classified ranges for IVD and V are based on Table 1, 32 and as shown in Figure 5, in a single direction scenario, the IVD is classified into four ranges (i.e., IVD ≤ 24.4 (M), IVD ≤ 48.8 (M), IVD ≤ 73.3 (M), and IVD > 73.3), and the vehicle speed is separated into four ranges (i.e., V ≤ 40 (km/h), V ≤ 80 (km/h), V ≤ 120 (km/h), and V > 120 (km/h)) accordingly. If the speed of the host vehicle is between 0 and 40 km/h and the spatial distance between the host and the next vehicle is more than 24.4 m, FIS 2 determines the degree of the importance of received DENMs/DENMs+.…”

Section: Fdcc Solution For Single-directionmentioning

confidence: 99%

“…As shown in Figures 9-11, , the set of linguistic values is {LV, MV, HV} representing Low Velocity, Medium Velocity, and Heigh Velocity. Measured intervals for the speed membership function of each fuzzy inference are based on Table 1, 32 and these classified speed ranges are utilized in simulation. According to the obtained speed, FDCC selects safe, none safe, and beyond-safe distances, and then, it decides which of these received messages is unimportant.…”

Section: Fdcc Solution For Single-directionmentioning

confidence: 99%

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A traffic‐centric fuzzy approach for solving the starvation problem of cooperative awareness messages in vehicular ad hoc networks

Abdollah

Zarei

2021

Int J Communication

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Congestion problem in vehicular ad hoc networks (VANETs) is one of the main factors that negatively affect the inter-vehicle communication (IVC) efficiency of such emerging networks. Decentralized congestion control (DCC) is an effective method for overcoming the congestion problem. However, lower priority packets such as cooperative awareness messages (CAMs) may be encountered with the starvation problem in the DCC approach. This paper proposes FDCC: a traffic-centric Fuzzy DCC approach for improving CAMs delivery ratio, especially in congested traffic flows. FDDC architecture consists of four fuzzy inference systems (FISs). The inter-vehicle distance (IVD) and the speed variation are the spatiotemporal fuzzy inputs, which are customized for both single direction and multi-direction scenarios. Based on the FIS outputs, the FDCC control system, unlike the DCC, provides a way for processing starved CAMs by ignoring unnecessary even-driven messages. The obtained simulation results through OMNET++ in both single and multi-direction scenarios show the efficiency of FDCC in terms of the end-to-end delay, network throughput, and packet delivery ratio for event-driven messages as well as essential CAMs.

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Section: Fdcc Solution For Single-directionmentioning

confidence: 99%

Section: Fdcc Solution For Single-directionmentioning

confidence: 99%