TY - GEN
T1 - A Scheme on Pedestrian Detection using Multi-Sensor Data Fusion for Smart Roads
AU - Wang, Hui
AU - Li, Changle
AU - Zhang, Yao
AU - Liu, Zhao
AU - Hui, Yilong
AU - Mao, Guoqiang
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/5
Y1 - 2020/5
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85088311108
U2 - 10.1109/VTC2020-Spring48590.2020.9128855
DO - 10.1109/VTC2020-Spring48590.2020.9128855
M3 - 会议稿件
AN - SCOPUS:85088311108
T3 - IEEE Vehicular Technology Conference
BT - 2020 IEEE 91st Vehicular Technology Conference, VTC Spring 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 91st IEEE Vehicular Technology Conference, VTC Spring 2020
Y2 - 25 May 2020 through 28 May 2020
ER -