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

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