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Communication Network Architectures for Driver Assistance Systems

Department of Engineering Science, Guglielmo Marconi University, Via Plinio 44, 00198 Rome, Italy
Department of Enterprise Engineering “Mario Lucertini”, University of Rome Tor Vergata, Via del Politecnico 1, 00133 Rome, Italy
Department of Computer Engineering, City University of New York, 160 Convent Avenue, New York, NY 10031, USA
Author to whom correspondence should be addressed.
Academic Editor: Francisco J. Martinez
Sensors 2021, 21(20), 6867;
Received: 3 September 2021 / Revised: 10 October 2021 / Accepted: 14 October 2021 / Published: 16 October 2021
Autonomous Driver Assistance Systems (ADAS) are of increasing importance to warn vehicle drivers of potential dangerous situations. In this paper, we propose one system to warn drivers of the presence of pedestrians crossing the road. The considered ADAS adopts a CNN-based pedestrian detector (PD) using the images captured from a local camera and to generate alarms. Warning messages are then forwarded to vehicle drivers approaching the crossroad by means of a communication infrastructure using public radio networks and/or local area wireless technologies. Three possible communication architectures for ADAS are presented and analyzed in this paper. One format for the alert message is also presented. Performance of the PDs are analyzed in terms of accuracy, precision, and recall. Results show that the accuracy of the PD varies from 70% to 100% depending on the resolution of the videos. The effectiveness of each of the considered communication solutions for ADAS is evaluated in terms of the time required to forward the alert message to drivers. The overall latency including the PD processing and the alert communication time is then used to define the vehicle braking curve, which is required to avoid collision with the pedestrian at the crossroad. View Full-Text
Keywords: pedestrian detection; communication system; system information block; convolutional neural networks pedestrian detection; communication system; system information block; convolutional neural networks
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MDPI and ACS Style

Giuliano, R.; Mazzenga, F.; Innocenti, E.; Fallucchi, F.; Habib, I. Communication Network Architectures for Driver Assistance Systems. Sensors 2021, 21, 6867.

AMA Style

Giuliano R, Mazzenga F, Innocenti E, Fallucchi F, Habib I. Communication Network Architectures for Driver Assistance Systems. Sensors. 2021; 21(20):6867.

Chicago/Turabian Style

Giuliano, Romeo, Franco Mazzenga, Eros Innocenti, Francesca Fallucchi, and Ibrahim Habib. 2021. "Communication Network Architectures for Driver Assistance Systems" Sensors 21, no. 20: 6867.

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