A Scheme on Pedestrian Detection using Multi-Sensor Data Fusion for Smart Roads

Hui Wang, Changle Li, Yao Zhang, Zhao Liu, Yilong Hui, Guoqiang Mao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Transforming our roads into smart roads is an indispensable step towards future self-driving systems, and therefore has drawn increasing attention from both academia and industry. To this end, this paper develops a novel cost-effective IoT-based target detection system utilizing the multi-sensor data fusion technology with a particular focus on pedestrian detection, as an important component of smart road system. Particularly, the developed intelligent pedestrian detection module ({i}PDM) consists of three major sensors, i.e., Doppler microwave radar sensor, passive infrared (PIR), and geomagnetic sensor. A multi-sensor data fusion algorithm is developed to fuse the sensor data and achieves reliable target detection. After that, {i}PDM sends the relevant warning signal wirelessly to nearby base station and vehicles. Experiments are conducted on real traffic environment to evaluate the performance of {i}PDM. The results validate the high reliability of {i}PDM with an average 91.7% detection accuracy. Moreover, to our best knowledge, {i}PDM is the first IoT-based implementation for pedestrian detection of smart roads. It is necessary to highlight that {i}PDM is a low-cost, low-power, wide-coverage pedestrian detection system where the cost of a single {i}PDM is only US 30, which makes it suitable to large-scale deployment.

Original languageEnglish
Title of host publication2020 IEEE 91st Vehicular Technology Conference, VTC Spring 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728152073
DOIs
StatePublished - May 2020
Externally publishedYes
Event91st IEEE Vehicular Technology Conference, VTC Spring 2020 - Antwerp, Belgium
Duration: 25 May 202028 May 2020

Publication series

NameIEEE Vehicular Technology Conference
Volume2020-May
ISSN (Print)1550-2252

Conference

Conference91st IEEE Vehicular Technology Conference, VTC Spring 2020
Country/TerritoryBelgium
CityAntwerp
Period25/05/2028/05/20

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