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.


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    Title :

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


    Contributors:
    Wang, Hui (author) / Li, Changle (author) / Zhang, Yao (author) / Liu, Zhao (author) / Hui, Yilong (author) / Mao, Guoqiang (author)


    Publication date :

    2020-05-01


    Size :

    711508 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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